["itemContainer",{"xmlns:xsi":"http://www.w3.org/2001/XMLSchema-instance","xsi:schemaLocation":"http://omeka.org/schemas/omeka-xml/v5 http://omeka.org/schemas/omeka-xml/v5/omeka-xml-5-0.xsd","uri":"https://johnntowse.com/LUSTRE/items/browse?collection=5&output=omeka-json","accessDate":"2026-05-22T22:18:31+00:00"},["miscellaneousContainer",["pagination",["pageNumber","1"],["perPage","10"],["totalResults","45"]]],["item",{"itemId":"203","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"234"},["src","https://johnntowse.com/LUSTRE/files/original/012b92077ab1d153d79327092c115315.pdf"],["authentication","992fc24af6f8d556815cd8fc13f48ca7"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"4047"},["text","Student Experiences of Mental Health Issues in Further and Higher Education. "]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"4048"},["text","Rachel Jordan "]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4049"},["text","17/09/2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4050"},["text","Previous research has shown that students are at risk of experiencing mental health difficulties, specifically relating to anxiety, depression, and stress (Andrews, Hejdenberg, & Wilding, 2006; Holland, 2016; Landow, 2006; Lattie, Lipson, & Eisenberg, 2019; Nascante, 2001; Shankar & Park, 2016). This study aimed to understand whether level of education, provisions to aid mental wellbeing within educational establishments, and students’ resilience were related to their mental wellbeing. A total of 94 participants were recruited for this study, however only 47 sets of data were complete enough to be used for the analyses. An online questionnaire using a series of demographic questions and subscales was used to collect data. No significant relationships were found between students’ mental wellbeing and their level of education or the provisions accessible to them in their place of education. However, a significant, negative correlation was found between students’ overall mental health and their resilience scores. Additional analyses were completed to better understand this and the same relationship was found between resilience and anxiety, depression, and stress. It was concluded that due to issues with power, more research with a larger sample is required to investigate these relationships further. It was also concluded that more understanding of resilience and mental health in students is required to be able to create better provisions."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4051"},["text","Mental health, stress, achievement anxiety, depression, students, education, provisions, resilience."]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"4052"},["text","Participants\r\n94 participants (25 males, 43 females) were used for this study, they were a minimum of 18 to over 51 years old and were from educational establishments around the UK. Participants were recruited using the SONA participant recruitment system through Lancaster University and also by advertising on Facebook and Instagram. All participants were treated in accordance with BPS ethical guidelines and Lancaster University Department of Psychology provided ethical approval for the study (Appendix A). Only data from 47 participants was used due to incomplete datasets.\r\n\r\nDesign\r\nThis cross-sectional study used volunteers from the student population as participants as one sample group. This was a questionnaire-based study with four sub-scales using correlational analyses. The factors being analysed are detailed in the procedural section to follow.\r\nProcedure\r\nAdverts were placed on Facebook, and Instagram to help recruit participants to the study. Potential participants were provided with a link to the online survey, administered through Qualtrics. They were then provided with a participant information sheet (Appendix B) and gave informed consent (Appendix C) to participate on that basis. Informed consent was gained by participants selecting all six consent statements on the questionnaire. Following this, participants were presented with demographic questions and four measures (Appendix D). Following this, participants were presented with a debrief sheet (Appendix E) before being asked to close the tab. \r\nMaterials\r\nDemographic Questions\r\nThe questionnaire started with two demographic questions. These were:\r\n“How old are you in years?”  with the options of “18-21/22-25/26-29/30-35/36-40/41-45/46-50/51+/Prefer not to answer” and “What was your assigned sex at birth?” with the options of “Male/Female/Prefer not to answer”.\r\nThese items were included in this questionnaire to better understand the sample of participants included in the study.\r\n\r\nLevel of Education\r\nParticipants’ level of education was measured using one multiple choice item.  This item was:\r\n“What level of education are you in?”\r\nThe options for this multiple-choice item are “A Levels/ Apprenticeship/ Undergraduate Degree/ Postgraduate Degree/ PhD/ Other (please specify)/ Prefer not to answer”. This item was included to help investigate whether the level of participant’s education is related to their mental wellbeing.\r\nMental Wellbeing\r\nExisting mental wellbeing was measured using two items which both used multiple choice options. These questions were:\r\n“Have you ever been diagnosed with a mental health condition?” This item had options of “Yes/ No/ Prefer not to answer”. \r\n“Please select if any of these diagnostic categories apply to your diagnoses.” This item was only included if the participant answered the previous question with “Yes”. The options for answering this item were “Anxiety Disorder/ Depression/ Eating Disorder/ Stress/ Psychosis/ Personality Disorder/ Other (please specify)/ Prefer not to answer.”\r\nMeasures of Support\r\nTwo questions were used in this questionnaire to decipher how supported students felt by their educational establishments. These questions were:\r\n“How much support do you feel is available for your mental health at your place of education?” This question used a Likert scale ranging from one (lots) to four (I don’t know) and including an option of ‘prefer not to answer’. \r\nThis was followed by the open, qualitative question of “Please tell us about any mental wellbeing support you know is available in your place of education.” This question had an open response box, allowing participants to communicate their understanding of support available for their mental wellbeing in their educational institutions.\r\nPerceived Stress Scale (PSS-10)\r\nThe Perceived Stress Scale (Andreou, et al., 2011; Cohen, Kamarck, & Mermelstein, 1983; Cohen, Kamarck, & Mermelstein, 1994; Reis, Hino, & Añez, 2010; Roberti, Harrington, & Storch, 2006) (Appendix F) using a five-point Likert scale ranging from 1 (never) to five (very often). This scale consisting of ten items was used to measure how stressed participants believed they were for this questionnaire. One example of the items used in this scale is:\r\n“In the last month, how often have you felt that you were on top of things?”\r\nThe ten-item version of this measure was used in this study because research (Roberti, Harrington, & Storch, 2006) generally commented that the ten-item scale was a reliable and valid measure of perceived stress when compared to the original, longer Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983). Therefore, the PSS-10 was chosen for this questionnaire to reduce time demands on participants without compromising the reliability and validity of the measure. \r\n\r\nAdult Resilience Measure Revised (ARM-R)\r\nThe Adult Resilience Measure Revised (Resilience Research Centre, 2018; Jefferies, McGarrigle & Ungar, 2018) (Appendix G) was used within the questionnaire to assess participants’ resilience skills. This sixteen-item measure was a five-point Likert scale ranging from one (not at all) to five (a lot) with an option of ‘prefer not to answer’ and consisting of seventeen items to measure resilience. An example of an item on this scale is:\r\n“My friends stand by me during difficult times.”\r\nCentre for Epidemiological Studies Depression Scale Revised (CESD-R)\r\nThe Centre for Epidemiological Studies Depression Scale Revised (Eaton, et al., 2004; Van Dam, & Earleywine, 2011) (Appendix I) was used as a measure in this study to assess how depressed the participants felt. This measure used a four-point Likert scale with the first being rarely or none of the time to most or all of the time and an option of ‘prefer not to answer’ include. There were 20 items included in this scale in order to measure this factor, one such example of this is:\r\n“I felt everything I did was an effort.”\r\nAchievement Anxiety Test (AAT)\r\nThe Achievement Anxiety Test (Alpert & Haber, 1960) (Appendix I) was used in this questionnaire to measure how anxious participants were about their ability to achieve. This measure used a five-point Likert scale with one meaning always and five meaning never, this scale consisted of nineteen items. An option of prefer not to answer was also provided. One example of an item on the scale is:\r\n“I work most effectively under pressure, as when a task is very important.”\r\nAll measures in this questionnaire had an additional option of ‘prefer not to answer’ added to them for the purpose of this study to allow for forced choices to be selected for the questionnaire answers without removing the participants’ right to withdraw or withhold information.\r\nEthics\r\nThis study was conducted after ethical approval was received from the ethics committee of the Lancaster University psychology department on 12th June 2023.\r\nOne ethical issue that could come up in this study is that participants could believe that there is some diagnostic weight to the questionnaire. \r\nAnalyses\r\nDescriptive statistics were taken for all variables and demographic data, specifically in regard to their mean and standard deviation.\r\nFollowing this correlational analyses were then completed to determine whether there were relationships between mental wellbeing scores taken as a combination of the AAT, PSS-10 and the CESD-R subscales included in the questionnaires, resilience, preexisting mental health, provisions being accessed, and educational level.\r\nIf significant relationships are identified through the correlational analyses, regressions will be conducted to further investigate these relationships to identify whether they were causational."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"4053"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4054"},["text","Data/excel.csv"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"4055"},["text","Jordan2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"4056"},["text","Megan Grace Liddell"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"4057"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"4058"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4059"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4060"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"4061"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"4062"},["text","Dr. Chris Walton"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"4063"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"4064"},["text","Clinical "]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"4065"},["text","94"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"4066"},["text","ANOVA, Correlation"]]]]]]]],["item",{"itemId":"202","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"229"},["src","https://johnntowse.com/LUSTRE/files/original/2fc9eb768f4d00d92b5e73627b2912cf.docx"],["authentication","2c3e89d0f82f7c2b4dd77fac20aa220e"]],["file",{"fileId":"230"},["src","https://johnntowse.com/LUSTRE/files/original/199d736584372c0beff6cff855b5aae8.xlsx"],["authentication","0fba9d41dead25b6239c2151286388d8"]],["file",{"fileId":"231"},["src","https://johnntowse.com/LUSTRE/files/original/aa42a4e75948741e54f7972ce17998eb.xlsx"],["authentication","75c7c9e87ba5477883053a77a5350982"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["itemType",{"itemTypeId":"14"},["name","Dataset"],["description","Data encoded in a defined structure. Examples include lists, tables, and databases. A dataset may be useful for direct machine processing."]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"4028"},["text","Is selfie-related behaviour motivated by sexual orientation and gender conformity"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"4029"},["text","Wen Li"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4030"},["text","2022-2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4031"},["text","In the digital age, selfie culture has become an integral part of social media platforms. This globally widespread phenomenon created a distinctive form of self-expression, allowing selfie- makers to convey their identities, shape online personas, and build connections with others. Selfies are more than photos presented but also refer to a series of backstage to finally lead to creation and sharing. As research into selfies, gender differences in selfie-related behaviours have enabled further comprehension of selfies in terms of self-expression. Social Role Theory (SRT) explained the existence of gender differences in selfie culture that gender role norms and social expectations shape individuals' identity and behaviours. This study explored the concept of gender conformity among heterosexuals and non-heterosexuals and the impact on selfie-related behaviours. A total of 120 participants, categorized into heterosexual men, heterosexual women, non-heterosexual men, and non-heterosexual women, engaged in an online questionnaire, and contributed a total of 150 selfies. Data analysis involved one-way variance (ANOVA) to test the differences between the four groups, and multiple regression analysis to assess the influence of gender, the Traditional Masculinity-Femininity (TMF) scale and sexual attraction to me score. The results revealed no differences across the four groups in terms of the nine domains of selfie motives, as well as preoccupation. However, retention of moments and entertainment as the most prominent motives for selfies. For selfie behaviours, time spent on taking, editing, and selecting selfies, as well as taking amount and edit frequency differed significantly among the four groups. Specifically, both heterosexual women and non-heterosexual women tended to allocate more time on taking, editing, and selecting selfies for posting. Meanwhile, heterosexual women and non-heterosexual men displayed a higher trend for taking a greater number of selfies and editing selfies more frequently. These findings support the current studies indicating that women engage in selfie-related behaviours more actively than men, but more deeply that sexual orientation, especially the sexual attraction to men, also encourage some of selfie-related behaviours. While the results provide evidence for SRT as gender roles shape behaviours through socialisation, but also draw criticisms as TMF scale failed to predict the impacts on selfie-related behaviours and sexual orientation can break the traditional gender role expectations. Future research should keep exploring these relationships, offering deeper insights into gender conformity and gender non- conformity in the realm of self-presentation across diverse identity roles, thereby contributing to a more inclusive and diverse self-image narrative."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4032"},["text","Selfie, Self-expression, Gender conformity, Sexual orientation"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"4033"},["text","Participants\r\nThis study recruited 120 normal adults as participants through a Qualtrics online questionnaire voluntarily and anonymously, of which 67 completed the questionnaire, including 22 men, 42 women and 3 self-identified as non-binary gender. However, since only binary gender was considered for the analysis of gender conformity, the three non-binary gender responses were removed. Additionally, in terms of sexual orientation, there were 48 heterosexuals, 6 homosexuals, 8 bisexuals and 2 others. The participants were divided into four sample groups, that is, 19 heterosexual men, 29 heterosexual women, three non-heterosexual men and 13 non-heterosexual women.\r\nMaterials\r\nSexual orientation and gender conformity were the two domains of prediction, and selfie- related behaviours were regarded as the outcomes. Sexual orientation was measured by self-rated sexual attraction to men and women, while gender conformity was measured by self-ascribed the Traditional masculinity/femininity (TMF) scale. As for selfie-related behaviour, it involves several aspects of selfie taking, editing, and posting. In addition, selfie motives and attributes of uploaded selfies were also be taken into account.\r\nSexual attraction score\r\nSexual orientation was self-identified by participants themselves as heterosexual, homosexual, bisexual, and others. The participants were asked to self-rated two statements about their sexual attraction to men and women. The latter, with some adjustment to Lippa's (2002) methodology, included two questions: \"I am sexually attracted to men\" and \"I am sexually attracted to women\". In both cases, participants were asked to self-rated on a scale of 0-10, where a higher score indicated greater sexual attraction to men or women. This measure transformed sexual orientation from a categorical variable to a continuous variable for further analyses of differences and relationships. In particular, the separate assessment of sexual attraction to men and sexual attraction to women could help to better detect whether sexual attraction to men is more influential in selfie-related behaviours.\r\nTraditional Masculinity-Femininity scale\r\nGender conformity was measured as a continuous variable by using the Traditional Masculinity-Femininity (TMF) scale from Kachel et al. (2016). This scale comprises of six questions which are self-rated on a scale of 1-7, with 1 represented very feminine and 7 represented very masculine. Example items include \"I consider myself as...\", \"Ideally, I would like to be...\" regarded their preferred gender role. The remaining four questions concerned identified gender roles in terms of interests, attitudes and beliefs, behaviours, and appearance from a traditional perspective, being asked respectively as \"Traditionally, my... would be considered as...\". Then, the mean score of these six items would eventually be used as the individual’s masculinity/femininity score.\r\nKachel et al.(2016) pointed out that the TMF scale had been proven to be a reliable one- dimensional construct tool to assess masculinity because it correlates well with another gender- related instrument, the Bem Sex Role Inventory (BSRI) and successfully distinguishes between groups, such as females vs. males, lesbians/gays vs. heterosexual females and males,that are expected to be different (See Figure 2).\r\nFigure 2\r\nMediation of the relation between BSRI and sexual orientation by the TMF (Kachel et al., 2016). Mean TMF scores separately for gender and sexual orientation (Kachel et al., 2016).\r\n   \r\nSelfie Coding\r\nParticipants were asked to upload 1-3 selfies that they would post on social media. All selfies were coded based on four aspects. First, participants were asked to whether their uploaded selfies had been edited or retouched. Then, the experimenter coded the number of people in the selfie (alone or in a group), the angle of the selfie (upward, horizontal, or downward), and the amount of body shown (face only, upper body, body without face, or whole body with face).\r\nMeanwhile, a total of 150 portrait pictures were collected from the participants, although 17 of them looked like taken by others rather than selfies.\r\nSelfie-related Behaviours\r\nSelfie-related behaviours and motivations measures were taken from Bij de Vaate et al. (2018). For motivations, 33 items were used to reflect nine domains of motives, and each item was an agreement extent scale (1 = totally disagree to 5 = totally agree). The nine domains of motives included \"Retention of moments\" (e.g., \"I make selfies to memorise a moment\"), \"Entertainment\" (e.g., \"Making selfies is enjoyable\"), \"Expressive information sharing\" (e.g., \"I tell others something about myself by using selfies\"), \"Social interaction\" (e.g., \"I make selfies to keep in touch with friends and family\"), \"Social use\" (e.g., \"I make selfies to show who I am and what I do\"), \"Habitual passing of time\" (e.g., \"Making selfies is a habit\"), \"Relaxation\" (e.g., \"Making selfies enables me to relax\"), \"Imaginary audience\" (e.g., \"I post selfies with a specific audience in mind\"), and \"Social pressure and identity\" (e.g., \"I make selfies because everybody does it\"). Preoccupation (e.g., \"I often share selfies\") implied the degree to take part in selfie behaviours, was measured with six items by an agreement extent scale (1 = totally disagree to 5 = totally agree). See Appendix A for specific questions of items in the questionnaire.\r\nSelfie-taking behaviour was measured by taking frequency, time spent and amount in the last three months, each of them was designed as an ordinal variable depending on an increasing degree. For instance, frequency referred to how often taking selfies, time spent referred to how long it taken within a selfie session, whereas amount referred to how many photos taken within a selfie session. Selfie-editing behaviour only accounted for two aspects, editing frequency and time spent, and selfie-posting behaviour used to select spend time instead of posting spend time.\r\nAdditionally, four items were designed to collect feedback on related concerns and feelings about selfies with a Likert scale that ranged from 0 (totally unconcerned) to 10 (totally concerned), including the attractiveness of their online image, the attention and comments of others on their selfies post on the social platform, the comparison with other people's selfies. Finally, 3 questions were designed to reflect participants’ satisfaction degree on their appearance in real life, before retouching and after retouching by the same 11-point Likert scale (0 = extremely uncomfortable to 10 extremely comfortable).\r\nProcedure\r\nThis study had been reviewed and approved by a member of the Psychology department from the Lancaster University Board of Ethics. At the beginning of the survey, participants were provided with a participant information sheet, informing them that the study is about selfie- related behaviours in terms of sexual orientation and gender conformity. Anonymity and confidentiality were ensured because of sensitive information such as selfies and sexual orientation. After the confirmation of the consent, all participants complete the same questionnaire which is conducted on Qualtrics (www.qualtrics.com).\r\nThe survey questionnaire (See Appendix A) was designed to collect information through six key sections. In the first section, some demographic information was asked, such as age,\r\nrelationship status, gender identity, sexual orientation, and sexual attraction. Then, it was a six- item self-ascribed Traditional Masculinity/Femininity (TMF) scale. For the third section, participants were expected to upload three different selfies that would be posted on social media and state whether these selfies have been retouched. This section was optional and if a selfie were uploaded, a specific selfie consent would be required to confirm. The last three sections involved a series of questions on selfie-motives and preoccupation, selfie-related behaviours and feelings. At the end of the survey, participants were given a debrief sheet upon completion and were allowed the chance to ask any questions after the survey was undertaken. Meanwhile, information consent would be confirmed to get the final approval about all responses before submitting the questionnaire.\r\nAnalysis \r\nPre-Tests\r\nFirstly, two pre-tests were conducted by one-way analyses of variance (ANOVA) to analyse differences in Traditional Masculinity/Femininity (TMF) score and sexual attraction to men score for each of the four sample groups, with corresponding post-hoc multiple comparison tests, to examine expected differences in TMF scale and sexual attraction to men score by the four sample groups.\r\nMain Tests\r\nSubsequently, selfie motives in nine domains, and preoccupation were examined for each of their differences across the four sample groups by ANOVAs with corresponding post-hoc multiple comparison tests.\r\nIn the context of selfie behaviours, we tested three relevant stages: selfie-taking, editing and posting. For each of these behaviours, we conducted ANOVAs with corresponding post-hoc\r\nmultiple comparison tests to assess differences across the four sample groups, and Multiple Linear Regression analyses to investigate the influences by self-identified gender, TMF score\r\nand sexual attraction to men score. In particular, we delved into three aspects of taking selfies, involving frequency, time spent, and amount. Similarly, we analysed two aspects of selfie editing, frequency and time spent, as well as three aspects of selfie posting , which included frequency, time spent on selection, and amount.\r\nIn addition, in order to examine whether selfie content itself was affected by gender conformity and sexual orientation, a total of 150 uploaded selfies were coded according to four attributes: editing usage, selfie format, the shown part in the selfie, and the taking angle. Each of the attributes was firstly tested by a chi-square test to examine the association between attribute and the four sample groups because both were categorical variables. Furthermore, we conducted two separate ANOVAs, each followed by post-hoc multiple comparison tests. One used the TMF scores and the other used sexual attraction to men scores, with each of the four attribute of these selfies as independent variables to test for differences. In these analyses, the TMF scores and sexual attraction to men scores, as interval data, were regarded as dependent variables.\r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"4034"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4035"},["text",".xlsx"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"4036"},["text","Li2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"4037"},["text","Mshary Al Jaber"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"4038"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"4039"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4040"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"4041"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"4042"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"4043"},["text","Jaime Benjamin"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"4044"},["text","Developmental"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"4045"},["text","120"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"4046"},["text","ANOVA, Chi-sqaured, Regression"]]]]]]],["tagContainer",["tag",{"tagId":"5"},["name","gender coformity"]],["tag",{"tagId":"7"},["name","self -xpression"]],["tag",{"tagId":"8"},["name","Selfie"]],["tag",{"tagId":"6"},["name","sexual orientation"]]]],["item",{"itemId":"196","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"218"},["src","https://johnntowse.com/LUSTRE/files/original/f9177519dc5c68194a35cb5df1d2411d.doc"],["authentication","ddb02b65d50864142451fb8a56e51c8a"]],["file",{"fileId":"225"},["src","https://johnntowse.com/LUSTRE/files/original/1e48795e7a9817c81dec944c610bf3b2.doc"],["authentication","03e45136274151b42c745dcc2f9956e7"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3909"},["text","Hemispheric Lateralisation of Facial Emotion Processing: A Possible Explanation of Atypical Empathetic Responses in Children with Autism Spectrum Disorder"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3910"},["text","Lydia Brooks"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3911"},["text","07.09.2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3912"},["text","Existing research suggests that children with autism are endowed with a significant delay in the lateralisation of facial emotion processing (Taylor et al., 2012), and that this delay is associated with some of the social and emotional based deficits that manifest within the disorder. The present study therefore aimed to ascertain the reliability of Taylor et al.’s (2012) findings by determining whether the strength of lateralisation for facial emotion processing differs between children with and without autism, while also determining whether this difference can explain atypical empathetic responses in children with autism. To explore these aims, an online version of the chimeric face task was administered to 11 neurotypical children and 5 children with a diagnosis of autism. The Child Empathy Quotient was completed by parents of all children, and The Autism Quotient – Children’s Version was completed by parents of children with autism. Results indicated that there was no significant difference in the strength of hemispheric lateralisation for facial emotion processing between children with and without autism, and that the strength of lateralisation did not predict a child’s level of empathy, nor did a child’s autism severity. Instead, levels of empathy were best predicted by an individual’s diagnostic status and age. The present study was therefore unable to support the finding of Taylor et al. (2012) or explain empathy deficits in the autistic population. However, the limitations identified in this study help to inform future research on the relationship between the lateralisation of facial emotion processing and empathy."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3913"},["text","Hemispheric Lateralisation, Emotion Processing, Autism Spectrum Disorder, Empathy, Chimeric Face Task"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3914"},["text","Participants \r\nParticipants were recruited from mainstream primary schools, wrap around care settings and specialist educational provisions in the Lancashire area, as well as via social media. A total of 22 parents completed the required questionnaires on behalf of their children, out of which 17 parents arranged a date and time for their child to complete the chimeric face task. One child, who is non-verbal and has received a diagnosis of ASD had difficulty completing the task and selected responses impulsively without looking at, or taking sufficient time to consider, the facial stimuli and the emotion it depicted. For this reason, the chimeric face task was terminated prior to completion and the child’s data was not included in the analysis. \r\nThe final sample of participants consisted of 16 children aged between 5- and 10-years-old, of which 5 had received a formal diagnosis of ASD (5 boys; Mage = 6.8, SDage = 1.48). One child with ASD had a comorbid diagnosis of hypermobility and sensory processing disorder. All children with ASD were reported to speak English at home, one child was left-hand dominant, and four children were right-hand dominant. \r\nThe remaining participants were 11 typically developing children (6 girls, 5 boys;  Mage =  7.0, SDage = 1.90), who had not been diagnosed with any neurodevelopmental disorders. One of these children was reported to speak Russian at home, however, is fluent in English. All children in the typically developing group were right-hand dominant.\r\nDesign \r\nA two-factor between-subjects experimental design was employed to determine whether the strength of hemispheric lateralisation for facial emotion processing differs between children with and without a diagnosis of ASD. The independent variable for this research question was diagnostic status, a between-subject factor, with two groups; ASD and typically developing. Participants were assigned to one of these groups based on their diagnostic status, which was ascertained by their parent’s responses on the demographic questionnaire. The dependent variable for this research question was the strength of hemispheric lateralisation for facial emotion processing which was measured using the chimeric face task. \r\nA three-factor mixed-subjects predictive correlational design was employed to determine whether a child’s diagnostic status, and strength of hemispheric lateralisation for facial emotion processing can predict a child’s level of empathy. The predictor variables for this research question were diagnostic status, a between-subject factor (typically developing or ASD), and the strength of hemispheric lateralisation for facial emotion processing, a within-subject factor. The outcome variable for this design was empathy, a within-subject factor, measured by the Child Empathy Quotient. \r\nMeasures \r\nDemographic Questionnaire \r\nMaterials. The online demographic questionnaire (see Appendix A) was comprised of eight questions. Three of which required parents of the participants to input a response, these questions were used to determine the child’s age (in years), month of birth, and year of birth. The remaining questions were multiple choice, and therefore, required parents to select an answer out of 2-4 possible answer options. These questions acquired information including the child’s gender (male or female), dominant hand (left, right or don’t know/no preference), the language used in their home environment (English or other) and diagnostic status (formal diagnosis of ASD or no formal diagnosis of ASD). If the child did not speak English at home, then parents were required to input the language predominantly spoken. Parents who confirmed that their child had received a formal diagnosis of ASD were asked to input any comorbid diagnoses their child had received, so that they could be considered in the analysis. Parents who confirmed that their child had not received a diagnosis of ASD were asked if their child had received a diagnosis of any other neurodevelopmental disorders, this question was used for exclusionary purposes. \r\nProcedure. Completion of the questionnaire took approximately 2 minutes. Following completion of the questionnaire participants were excluded from the study and unable to proceed to the next stage if they did not meet the age criterion, or if they had not received a diagnosis of ASD but had received a diagnosis of another developmental disorder. \r\nThe Child Empathy Quotient (Auyeung et al., 2009)\r\nMaterials. The Child Empathy Quotient (EQ-Child) is a parent report questionnaire composed of 27 items (see Appendix B) used to measure a child’s level of empathy. This questionnaire was developed by Auyeung et al. (2009) using the adapted version of The Adult Empathy Quotient (Baron-Cohen & Wheelwright, 2004), individual items have therefore been modified and made applicable and relevant to children. The items therefore refer to behaviours, responses or difficulties commonly exhibited or experienced by children, e.g., ‘My child shows concern when others are upset’. Parents had to indicate the extent to which they agreed with each item by selecting one of the following options on a four-point Likert scale; ‘definitely agree’, ‘slightly agree’, ‘slightly disagree’, and ‘definitely disagree’.\r\nThe EQ-Child has previously been completed by parents of neurotypical children, and children with ASD, aged between 4- and 11-years-old. The pilot study conducted by Auyeung et al. (2009) yielded findings indicative of a high-internal consistency and good test-retest reliability, and the patterns of results were consistent with those found in adult research (Baron-Cohen & Wheelwright, 2004). \r\nProcedure. All parents were required to complete the EQ-Child, which took approximately 5 minutes. The order the questionnaire items were presented in remained consistent between parents and parents were unable to proceed to the next part of the study before they had provided a response for all 27 items. \r\nScoring. Parental responses on individual questionnaire items were converted into numerical points and summed together to calculate an empathy score for each child. For the following numbered items; 1, 4, 8, 10, 13, 14, 15, 16, 19, 21, 22, 23, 24, and 25, a response of ‘definitely agree’ equalled 2, ‘slightly agree’ equalled 1, and, ‘slightly disagree’ or ‘definitely disagree’ equalled 0. The remaining items were reverse coded. The maximal attainable empathy score was 54, the higher the score, the more empathetic a child is perceived to be by the adult completing the questionnaire. The scoring method applied in this study is consistent with the scoring method used, and detailed, in Auyeung et al. (2009). See Appendix B for the 27 items, and their corresponding item number. \r\nThe Autism Spectrum Quotient – Children’s Version (Auyeung et al., 2008)\r\nMaterials. The Autism Spectrum Quotient – Children’s Version (AQ-Child) developed by Auyeung et al. (2008) is a parent report questionnaire composed of 50 items (see Appendix C), used to quantitatively measure autistic traits in children aged between 4- and 11-years-old. The items in the AQ-Child are derived from the Autism Spectrum Quotient – Adult’s Version (Baron-Cohen et al., 2001) and the Autism Spectrum Quotient – Adolescent’s Version (Baron-Cohen et al., 2006), however, they have been revised and adapted to be pertinent to children. The items therefore refer to scenarios and behaviours that children are likely to have experienced or exhibited, e.g., ‘S/he would rather go to a library than a birthday party’. Parents indicated how strongly they agreed with each descriptive statement by selecting one of the following responses on a four-point Likert scale; ‘definitely agree’, ‘slightly agree’, ‘slightly disagree’ and ‘definitely disagree’. \r\nPrevious studies have administered the AQ-Child to parents of children with ASD, aged between 5- and 11-years-old (Auyeung et al., 2008). Administration of the AQ-Child has been reported to have excellent test-retest reliability and a high alpha and reliability coefficient. \r\nProcedure: \r\nThis questionnaire was only completed by the parents of children with a diagnosis of ASD, all of whom were unable to proceed to the next stage of the study until they had provided an answer for all 50 items. This questionnaire took approximately 5-10 minutes to complete. The order of items remained constant between parents. \r\nScoring. For each child reported to have a diagnosis of ASD, a total AQ score was calculated. Total scores were calculated by converting responses on the four-point Likert scale into numerical scores and summing them together. For the following items 1, 3, 8, 10, 11, 14, 15, 17, 24, 25, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 40, 44, 47, 48, 49 and 50, a response of ‘definitely agree’ equalled 0, ‘slightly agree’ equalled 1, ‘slightly disagree’ equalled 2, and ‘definitely disagree’ equalled 3. The remaining items were reverse scored. The higher the overall score, the greater number of autistic traits exhibited and endowed by the child. See Appendix C for the 50 items, and their corresponding item number. \r\nThe Chimeric Face Task \r\nMaterials. The chimeric face task is a widely used measure of the lateralisation of facial emotion processing. Chimeric faces are composite visual stimuli that are made by splitting two symmetrically averaged images of a face vertically down the middle and combining them together to depict a different emotional expression in each hemiface. The chimeric faces and the symmetrically averaged images used in this study derive from the work of Michael Burt (Burt & Perrett, 1997; Innes et al., 2016), and are supplied by Parker et al. (2021) via Gorilla Open Materials: https://gorilla.sc/openmaterials/104636.\r\nIn the practice trail, two symmetrically averaged images of male faces and two chimeric faces were used, these faces depicted the emotions fear and surprise. A further 12 chimeric faces were used in the experimental trial, which depicted all possible combinations of the emotion’s happiness, sadness, anger and disgust. Four symmetrically averaged images of male faces depicting these emotions were also used. See Figure 2 for the stimuli used in the experimental trial. \r\nFigure 2. The Facial Stimuli used in the Experimental Trial of the Chimeric Face Task  \r\nNote. The 16 facial stimuli presented to children during the experimental trial of the chimeric face task, including the four symmetrically averaged faces depicting the emotions happiness, sadness, anger and disgust, and the 14 possible combinations of these four symmetrically averaged faces. Adapted from “A leftward bias however you look at it: Revisiting the emotional chimeric face task as a tool for measuring emotional lateralisation” by B. R. Innes, D. M. Burt, Y. K. Birch, and M. Hausmann, 2016, Laterality: Asymmetries of Body, Brain and Cognition, 21(4-6), p. 649, supplied by “Assessing the reliability of an online behavioural laterality battery: A pre-registered study” by A. J. Parker, Z. V. Woodhead, P. A. Thompson, and D. V. Bishop, 2021, Laterality, 26.\r\nThe participants used emotional emoticons to indicate the emotion they believed to be depicted by the facial stimuli. In the practice trial, two emoticons were used, which illustrated the emotions fear and surprise. In the experimental trial, a further four emoticons were used, which illustrated the emotions happiness, sadness, disgust and anger. The emoticons used were taken from Oleszkiewicz et al. (2017), as it was found that children aged between 4- and 8-years-old were able to accurately assign emotions to these emoticons. See Figure 3 for the emoticons used in the experimental trial.\r\nFigure 3.The Emoticon Stimuli used in the Experimental Trial of the Chimeric Face Task\r\nNote. The emoticon stimuli selected by the child participants to indicate which emotion they believed the facial stimuli to be depicting. The emotions depicted by the emoticons, from left to right, are; anger, disgust, happiness and sadness. Adapted from “Children can accurately recognize facial emotions from emoticons” by in A. Oleszkiewicz, T. Frackowiak, A. Sorokowska, and P. Sorokowski, 2017, Computers in Human Behavior, 76, p. 373.\r\nProcedure. The procedure used derives from Parker et al. (2021), however, it has been adapted accordingly for its use with children. The chimeric face task was administered remotely via Microsoft Teams, a video collaboration platform. The virtual meeting was only accessible by the participant and the researcher, via a unique uniform resource locator, meeting ID and passcode. The chimeric face task could be completed on a laptop, computer or electronic tablet, and participants were required to share their screen to allow for the delivery of verbal instructions. Children were accompanied by an adult family member who was asked to refrain from engaging in any verbal and non-verbal communication with their child during completion of the task.\r\nPrior to administration of instructions, participants completed an estimation of screen size. This involved placing a 8.56cm X 5.39cm card onto the screen and dragging a bar until the size of the card on the screen corresponded with the physical card possessed by the participant. This was to ensure that all instructions and stimuli were presented as the same size to all participants. Instructions were administered visually to the child participants, using visual-graphic symbols, example screens and visual stimuli taken from the study, to ensure that the child’s understanding of the task was not compounded by their language ability. The visual instructions were accompanied by verbal instructions, that omitted the use of vocabulary that would not typically be understood by children aged between 5- and 10-years-old. Following administration of the instructions, participants were familiarised to two symmetrically averaged faces depicting the emotions fear and surprise, and their corresponding emoticons, which would be used in the practice trial. During completion of the practice trial participants were exposed to each symmetrically averaged face and each chimeric face, twice, meaning they were exposed to a total of 8 stimuli. The practice trial was employed to acquaint the child to the procedure used in the experimental trial.  \r\nFollowing the practice trial, participants were familiarised to the symmetrically averaged faces that comprised the chimeric faces used in the experimental trial, as well as their corresponding emoticons. These faces, and emoticons, depicted the emotions happiness, sadness, anger and disgust. Participants were verbally informed of the emotion depicted by stimuli and were instructed to click ‘next’ or indicate to their parent when they felt they had familiarised themselves with the stimuli presented. Participants were familiarised to the stimuli to ensure they knew which emotion each face and emoticon represented. The experimental trial was composed of four blocks. In each block the participants were exposed to the four symmetrically averaged faces, and the 14 chimeric faces, twice, meaning they were exposed to 32 stimuli per block, and 128 stimuli in total. Participants were exposed to the symmetrically averaged faces to assess their recognition of the emotion, and to the chimeric faces to determine the strength of their hemispheric lateralisation for facial emotion processing. \r\nBefore being exposed to the stimuli participants were asked to fixate on a white cross in the middle of the screen for 1000ms to ensure the child was looking directly at the facial stimuli when it appeared. This was important as the facial stimuli was only presented for 400ms. Following the presentation of each facial stimulus the participants had 10400ms to provide a response before automatically advancing to the next screen. All participants were instructed to “decide how the face is feeling and click on/point to/touch the emoji that shows that feeling”. The instruction provided differed depending on whether the child was responding using an electronic mouse, touch screen device, or by pointing and having their parent select the response for them. The latter of which was used for children who did not have access to a touch screen device, and who were not yet able to independently control an electronic mouse. \r\nAt three intervals during the chimeric face task, children were provided with the opportunity to take a break. During this break children received verbal praise and encouragement, the duration of the break was determined by the child. Administration of the chimeric face task took approximately 20-40 minutes. \r\nScoring. A laterality index was calculated for each child, to determine their strength of lateralisation for facial emotion processing. The laterality index was calculated by calculating the number of times the participant selected the emoticon corresponding with the emotion depicted on the right and left side of the face. The following sum was then computed for each participant 100 X (No. of right hemiface responses – No. of left hemiface responses)/(No. of right hemispace responses + No. of left hemiface responses). \r\nStudy Procedure \r\nEthical approval was obtained from the Lancaster University Department of Psychology Ethics Committee. Consent was received from all schools who agreed to distribute the study information to parents. Parental consent was obtained on behalf of all child participants, and oral consent was sought from the child participants during the virtual meeting. \r\nThe study was comprised of two parts, the first of which required parents to complete a series of questionnaires to provide a measure of the child’s demographic information, level of empathy, and autism severity. Parents were first presented with the demographic questionnaire to determine which additional questionnaires they were required to complete. If the parent’s responses on the demographic questionnaire indicated that their child had a diagnosis of ASD, then they were directed to, and required to complete, the EQ-Child and AQ-Child. If the parent’s response denoted that their child did not have a diagnosis of ASD they were only directed to, and required to complete, the EQ-Child. All questionnaires were completed on Gorilla (www.gorilla.sc), a cloud-based software platform for collecting data in the behavioural sciences (Anwyl-Irvine et al., 2020). All participants therefore completed the questionnaires remotely, on a personal electronic device. The questionnaires were compatible with a range of technological equipment, including a laptop, computer, electronic tablet and mobile phone. \r\nFollowing successful completion of the required questionnaires, parents received an email arranging a convenient date and time for their child to complete the second part of the study, which required their child to complete the chimeric face task. The data collected during completion of the chimeric face task was linked to the parental questionnaire responses via a unique participant ID code, which was allocated to parents following confirmation of participation. Following completion of the chimeric face task, a debrief sheet and certificate was sent to the parent’s email address.\r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3915"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3916"},["text","Data/Excel.csv"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3917"},["text","Brooks2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3918"},["text","Ching Yee Pang\r\nAleeza Sulaman\r\n"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3919"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3920"},["text","N/A"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3921"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3922"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3923"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3924"},["text","Dr Margriet Groen"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3925"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3926"},["text","Cognitive, Developmental"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3927"},["text","The final sample of participants consisted of 16 children aged between 5- and 10-years-old, of which 5 had received a formal diagnosis of ASD (5 boys; Mage = 6.8, SDage = 1.48). The remaining participants were 11 typically developing children (6 girls, 5 boys;  Mage =  7.0, SDage = 1.90), who had not been diagnosed with any neurodevelopmental disorders."]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3928"},["text","Linear Mixed Effects Modelling"]]]]]]]],["item",{"itemId":"190","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"208"},["src","https://johnntowse.com/LUSTRE/files/original/986ca14e7163ef0ec031b820f41202ef.pdf"],["authentication","15ac31078692a6a822b1e06dfab1c670"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3792"},["text","Inner Speech and Its Role in Purchasing Decision-Making Process: Analysis of Within-Subjects Experiment and Questionnaires"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3793"},["text","Han-Yi Wang"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3794"},["text","2022-23"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3795"},["text","Inner speech is a cognitive function related to language processes. Based on its functions reflecting information processing and memorising, it may link to the purchasing process, which includes searching and evaluating product information. Inner speech may also help people think and imagine using the product in the future during their purchasing process.\r\nThis study discussed and investigated the role of inner speech in the purchasing process and how it might affect the decision-making time. This study also mentioned how inner speech may be identified and suppressed. Participants’ data was collected through experiments and several questionnaires. The findings indicated that inner speech might help people in Information Search and Alternative evaluation and affect decision time. The findings also suggested what people may consider and how they use inner speech. \r\nBy uncovering the potential relationship between the purchasing process and inner speech, this research provided valuable information for marketing and psychology research fields. It gave companies some suggestions for practical use, reflecting how people may use inner speech during the purchasing process."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3796"},["text"," inner speech, purchasing behaviour, memory, decision-making."]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3797"},["text","Methods Section:\r\nThis study was approved by ethics committees at Lancaster University. There were no ethical issues for researchers managing the personal information. The participants’ information remained anonymous and were assigned subject ID (P01, P02, P03…, P30 in Experiment 1 and PCT01, PCT02, PCT03…, PCT30 in Experiment 2). All data were stored anonymously with no identifiable information. \r\nParticipants were given the Participant Information Sheet (PIS) before participating in the experiments. On the day of testing, they asked any questions they might have, then consented to attend the experiment in person or via online platforms like Microsoft Teams, Zoom, or Google Meet to ensure that the suppression was active when needed. The experiment took approximately 30 minutes, including answering all questionnaires. The experiment was held in the participant’s home or a place where no one spoke so that the participant would not be disturbed by any chance.\r\nExperiment 1\r\nParticipants\r\nG*power suggested 52 participants within groups using t-tests and multiple mixed linear regression models, with a .4 effect size and .05 (5%) a-error probability in 80% power (1-b error of probability) (Brysbaert, 2019). Thirty participants were recruited in this experiment with no record or history of neurophysiological disorders, such as dyslexia or aphasia, to ensure that no conditions influence the result and affect the participant to complete the tasks in the experiment. The recruitment process included in-person invitations around campus and social media messages to reach diverse participants.\r\nAlthough only 30 participants were recruited in this experiment, the results of the t-tests suggest that the effect size (see Experiment 1 result section) may be enough for testing the hypothesis.\r\nDesign\r\nThis study was an experimental within-subjects design. Participants simulated purchase experience in the suppression task and the control task without interference assigned to them. The independent variables were self-rating agreements on information search and alternative evaluation and participants’ average decision time in the suppression and control tasks. The dependent variables were inner speech frequency in five dimensions measured by the Inner Speech Frequency Questionnaire (VISQ). \r\nQuantitative data were analysed using R to conduct t-tests, GLMM and CLMM. Secondly, qualitative data were collected through questionnaires and categorised into different variables to identify why participants made the decisions and their inner speech content during the purchasing process.\r\nOverall, the experiment aims to investigate how people use inner speech during purchasing and whether Articulate Suppression task and task without interference influenced decision time and agreement score on information search and alternative evaluation.\r\nMaterials\r\nStimuli\r\nParticipants viewed six product sets (stimuli), which information was copied from the official website. To prevent participants from focusing on the effect of the products’ brands and prices (Albari & Safitri, 2020), the products in each set were the same brand with similar or the same price, unisex, and recognisable, although these products might not exist or remain the latest information on the market.\r\nTwo-item Statement Questions (see Appendix B)\r\n\tParticipants rated the two statements on a seven-point Likert score from strongly disagree to strongly agree (Maity & Dass, 2014) to identify the Information Search and Alternative evaluation agreement level between tasks. Then, participants were asked: “Which product did you choose? Why?” after each purchasing decision.\r\nVariety of Inner Speech Frequency Questionnaire (VISQ, see Appendix C)\r\nThe Inner Speech Frequency Questionnaire (Alderson-Day et al., 2018) included twenty questions asking participants to generally rate their inner speech frequency after the mock e-commerce purchasing tasks with a 7-point Likert scale ranging from \"Never\" to \"All the time\". Questions 7 and 15 were reversely coded; the value should be reversely calculated when doing analysis.\r\nExperiment 1 Qualitative Questions (ExpQ1, see Appendix D)\r\nAfter participants finished all the tasks (six decisions), they were asked to answer three questions at the end of the experiment. These questions gathered qualitative data about the participants’ experiences during the mock e-commerce purchasing tasks and what they had in mind. \r\nProcedure\r\nFigure 2 illustrates the diagram of Experiment 1. Participants were invited and consented to join the research to do Suppression and Control (without interference) tasks. \r\nEach task contained three product sets; participants were asked to imagine and choose a product for themselves or a friend according to the provided information on the mock e-commerce channel (Maity & Dass, 2014). The screen of the researcher or participants presented the information, including the price and details of the product set. Since these two tasks are counterbalanced and randomly ordered, participants repeated the decision-making process three times in the control task and the other three in the suppression task. After each decision, participants answered the two-statement questionnaire and explained which products they chose and why they chose them. According to different tasks, they started with the control task by themselves. However, they were asked to practise counting out loud from 1 to 4 following 160 bpm metronome sounds until the researcher ensured they remained suppressed before starting the suppression task.\r\nThen, they answered VISQ, which measured their inner speech frequency and qualitative questionnaires (ExpQ1) to understand how they used inner speech when viewing the products in the last part of the study. \r\nAnalysis\r\nR was used to analyse the quantitative data to identify the task differences via t-tests and the relationship between variables in two tasks via Generalised Linear Mixed Effect Models (GLMM) and Cumulative Link Mixed Model (CLMM). When conducting the GLMM with family gamma, the quantitative data will follow the standard procedure of data trimming and keep the trimmed data within 5% or 2.5 standard deviations (Berger & Kiefer, 2021). \r\nThe qualitative coding scheme (See Appendix F) was created to identify what participants considered and what they said to themselves using inner speech during the experiment. The coding process involved re-reading the data to identify and assign relevant contexts to the appropriate categories. For example, if participants mention that they have used the product before, the value of the variable “Memory” increases by one unit. These variables were then calculated to identify what factors influenced participants’ purchasing decision-making more. Following the same coding scheme, what kind of inner speech was used when viewing the products could also be found. For example, people may ask themselves questions or repeat the product in mind.\r\nIn summary, Quantitative and qualitative data were analysed to report the results for different purposes and test the hypothesis in this research.\r\nExperiment Optimising\r\nThe task without interference in Experiment 1 may not be a reasonable control task since it might include the secretary task effect, as participants were asked to do both tasks and be influenced after they did the suppression task when they were doing the control task. \r\nAs a secretary task, the finger-tapping task, which has been used in inner speech experiments, could be the better control task in Experiment 2 (Emerson & Miyake, 2003; Wallace et al., 2009). Although Finger-tapping might influence working memory’s function and influence people to memorise (Armson et al., 2019; Kane & Engle, 2000; Moscovitch, 1994; Rose et al., 2009), Rogalsky et al. (2008) also mentioned that the performance of people’s understanding of complex sentences might decrease but not as much as suppression occur. \r\nTherefore, doing the second experiment was motivated to replicate the results with a better control condition involving Finger-tapping.\r\nExperiment 2 \r\nParticipants\r\nBased on the findings of Experiment 1, another 30 participants were recruited with the duplicate requirements as the first experiment. The recruitment requirement and process were the same as in the previous experiment.\r\nDesign\r\nThe independent variables were similar to Experiment 1, while the only difference was that the control task here had been changed into the Finger-tapping task. The goal of the whole design is to replicate the results of Experiment 1 to investigate the role of inner speech in the purchasing process.\r\nMaterials\r\nExperiment 2 applied the same materials used in Experiment 1. The only difference was the qualitative questions after tasks. In Experiment 1, participants answered “Experiment 1 Qualitative Questions” at the end of the experiment. However, to better understand the difference between tasks, they were asked to answer a similar questionnaire (see below) after each task to discover the inner speech used in the two tasks.\r\nExperiment 2 Qualitative Questions (ExpQ2, see Appendix E)\r\nParticipants were asked to answer three questions about their experiences during the mock e-commerce purchasing tasks and what they had in mind for the Suppression and Finger-tapping tasks separately.  \r\nProcedure\r\nThe procedure was the same as the first experiment, except for adjusting the control task and the order of the qualitative questionnaire (ExpQ2). Figure 3 illustrates that participants were invited to the experiment using the same stimuli, similar questionnaires, and the same method of presenting stimuli (participants joined in person or via online platforms) with Suppression and Finger-tapping tasks. Participants were asked to practice counting 1,2,3,4 out loud or tapping their index, middle, ring, and little fingers in order (see which task came first) following metronome beats at 160 bpm before the researchers decided to move on. They were asked to view the product set by imagining choosing one for a friend or themselves three times in each task. Participants answered two statements and answered what product was chosen and why after each decision they made. Then, they were asked to answer three Qualitative questions (Appendix E) after each task. They repeated another task in the same process afterwards with a 2-minute break between tasks. After they finished the Finger-tapping and Suppression tasks, they answered VISQ questions at the end of the experiment.\r\nAnalysis\r\nR was also used to analyse the quantitative data for the same purposes and followed the same data-trimming procedure if needed. The same coding scheme was followed to generate the result that could replicate and optimise the clarity of the Experiment 1 results. Overall, the second experiment is to generate the same or more evident results as Experiment 1 and to find more valuable information for the different inner speech used between tasks.\r\nIn conclusion, these two experiments and the analysis might give this research a deeper understanding of inner speech and its role and provide more precise information on how inner speech may related to the purchasing process."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3798"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3799"},["text","The data set is in csv format."]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3800"},["text","Wang2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3801"},["text","Melanie Thomas\r\nVickie Huang"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3802"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3803"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3804"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3805"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3806"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3807"},["text","Dr Bo, Yao"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3808"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3809"},["text","Cognitive \r\nDevelopment "]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3810"},["text","60 participants \r\n30 in experiment 1\r\n30 in experiment 2"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3811"},["text","Linear mixed-effects modelling, Power Analysis, Qualitative, Regression, t-test"]]]]]]]],["item",{"itemId":"188","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"221"},["src","https://johnntowse.com/LUSTRE/files/original/02bb9218d0b3af78bfd7128818e52817.doc"],["authentication","19a8aed24e888a51cf35142b9e4852b2"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3747"},["text","Prospect theory and intermediate audience: the effects of context on behavioural intention"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3748"},["text","Wai Man Ko "]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3749"},["text","01/09/2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3750"},["text","Prospect theory predicts how people react to gain or loss-framed outcomes in dilemma situations, where the potential consequence of the choice is framed as a gain (e.g., lives saved) or as a loss (lives lost). This gain-loss framing communication strategy, derived from the theory, has been applied in many contexts, from promoting the use of reusable coffee mugs to vaccination compliance, with loss-framed appeals being found generally to be more persuasive than gain-framed appeals in the context of promoting vaccination. The current study focused on exploring whether these well-established effects persist when an intermediate audience is exposed to gain/loss-framed messaging, using influenza (flu) vaccination intentionality as an outcome. Intermediate audiences refer to those who are evaluating the gains and losses from the message on behalf of someone else (the ultimate audience), while normal audiences are those making decisions on their own behalf. Two hundred participants were recruited for an online, between-subject study, in which participants were split into two audience conditions and within which they were further split to view a gain-framed or a loss-framed message. Their subsequent behavioural intentions were measured as the outcome, with age as a potential moderating factor (and emotional attachment as a potential mediator exclusively for the intermediate audience condition). Results indicate that neither age nor emotional attachment are significant moderators or mediators. Loss-framed appeal enjoyed a persuasive advantage over the gain-framed appeal only in the intermediate audience condition. Possible interpretations of results, along with potential further directions of research, are discussed. "]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3751"},["text","Prospect theory, gain/loss framing, intermediate audience, communication research, health communication, vaccination"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3752"},["text","To test the outlined hypotheses, our current study took the form of an online Qualtrics questionnaire (see appendix B for questions) where the questionnaire would introduce participants to one of the audience conditions and view the appropriate version of the manipulated message before moving on to answering some items measuring their behavioural intention and emotional attachment. The study has a 2 (intermediate/normal audience condition) X 2 (gain/loss-framed appeal) design with emotional attachment as a potential mediating variable for the intermediate audience condition and behavioural intention as the outcome variable for all audience conditions. \r\nParticipants\r\nWe recruited 200 healthy adults based in the UK on Prolific, an online research participant recruitment platform. Participants have provided consent and completed the study remotely with their personal devices. Their unique Prolific ID was used in this study as the only identifier, which cannot be traced back to them personally. Participants were compensated monetarily for their participation.\r\nWe randomly assigned our participants to one of the four audience conditions with 50 participants each: the normal gain-framed condition, the normal loss-framed condition, the intermediate gain-framed condition, and the intermediate loss-framed condition.\r\nQuestionnaire design\r\nConsent\r\nThe participant gave consent to participate in the study with the Qualtrics consent element so that participants can check a box for each item. There were seven items that the participants had to check one by one before commencing the study. Responses which failed to provide a full response in the consent item would be removed from the study.\r\nDemographics\r\nFor demographics, we have recorded the participants' age and gender for the records. As mentioned, age was also analysed as a moderator as part of our analysis. We have also recorded their Prolific IDs to ensure completion and arrange payment.\r\nSettings of the study\r\nAfter giving demographic information, participants were introduced to a small piece of information that gave them the context of this study. In normal audience conditions, participants were told that someone had sent them an ad about the flu vaccination, which refers to the manipulated message they will soon view. While for the intermediate audience, on top of the information that is revealed to the normal audience, they were exclusively told that they were a manager in a small town's paper company, which gives them the role of an intermediate audience (manager) who must evaluate the later presented message on behalf of other parties (employees) with themselves irrelevant to the gains and losses. \r\nMaterial\r\nWe have chosen flu vaccination as our topic malady for the manipulation messages as COVID vaccines, as used in recent studies, are perhaps less relevant in what is generally thought of as the post-COVID era. Flu vaccinations, unlike many other vaccines, remain relevant to the major population and most age groups. To allow a closer resemblance to real-world settings and increase the generalisability of the results, we have made unofficial Facebook posts that claim to be from the NHS as the message format. Participants were informed that the graphics were not an actual Facebook post from the NHS but rather a material used solely for this study. See Figure 2 for an example, and appendix A for the complete set of stimuli presented to the participants in the study.\r\nAudience condition. Figure 2 is the gain-framed version of the message from the normal audience condition. In normal audience conditions, the message communicates directly to the participants, stating the potential pros or cons for the participants when the participants decide to vaccinate or not vaccinate. In this condition, it is assumed that the participants evaluated the message on their behalf and nobody else's. While on the contrary, the intermediate audience condition communicates a slightly different message. The \"you\" in the message is replaced by \"your employees\". The purpose of this is to highlight that the participants evaluate this message as an intermediate audience (the manager), deciding whether they would recommend the vaccine to somebody else (the ‘ultimate audience’) given the outlined potential gains and losses, while the gains and losses remain irrelevant to the participants personally.\r\nMessage framing. The figure is a gain-framed message, and as mentioned, it follows the logical flow of \"if you vaccinate, good things will happen\". As we can see in Figure 2, if the recipient vaccinates, then according to the text, he/she would have a reduced chance of infection and a reduction in the duration and severity of the symptoms. The lost-framed version of the message follows the logical flow of \"if you do not vaccinate, bad things will happen.\" So, in contrast to figure 2, the lost framed messages would say if the recipient does not vaccinate, he/she would have an increased chance of infection and increase in duration and severity of the symptoms. The two messages communicate the same reality and are logically equivalent. Hence, any differences between the groups can be attributed to the message framing.\r\nCheck questions.\r\nAfter viewing the message, the participants were asked two questions regarding the ads content before moving on to later questions. The check questions were designed to be simple reading comprehension questions that check whether the participants attended to the message in the reading process. We have removed all responses failing to provide a correct answer in either one of the questions.\r\nBehavioural intention\r\nAfter viewing the framed messages, we have several Likert scale 7-point agree-disagree items used to measure the behavioural intention of the participants. However, given the audience condition differences and hence the potential differences in the decision-making process, behavioural intention for the two types of audience is defined differently. For the intermediate audience condition, behavioural intention is defined as \"the intention to recommend/promote behaviour to the ultimate audience (employees)\". While for the normal audience conditions, we measure their intention to get the vaccination for themselves. Both audience conditions responded to six items probing their behavioural intentions. In the normal audience condition, participants were asked how likely they would be to get the flu jab, how urgent they thought it is, and whether they would likely plan to get a flu jab after viewing the message. There are also items with reversed wordings asking whether they think getting a flu jab is NOT urgent. The intermediate audience was asked how likely they are to recommend the flu vaccine to their employees and how urgent and necessary they believe the vaccine is to their employees. (See the appendix for the complete set of questions.)\r\nEmotional attachment\r\nAs mentioned, there are speculations revolving around the involvement of relational dynamics and relevant emotions in the intermediate audience. Therefore, we have arranged a set of questions probing the participant's emotional attachment towards the employee exclusively for the intermediate audience condition. There were four questions in total in this part of the study, which focused solely on the participants' sense of protection towards the employee, asking to what extent the participants thought that the vaccine was necessary for the employee's own good and well-being, and to what extent were the participants eager to protect them; an item with reversed wordings were also included. (See the appendix for the complete set of questions.)\r\nMethod of analysis \r\nWe analysed the data using the clm() and clmm() functions from the ordinal package in RStudio using R version 4.1.1. We first confirmed the main effects of message framing and audience conditions using clm(), and then we moved on to analyse the magnitude of random interacting effects of age, question type and individual differences. The reason for choosing cumulative link models (clm) was that the models were designed explicitly for ordinal variables like Likert scales, which predict the probability of each response level, unlike some metric models and prevent type 1 and type 2 errors resulting from forcing ordinal variables onto metric models (Liddell & Kruschke, 2018). As for emotional attachment, given each item was probing quite a different emotion (e.g., sense of responsibility/ sense of protection), we have decided to fit a multivariate ordinal variable using the mvord() function to see if there is a significant difference in the multiple emotional outcomes under different audience condition, after which we investigated if any emotional attachment item was a significant predictor of behavioural intention using another clm model. We have also fitted clm() models including the interaction term between age and conditions predicting behavioural intention to see if age moderates the relationship between message framing and behavioural intention as proposed. Lastly, we have fitted a cumulative link mixed model (clm) to consider the role of potential sources of random effects such as participant differences and question differences in the analyses."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3753"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3754"},["text","Data/Excel.csv\r\nAnalysis/r_file.R\r\nText/Word.doc"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3755"},["text","Ko2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3756"},["text","Eleanor Little, Alicia Turner, Laurie Dixon"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3757"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3758"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3759"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3760"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3761"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3762"},["text","Leslie Hallam"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3763"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3764"},["text","Marketing"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3765"},["text"," 185 participants (124 females, 58 males, 2 non-binary, and 1 undisclosed)"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3766"},["text","Regression"]]]]]]]],["item",{"itemId":"186","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"205"},["src","https://johnntowse.com/LUSTRE/files/original/42f25a4afae4681322de3eaca175d305.pdf"],["authentication","f34904e516c4c04821ec1e52402b3ea9"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3707"},["text","Cerebral Lateralisation for Emotion Processing of Chimeric Faces in Individuals with Autism Spectrum Disorder "]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3708"},["text","Alexandra Crossley"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3709"},["text","5th September 2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3710"},["text","Many studies have suggested that typical lateralisation for emotion processing tasks, such as facial emotion recognition, is lateralised to the right-hemisphere, with different emotions eliciting differing strengths of lateralisation (Bourne, 2010). However, there has been much debate as to the lateralisation of individuals with autism spectrum disorder (ASD) (Ashwin et al., 2005; Shamay-Tsoory et al., 2010). This study assessed the cerebral lateralisation of 30 adults with ASD, five children with ASD, 435 neurotypical adults and ten neurotypical children in a chimeric faces task, and aimed to identify whether the atypical lateralisation seen in children with ASD persists into adulthood (Taylor et al., 2012). Furthermore, the study aimed to identify whether lateralisation strength is affected by the emotion of the facial stimuli. No emotion- or age-related change in lateralisation was found, however, participants with ASD demonstrated a weaker right-hemispheric lateralisation compared to neurotypical participants. Therefore, this study supported the concept that individuals with ASD show atypical lateralisation which persists into adulthood, however, no evidence was found to support the concept that different emotions elicit different strengths of lateralisation."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3711"},["text","autism spectrum disorder, cerebral lateralisation, emotion processing, adults, children, chimeric faces task"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3712"},["text","Method\r\nParticipants\r\nData from a total of 481 participants with native level English proficiency (or age expected language development in children), normal or corrected-to-normal vision and no history of neurological disease or hearing loss were analysed for the current study (Table 1). Participants in the group ‘adults with ASD’ (N = 30; age: M = 30.17, SD = 9.85) were recruited through adverts on social media, through Prolific Academic (www.prolific.co), and through word of mouth. Participants in the groups ‘children with ASD’ (N = 5; age: M = 6.8, SD = 1.48) and ‘neurotypical children’ (N = 11; age: M = 7.0, SD = 1.90) were recruited through primary schools and word of mouth (Brooks, 2023), and parents of potential child participants were required to \r\n \r\nemail a researcher to express their interest in participation. Participants in the group ‘neurotypical adults’ (N = 435; age: M = 29.44, SD = 8.03) were recruited through Prolific Academic (www.prolific.co) as part of a larger online behavioural laterality battery (Parker et al., 2021). Of the 481 participants who took part in the study, 32 were excluded during the data cleaning process (see Table 1 and Data Analysis for further information).\r\nMeasures\r\nAs part of the study, a series of questionnaires were administered to collect information about the participants to ensure that individual differences could be accounted for. Participants were asked to complete the study and its associated questionnaires and tasks prior to beginning the main chimeric faces task, and were requested to use a desktop or laptop computer for the entirety of the study. For the ‘neurotypical children’ and ‘children with ASD’ groups, parents were asked to complete the questionnaires on behalf of the children and were asked to be present for the tasks, which were completed during a Microsoft Teams call with a researcher.\r\nThe study was completed online using the Gorilla Experiment Builder (www.gorilla.sc), a cloud-based tool for collecting data in the behavioural sciences. \r\nDemographic Questionnaire\r\nThe demographic questionnaire asked participants their age, gender, length of time in education (in years), language status, two questions assessing handedness (“Which is your dominant hand? / Which hand do you prefer to use for tasks such as writing, cutting, and catching a ball?”) and footedness (“Which foot do you normally use to step up on a ladder/step?”), and two eye dominance tests (Miles, 1929; Porac & Coren, 1976). Participants were also asked whether they had a diagnosis of any developmental disorders, including ASD, dyslexia, attention deficit hyperactivity disorder or a language disorder (such as 'developmental language disorder' or 'specific language impairment'). For each diagnosis, participants had the option to answer “Yes”, “No”, or “Prefer not to say”, with the exception of ASD which also had the option to answer “No but I am self-diagnosed”. At this point, participants were sorted into their groups based on age (‘children’: five- to 11-years-old; or ‘adults’: 18- to 50-years-old) and ASD diagnosis (‘with ASD’, or ‘neurotypical’). Adults with a self-diagnosis of ASD were included in the ‘adults with ASD’ group.\r\nEdinburgh Handedness Inventory\r\nThe Edinburgh Handedness Inventory (EHI; Oldfield, 1971) was administered to provide a scaled score of handedness. Adult participants were asked to score ten daily tasks on a five-point Likert scale based on which hand they preferred to use during each task (“Left hand strongly preferred” = 2, “Left hand preferred” = 1, “No preference” = 0, “Right hand preferred” = 1, or “Right hand strongly preferred” = 2). These tasks included daily activities such as writing, brushing teeth, and opening a box. The EHI was scored by combining the direction and exclusiveness of the hand preference. Two totals were created: one of right-hand preference and one of left-hand preference. The difference was then found by subtracting the left-hand total from the right-hand total. This was then divided by the total score of both hand preference scores and multiplied by 100 (i.e., 100 x (right-hand total – left-hand total) / (right-hand total + left-hand total)). Final EHI scores ranged from -100 to +100, with positive scores indicating right-handedness, and negative scores indicating left-handedness. Child participants were not required to complete the EHI questionnaire.\r\nLexical Test for Advanced Learners of English\r\nA version of the Lexical Test for Advanced Learners of English (LexTALE; Lemhöfer & Broersma, 2012) was provided to assess the participants’ level of proficiency in English. Within this, adult participants were shown 60 written stimuli comprised of English words and pseudowords (words that follow the orthographical and phonetic rules of the English language and are pronounceable but are otherwise nonsense words, e.g. ‘proom’) and asked to assess whether each word was an existing English word or not. Scores of the test were collected by averaging the percentages of correct answers for English words and pseudowords, with final scores ranging from 0-100. Child participants were not required to complete the LexTALE task.\r\nAutism-Spectrum Quotient (Short Version)\r\nAn abridged version of the Autism-Spectrum Quotient (AQ-Short; Hoekstra et al., 2011) was used to provide a measure of ASD traits. Participants with ASD were asked to rate 28 statements on a four-point Likert scale based on their level of agreement, with each answer accruing a different number of points (“Definitely agree” = 1, “Slightly agree” = 2, “Slightly disagree” = 3, or “Definitely disagree” = 4). On items in which “Definitely agree” represented a characteristic of ASD, the scoring was reversed. The scores for each question were totalled, with potential scores ranging between 28 (no ASD traits) to 112 (full inclusion of all ASD traits). Scores above 65 indicated ASD traits to a diagnosable degree. Neurotypical participants were not required to complete the AQ-Short questionnaire.\r\nProcedure\r\nLateralisation for Facial Emotion Processing Task\r\nA chimeric faces task was used to assess lateralisation for facial emotion processing.\r\nStimuli. The chimeric faces stimuli were created by Dr Michael Burt (Burt & Perrett, 1997) and provided by Parker et al. (2021).\r\nA collection of 16 different facial stimuli were created by merging two photographs of a man’s face depicting one of four emotions (‘happiness’, ‘sadness’, ‘anger’, or ‘disgust’) vertically down the centre of the face and blended at the midline (see Figure 1 for an example). Each emotion was paired either with itself, causing both hemifaces of the facial stimuli to match in emotion (a ‘same face’), or with a differing emotion, causing both hemifaces of the facial stimuli to be different (a ‘chimeric face’). Of the 16 stimuli, 12 were ‘chimeric face’ and four were ‘same face’.\r\nTask. Each trial began with a fixation cross shown for 1000ms, followed by the face stimuli for 400ms. Participants then recorded which emotion they saw most strongly by clicking the corresponding button from a choice of the four emotions (Figure 2). For the children, emoticons were used instead of written words (Oleszkiewicz et al., 2017) (Figure 3). A response triggered the beginning of the next trial, with a time-out duration set at 10400ms after which the next trial was triggered automatically. Response choice and response times were recorded. \r\nThe task was split into four blocks of trials with a break between each block. Stimuli were presented in a random order and shown twice in each block, resulting in the participants being shown 32 stimuli per block and a total of 128 within the whole task. \r\n\r\n   \r\nParticipants were familiarised with the stimuli at the start of the task, with the ‘same face’ stimuli being shown alongside a label explaining which emotion was being presented, to ensure they could recognise the emotions. A practice block was given at the start of the task to ensure participants knew how to complete the task, using the emotions ‘surprise’ and ‘fear’. \r\nAdditional Measures\r\nAs data collection also included tasks for other studies, participants were also asked to complete a version of the Empathy Quotient – short (Wakabayashi et al., 2006), and undertake a dichotic listening task and its associated device checks (Parker et al., 2021). As these items were not part of the main study, participants were asked to complete these following the completion of the main study and its associated questionnaires and tasks, to ensure any findings from the study were not due to the additional measures.\r\nLaterality Index\r\nA laterality index (LI) for each participant was calculated using the same method as Parker et al. (2021) by finding the difference between the number of times the participant chose the right-hemiface emotion and the left-hemiface emotion. This was then divided by the total number of times they chose either the right- or left-hemiface emotion, and multiplied by 100 (i.e., 100 x (right hemiface – left hemiface) / (right hemiface + left hemiface)). Scores ranged between -100 and +100, with a negative LI indicating a left-hemiface bias, and thus, a right-hemispheric dominance, and a positive LI showing the opposite.\r\nData Analysis\r\nParticipants who scored less than 80 on the LexTALE task were removed as it was deemed their understanding of the English language was not strong enough and may cause issues with understanding the instructions (Parker et al., 2021). Furthermore, all trials with a response time faster than 200ms were removed as it was suggested that responses at this speed were too quick to have been based on the processing of the stimuli (Parker et al., 2021). In addition to this, outlier response times for each participant were removed using Hoaglin & Iglewicz's (1987) procedure. Within this, outliers were any response times 1.65 times the difference between the first and third quartiles, below the first quartile or above the third (e.g., below Q1 – (1.65 x (Q3-Q1)), and above Q3 + (1.65 x (Q3-Q1))). Following the removal of all outlying trials, any participant with less than 80% of trials remaining were removed. In addition to this, participants who scored less than 75% on ‘same face’ trials (trials in which both hemifaces depicted the same emotion) were noted, because emotion processing is an area of difficulty for individuals with ASD. Within this, three participants in the ‘children with ASD’ group (60%), three participants in the 'neurotypical children’ group (27.27%), four participants in the ‘adults with ASD group (13.33%), and 30 participants in the ‘neurotypical adults’ group (7.41%) scored less than 75% on ‘same face’ trials, suggesting they had difficulties identifying the emotions.\r\nTo address the hypotheses, a linear model was performed using LI as the outcome and group (‘ASD’ or ‘neurotypical’), age (‘adult’ or ‘child’) and emotion (‘happy’ and ‘angry’, or ‘sad’ and ‘disgust’) as the predictors, including interactions between each predictor (Group x Age; Group x Emotion; Age x Emotion; and a three-way interaction, Group x Age x Emotion).\r\n\r\n\r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3713"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3714"},["text",".csv"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3715"},["text","Crossley2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3716"},["text","Alexandra Haslam \r\nAlexis McGuire\r\nxue guo"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3717"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3718"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3719"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3720"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3721"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3722"},["text","Margriet Groen"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3723"},["text","MSC"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3724"},["text","Developmental, Neuropsychology "]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3725"},["text","481 participants with native level English proficiency, 164 Male, 240 female and 1 other."]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3726"},["text","Linear Mixed Effects Modelling and T-Test"]]]]]]]],["item",{"itemId":"180","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"201"},["src","https://johnntowse.com/LUSTRE/files/original/a4991188e6e5a175e3e601f45ba8d3d5.csv"],["authentication","c132301a3565074e33f0070c2a24dfd8"]],["file",{"fileId":"202"},["src","https://johnntowse.com/LUSTRE/files/original/08c1180ec22a5097b67bdf27998d19cd.csv"],["authentication","8b729f810fe7b7fa25327f0ec2d0e5be"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3632"},["text","Inner Speech and Grit: Do Positive Inner Speech and Evaluative Inner Speech Lead to Grit Behaviour"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3633"},["text","Huzaifah Adam"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3634"},["text","2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3635"},["text","Grit, defined as perseverance and passion for long-term goals, is a reliable predictor of success metrics, surpassing even IQ. While the exploration of grit has been conducted extensively, studies on the mechanisms of grit are still lacking. Inner speech, the silent production of words in one’s mind, plays a pivotal role in managing thoughts. This includes cognitive reframing, which is essential for enhancing perseverance. Theoretically, inner speech can predict grit. This study, employing a survey and experimental design, aims to investigate whether positive inner speech and evaluative inner speech can predict grit behaviour. The data for this study (n=56) were collected in two ways: (1) using the grit scale and inner speech VISQ-R via a Qualtrics survey, and (2) using participants’ task retention decisions and a qualitative classification approach. The data were analysed using R Studio. The survey data were analysed via a linear model, while the qualitative data were analysed using a generalised linear mixed-effects model. The survey results showed that only evaluative inner speech can positively predict grit. However, there were imbalanced results regarding the participants’ task retention decisions. Collectively, these findings underscore that grit can be predicted by evaluative inner speech. This prompts further research to explore its multifaceted role in shaping grit across various domains."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3636"},["text","Inner speech, grit, articulatory suppression"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3637"},["text","This study applied a mixed-method and correlational research design that aims to examine whether evaluative inner speech and positive inner speech lead to grit behaviour. The data for this study were collected using two methods: (1) questionnaires through a Qualtrics survey, and (2) an experimental task where the participants were asked to complete two sets of puzzles under different conditions (baseline and with articulatory suppression) and provide their retrospective experience after each puzzle task. Participants’ task retention decisions (decision to quit) were also recorded in the study. Three different analyses were applied in the research. For the first analysis, the positive inner speech and evaluative inner speech scores from VISQ-R acted as the predictors, and grit from the Short Grit Scale as the outcome. For the second analysis, the participant’s grit score acted as the predictor, and the participant’s task retention decision acted as the outcome. Lastly, the third analyses the types of inner speech based on the participant’s retrospective experience (positive inner speech and evaluative inner speech) acted as the predictors, and the participant’s decision to quit or not to quit was the output.\r\n\r\nIn this study, the participants were students from Lancaster University, ranging from undergraduate degree students to master’s degree students and doctorate students. Participants were recruited using social networks, direct emails, and posters around the campus and/or on social media. The session took approximately 30 minutes for the data collection process, including the briefing, and each participant was reimbursed with five GBP for participating. Ethical approval for this study was submitted and approved by the ethics committees at Lancaster University.\r\n\r\nThe number of participants involved in the study was 56 people in total. This number was determined by using G power. The test family was set at the t-test because this research will use a comparison between the control approach (baseline) and the experimental approach (with articulatory suppression). The effect size f2 was set at 0.15, while the α-error probability was set to 0.05 (5%) and the power 1−β error of probability at 0.8 (80%), with the number of predictors set at five. In total, 56 participants took part in the study, where the number of male and female participants was 23 (41%) and 33 (59%), respectively, and the number of native English participants in the study was 15 (27%), while non-native speakers were 41(73%).\r\n\r\nDemographic Information: The demographic information collected pertained to each\r\nparticipant’s attributes. This included sex (male, female, non-binary/third gender, and prefer not to say) and English native background (yes or no). Although the study has no biases towards the participant’s native language, the word used in the study ‘aluminium’, a word that is suggested by Gathercole and Baddeley (2014) for the research, may or may not influence the fluidity of pronunciation, making the articulatory suppression more challenging for non-native speakers.\r\n\r\nVarieties of Inner Speech Questionnaires Revised (VISQ-R): The VISQ-R was developed to link the everyday phenomenology of inner speech, including any psychopathological traits and inner dialogue (Alderson-Day et al., 2018). There are two versions of the Varieties of Inner Speech Questionnaire, where the original one consisted of 18 items and the revised version VISQ-R consisted of 26 items (see Appendix D) that took approximately 5-8 minutes to be completed via a Qualtrics survey. In this study, VISQ-R has been presented as internal experience questions as a dummy to the real name. This is to eliminate any possible biases by the respondents.\r\n\r\nResponses from VISQ-R can be subdivided into five dimensions and into seven scales (Not like me at all – Very much like me) for scoring: dialogical, evaluative, condensation, other people, and positive. A higher score in dialogical indicates that the person often uses inner speech to exchange ideas with oneself and vice-versa. A higher evaluative score means that the person often uses inner speech to evaluate their thoughts, actions, and decisions. For condensation, a higher score indicates that a person talks to themselves in a concise or short words manner to encapsulate complex thoughts or ideas. Meanwhile, a higher ‘other people’ score indicates that a person often imagines other people’s voices or opinions when engaging in inner speech. Lastly, a high positive score indicates that the person often uses inner speech to encourage oneself in a supportive and comforting manner. Subscale totals for each dimension were acquired by adding the scores for each subscale and dividing it by the total number of items answered across the respective subscale.\r\n\r\nThe Varieties of Inner Speech Questionnaire has been supported for its reliability and validity in measuring inner speech. Racy et al. (2022) have studied the reliability of VISQ-R and compared it to six other instruments relating to inner speech. VISQ-R has moderate to strong concurrent validity with other instruments with self-evaluation showing a strong correlation with other measures. The internal consistencies and reliabilities were excellent (Cronbach’s α > .80) for each of the dimensions with only a positive dimension that is slightly lower with moderate to high test-retest reliability (>.60) (Alderson-Day et al., 2018).\r\nShort Grit Scale (Grit-S): The questionnaire of Grit-S was developed by Angela Duckworth to measure the trait level of perseverance and passion for long-term goals (Duckworth & Quinn, 2009). The Grit-S consisted of eight items of questions (See Appendix D) with four fewer items in comparison to the original version, retaining the factor structure and improving on the psychometric properties. The questionnaire needs an approximation of 3- 5 minutes to be completed in the Qualtrics survey. Similar to VISQ-R, the Grit-S questionnaire has been presented as a personality instead of a grit scale to avoid any possible biases.\r\nThere are two dimensions included in the Grit-S for scoring: Consistency of Interest, where a higher scale subscale score indicates that the individual is able to maintain their interest for and focus on their long-term goal, and Perseverance of Effort, where a higher subscale score represents sustained effort towards a long-term goal despite the presence of setbacks (Van Doren et al., 2019). The subscale for the dimension of Consistency of Interest is acquired by adding the scores for all the subscale items (item-1, item-3, item-5, and item-6), while for Perseverance of Effort (item-2, item-4, item-7, and item-8). There are a few items that have been coded inversely and have been recoded before running the analysis.\r\nSeveral research studies have confirmed the validity and reliability of the Short-Grit Scale Instrument. Eskreis-Winkler et al. (2014) conducted a study involving predicting retention in the military where the grit instrument was used to measure the grit level of cadets. The instrument has been proven to be reliable as grittier soldiers were more likely to complete the Army Special Operation Forces (ARSOF), likely to get a job, and likely to stay married. In a more recent study by Priyohadi et al. (2019), the Grit-S again proved its validity and consistency. The internal consistencies between items in a dimension were moderate to high (>.60) for both persistence of effort and consistency of interest and have high consistencies between studies.\r\nActive Task: The jigsaw puzzle was used as the active task for this research. Two jigsaw puzzles from Livewire Puzzles were predetermined by the website as expert-level with 70 puzzle pieces (10 X 7) with an 8-minute time limitation. The puzzle can be accessed through the games.puzzle.ca website. The puzzles have been created by Arkadium, a company that is well-recognised in making online games. New puzzles have been uploaded daily, but to avoid any possible advantage or disadvantage, the puzzles used are from the 22nd of June 2023 and 21st of June 2023. Marks will also be provided at the end of each puzzle.\r\n\r\nThere are two ways of measuring participants’ performance: (1) Quitting - participants were allowed to quit the task at any time during the 8-minute time limit by telling the researcher present that they want to stop, and (2) Puzzle performance - marks will be given at the end of the puzzle (marks will be given even if participants quit halfway) by the source website. The marks will be calculated based on the number of puzzles fixed correctly and then divided by the total number of unfixed puzzles and will be multiplied by the amount of time left in the puzzle. The maximum score of the puzzle is 5,000 and the minimum score is zero. All calculations will be automatically measured by the source website.\r\nThe puzzle from Livewire Puzzle has also been used by other studies that focus on measuring grit using an active task. Kalia et al. (2019), similar to this study, used puzzles from Livewire Puzzle as an active task to measure perseverance in participants. Instead of using a jigsaw puzzle, Kalia opted to use sudoku to measure the role of grit and cognitive flexibility 2.4 Procedure\r\nThe research took place in one-on-one sessions at the Lancaster University library. Data collection sessions were administered in the following order: demographic information, the first puzzle task, the difficulty level question, the subjective inner speech question, the second puzzle task, the second puzzle difficulty level question, and finally, the second subjective inner speech question. Each participant undertook the puzzle task in both control (baseline) and experimental conditions (with articulatory suppression). The sequence of which puzzle task they had to complete first was decided based on the participant’s subject ID assigned by the researcher. Participants with odd Subject ID numbers were assigned the control puzzle task first, while participants with even Subject ID numbers were assigned the experimental puzzle task first. Before starting the experimental puzzle task, the researcher spent a few minutes helping the participants practice performing the articulatory suppression by saying the word ‘aluminium’ repeatedly at 90 BPM using an online metronome. Throughout the experimental task, if the participants mispronounced the word too obviously or consistently missed or skipped a beat, the researcher aided them by correcting their pronunciation or assisting them to meet the 90 BPM until they matched the rhythm again.\r\n\r\nDuring data collection, the researcher offered participants an opportunity for a break between puzzles if they began to get tired to prevent their answers from being expedited. The participants were also allowed to ask any questions while they were completing the questionnaire to clarify their understanding of the items presented. At the end of each data collection session, the researcher thanked the participants for their participation and answered any questions that they had. The researcher also explained that participants would be emailed a participant debrief sheet and could request a summary of the study’s findings once data analyses had been completed. For participants who were eligible for reimbursement of travel expenses, they were asked to fill out a participant payment form as a receipt of confirmation that they had been paid.\r\n\r\nThree different models of analysis were carried out in the study. To measure the first prediction, a linear model was used by entering the positive inner speech and evaluative inner speech scores from the VISQ as the predictors and the grit score from the short grit scale as the output. For the second prediction, a linear model was used with the outcome set at the participant’s decision to quit or not to quit and the predictor set as the interaction between different experimental conditions and grit. To measure the third prediction, a generalised linear mixed-effect model was explored by entering the interaction of different experimental conditions and dimensions of inner speech (evaluative inner speech and positive inner speech) recorded from the participant’s retrospective experience as the predictor and participant’s decision to quit the task as the outcome. In this model, a random effect of differences between the conditions (baseline and with articulatory suppression) in slope and participants in the intercept were also included."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3638"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3639"},["text","The data format is csv."]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3640"},["text","Adam2023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3641"},["text","Huzaifah Adam"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3642"},["text","Open"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3643"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3644"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3645"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3646"},["text","Dr. Bo Yao"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3647"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3648"},["text","Developmental"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3649"},["text","56 Participants"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3650"},["text","Linear Model, Qualitative"]]]]]]]],["item",{"itemId":"179","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"188"},["src","https://johnntowse.com/LUSTRE/files/original/6d20e4d1e492485766537bc65023ff1d.csv"],["authentication","fc464b4956692e558cf73d0dac2825c0"]],["file",{"fileId":"189"},["src","https://johnntowse.com/LUSTRE/files/original/92a8d43f4a29aa993e26ae5ebccfccab.csv"],["authentication","d60890e730eb0bbec9a7f8bdc0eda7d3"]],["file",{"fileId":"192"},["src","https://johnntowse.com/LUSTRE/files/original/b4e318b0ff40205dddb2e27a77319608.pdf"],["authentication","15ac31078692a6a822b1e06dfab1c670"]],["file",{"fileId":"193"},["src","https://johnntowse.com/LUSTRE/files/original/cbbeddddc81f7b965d506800abffce2f.pdf"],["authentication","4a371fd6b1e3934f109efa94739a594c"]],["file",{"fileId":"194"},["src","https://johnntowse.com/LUSTRE/files/original/590ec6b7290dc81518e7712aadc3652b.pdf"],["authentication","c7a6bf799aa2440a9ea4f1493f2201f9"]],["file",{"fileId":"200"},["src","https://johnntowse.com/LUSTRE/files/original/2ff293badc8b69d085fc0772f35ed5dd.pdf"],["authentication","68da4579ceea5dc91732edef31c61a16"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3612"},["text","Han-Yi Wang"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3613"},["text","03/Sep/2023"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3614"},["text","Inner speech is a cognitive function related to language processes. Based on its functions reflecting information processing and memorising, it may link to the purchasing process, which includes searching and evaluating product information. Inner speech may also help people think and imagine using the product in the future during their purchasing process.\r\nThis study discussed and investigated the role of inner speech in the purchasing process and how it might affect the decision-making time. This study also mentioned how inner speech may be identified and suppressed. Participants’ data was collected through experiments and several questionnaires. The findings indicated that inner speech might help people in Information Search and Alternative evaluation and affect decision time. The findings also suggested what people may consider and how they use inner speech. \r\nBy uncovering the potential relationship between the purchasing process and inner speech, this research provided valuable information for marketing and psychology research fields. It gave companies some suggestions for practical use, reflecting how people may use inner speech during the purchasing process."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3615"},["text","Inner speech, memory, decision-making, purchasing behaviour."]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3616"},["text","This study was approved by ethics committees at Lancaster University. There were no ethical issues for researchers managing the personal information. The participants’ information remained anonymous and were assigned subject ID (P01, P02, P03…, P30 in Experiment 1 and PCT01, PCT02, PCT03…, PCT30 in Experiment 2). All data were stored anonymously with no identifiable information. \r\nParticipants were given the Participant Information Sheet (PIS) before participating in the experiments. On the day of testing, they asked any questions they might have, then consented to attend the experiment in person or via online platforms like Microsoft Teams, Zoom, or Google Meet to ensure that the suppression was active when needed. The experiment took approximately 30 minutes, including answering all questionnaires. The experiment was held in the participant’s home or a place where no one spoke so that the participant would not be disturbed by any chance.\r\nExperiment 1\r\nParticipants\r\nG*power suggested 52 participants within groups using t-tests and multiple mixed linear regression models, with a .4 effect size and .05 (5%) a-error probability in 80% power (1-b error of probability) (Brysbaert, 2019). Thirty participants were recruited in this experiment with no record or history of neurophysiological disorders, such as dyslexia or aphasia, to ensure that no conditions influence the result and affect the participant to complete the tasks in the experiment. The recruitment process included in-person invitations around campus and social media messages to reach diverse participants.\r\nAlthough only 30 participants were recruited in this experiment, the results of the t-tests suggest that the effect size (see Experiment 1 result section) may be enough for testing the hypothesis.\r\nDesign\r\nThis study was an experimental within-subjects design. Participants simulated purchase experience in the suppression task and the control task without interference assigned to them. The independent variables were self-rating agreements on information search and alternative evaluation and participants’ average decision time in the suppression and control tasks. The dependent variables were inner speech frequency in five dimensions measured by the Inner Speech Frequency Questionnaire (VISQ). \r\nQuantitative data were analysed using R to conduct t-tests, GLMM and CLMM. Secondly, qualitative data were collected through questionnaires and categorised into different variables to identify why participants made the decisions and their inner speech content during the purchasing process.\r\nOverall, the experiment aims to investigate how people use inner speech during purchasing and whether Articulate Suppression task and task without interference influenced decision time and agreement score on information search and alternative evaluation.\r\nMaterials\r\nStimuli\r\nParticipants viewed six product sets (stimuli), which information was copied from the official website. To prevent participants from focusing on the effect of the products’ brands and prices (Albari & Safitri, 2020), the products in each set were the same brand with similar or the same price, unisex, and recognisable, although these products might not exist or remain the latest information on the market.\r\nTwo-item Statement Questions (see Appendix B)\r\n\tParticipants rated the two statements on a seven-point Likert score from strongly disagree to strongly agree (Maity & Dass, 2014) to identify the Information Search and Alternative evaluation agreement level between tasks. Then, participants were asked: “Which product did you choose? Why?” after each purchasing decision.\r\nVariety of Inner Speech Frequency Questionnaire (VISQ, see Appendix C)\r\nThe Inner Speech Frequency Questionnaire (Alderson-Day et al., 2018) included twenty questions asking participants to generally rate their inner speech frequency after the mock e-commerce purchasing tasks with a 7-point Likert scale ranging from \"Never\" to \"All the time\". Questions 7 and 15 were reversely coded; the value should be reversely calculated when doing analysis.\r\nExperiment 1 Qualitative Questions (ExpQ1, see Appendix D)\r\nAfter participants finished all the tasks (six decisions), they were asked to answer three questions at the end of the experiment. These questions gathered qualitative data about the participants’ experiences during the mock e-commerce purchasing tasks and what they had in mind. \r\nProcedure\r\nFigure 2 illustrates the diagram of Experiment 1. Participants were invited and consented to join the research to do Suppression and Control (without interference) tasks. \r\nEach task contained three product sets; participants were asked to imagine and choose a product for themselves or a friend according to the provided information on the mock e-commerce channel (Maity & Dass, 2014). The screen of the researcher or participants presented the information, including the price and details of the product set. Since these two tasks are counterbalanced and randomly ordered, participants repeated the decision-making process three times in the control task and the other three in the suppression task. After each decision, participants answered the two-statement questionnaire and explained which products they chose and why they chose them. According to different tasks, they started with the control task by themselves. However, they were asked to practise counting out loud from 1 to 4 following 160 bpm metronome sounds until the researcher ensured they remained suppressed before starting the suppression task.\r\nThen, they answered VISQ, which measured their inner speech frequency and qualitative questionnaires (ExpQ1) to understand how they used inner speech when viewing the products in the last part of the study. \r\nAnalysis\r\nR was used to analyse the quantitative data to identify the task differences via t-tests and the relationship between variables in two tasks via Generalised Linear Mixed Effect Models (GLMM) and Cumulative Link Mixed Model (CLMM). When conducting the GLMM with family gamma, the quantitative data will follow the standard procedure of data trimming and keep the trimmed data within 5% or 2.5 standard deviations (Berger & Kiefer, 2021). \r\nThe qualitative coding scheme (See Appendix F) was created to identify what participants considered and what they said to themselves using inner speech during the experiment. The coding process involved re-reading the data to identify and assign relevant contexts to the appropriate categories. For example, if participants mention that they have used the product before, the value of the variable “Memory” increases by one unit. These variables were then calculated to identify what factors influenced participants’ purchasing decision-making more. Following the same coding scheme, what kind of inner speech was used when viewing the products could also be found. For example, people may ask themselves questions or repeat the product in mind.\r\nIn summary, Quantitative and qualitative data were analysed to report the results for different purposes and test the hypothesis in this research.\r\n \r\nFigure 2\r\nThe Diagram of Experiment 1 Procedure\r\n \r\nNote: Participants were required to do suppression and control tasks, the order was randomised and counterbalanced. The products presented during the tasks were also randomised.\r\n\r\nExperiment Optimising\r\nThe task without interference in Experiment 1 may not be a reasonable control task since it might include the secretary task effect, as participants were asked to do both tasks and be influenced after they did the suppression task when they were doing the control task. \r\nAs a secretary task, the finger-tapping task, which has been used in inner speech experiments, could be the better control task in Experiment 2 (Emerson & Miyake, 2003; Wallace et al., 2009). Although Finger-tapping might influence working memory’s function and influence people to memorise (Armson et al., 2019; Kane & Engle, 2000; Moscovitch, 1994; Rose et al., 2009), Rogalsky et al. (2008) also mentioned that the performance of people’s understanding of complex sentences might decrease but not as much as suppression occur. \r\nTherefore, doing the second experiment was motivated to replicate the results with a better control condition involving Finger-tapping.\r\nExperiment 2 \r\nParticipants\r\nBased on the findings of Experiment 1, another 30 participants were recruited with the duplicate requirements as the first experiment. The recruitment requirement and process were the same as in the previous experiment.\r\nDesign\r\nThe independent variables were similar to Experiment 1, while the only difference was that the control task here had been changed into the Finger-tapping task. The goal of the whole design is to replicate the results of Experiment 1 to investigate the role of inner speech in the purchasing process.\r\nMaterials\r\nExperiment 2 applied the same materials used in Experiment 1. The only difference was the qualitative questions after tasks. In Experiment 1, participants answered “Experiment 1 Qualitative Questions” at the end of the experiment. However, to better understand the difference between tasks, they were asked to answer a similar questionnaire (see below) after each task to discover the inner speech used in the two tasks.\r\nExperiment 2 Qualitative Questions (ExpQ2, see Appendix E)\r\nParticipants were asked to answer three questions about their experiences during the mock e-commerce purchasing tasks and what they had in mind for the Suppression and Finger-tapping tasks separately.  \r\nProcedure\r\nThe procedure was the same as the first experiment, except for adjusting the control task and the order of the qualitative questionnaire (ExpQ2). Figure 3 illustrates that participants were invited to the experiment using the same stimuli, similar questionnaires, and the same method of presenting stimuli (participants joined in person or via online platforms) with Suppression and Finger-tapping tasks. Participants were asked to practice counting 1,2,3,4 out loud or tapping their index, middle, ring, and little fingers in order (see which task came first) following metronome beats at 160 bpm before the researchers decided to move on. They were asked to view the product set by imagining choosing one for a friend or themselves three times in each task. Participants answered two statements and answered what product was chosen and why after each decision they made. Then, they were asked to answer three Qualitative questions (Appendix E) after each task. They repeated another task in the same process afterwards with a 2-minute break between tasks. After they finished the Finger-tapping and Suppression tasks, they answered VISQ questions at the end of the experiment.\r\nAnalysis\r\nR was also used to analyse the quantitative data for the same purposes and followed the same data-trimming procedure if needed. The same coding scheme was followed to generate the result that could replicate and optimise the clarity of the Experiment 1 results. Overall, the second experiment is to generate the same or more evident results as Experiment 1 and to find more valuable information for the different inner speech used between tasks.\r\nIn conclusion, these two experiments and the analysis might give this research a deeper understanding of inner speech and its role and provide more precise information on how inner speech may related to the purchasing process.\r\nFigure 3\r\nThe Diagram of Experiment 2 Procedure\r\n \r\nNote: Participants were required to do suppression and control tasks, the order was randomised and counterbalanced. The products presented during the tasks were also randomised.\r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3617"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3618"},["text","The data format is csv."]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3619"},["text","Wang03092023"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3620"},["text","Han-Yi Wang"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3621"},["text","Open (unless stated otherwise)"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3622"},["text","None (unless stated otherwise)"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3623"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3624"},["text","Data or text"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3625"},["text","LA1 4YF"]]]],["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3626"},["text","Inner Speech and Its Role in Purchasing Decision-Making Process: Analysis of Within-Subjects Experiment and Questionnaires"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3627"},["text","Dr. Bo, Yao"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3628"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3629"},["text","Cognitive\r\nCognitive - developmental\r\nMarketing"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3630"},["text","60 participants, 30 in the Experiment 1 and 30 in the Experiment 2."]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3631"},["text","Quantitative- t-tests, GLMM, CLMM \r\nQualitative-Thematic"]]]]]]]],["item",{"itemId":"176","public":"1","featured":"0"},["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3557"},["text","An investigation of the influence of individual differences on susceptibility to product placement"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3558"},["text","Ellen Dimeck"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3559"},["text","14/09/2022"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3560"},["text","Product placement increased in popularity in 1982 when Reese’s Pieces Chocolate was included in E.T. the film, which led to a 65% increase in sales. Still to this day product placement is omnipresent within our cultural climate and research has supported that it enhances our purchase intentions. However, what remains unknown is how individual differences may influence product placement susceptibility. To address this gap, the current study investigated whether individual differences in cognitive capabilities, inhibitory control, age, familiarity, gender and timepoint enhance/reduce the likelihood of individuals' purchasing intentions being influenced by product placement. To do this, 55 participants (23 younger adults (Mage = 61.62(8.70)) and 22 older adults (Mage = 21.75(0.68)) were presented with images of four cups of coffee and asked to rank their purchase intentions/familiarity with the products. Following this, participants watched three scenes from Coronation Street, with the second clip including a product placement (Costa Coffee). Approximately 48 hours later, participants completed another purchase intentions questionnaire on the same four cups of coffee. The results highlighted that purchase intentions increased immediately post-clip; however they decreased 48 hours post-clip. Therefore, advertisers may use this information to discover ways in which the consumer can easily purchase the product immediately post-clip e.g. through QR codes. In regard to all other variables, no other significant relationships were found. Thus, it cannot be suggested to advertising agencies that product placement targeted to individuals who fulfil a given criteria (e.g. older adults, etc) will achieve optimal results when compared to non-targeted product placement."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3561"},["text","Marketing, Product placement, Individual differences, Cognitive capabilities, Inhibitory control, Age, Familiarity, Gender, Purchase intentions."]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3562"},["text","Method Design The present quantitative study adopted a repeated measures design. There were several predictor variables: overall cognitive capabilities (including executive functioning; as assessed by the ACE-III; Hsieh et al., 2013), inhibitory control (as assessed by the Stroop effect), age, familiarity, gender, and timepoint. The dependent variable was susceptibility to product placement as measured by change in purchase intention. Participants At the time of the current studies design no published studies had investigated the influence of individual differences on product placement susceptibility, therefore the required sample size was modelled on the most comparable study the authors could source. Specifically, Hoek et al. (2022) investigated the influence of inhibitory control on advertising literacy activation and advertising susceptibility. Hoek et al. (2022) recruited 57 participants. Given the time restraints of data collection, the authors elected to recruit 55 participants. A total of 55 participants volunteered to participate in part one of the study. All participants were recruited via opportunity sampling through word of mouth and through advertisements placed on various Lancaster University Facebook pages (e.g. the Perception and Action Lab group). Participants were either aged between 18-25 (younger adults) or aged 50 and over (older adults). Out of the 55 participants, there were 27 younger adults (19 women; Mage = 60.93; SDage = 8.26) and 28 older adults (18 women; Mage = 21.78; SDage = 0.85). No participant had a known diagnosis of a psychiatric, neurological, or visual impairment, thus psychiatric, neurological, and visual impairments were not included in the analysis. All participants were White British/Irish. Therefore, there was no variation between ethnicities, thus ethnicity was not included in the analysis either. Given that cognitive capabilities was a key predictor variable within this study, it was necessary to ensure that participants with a known cognitive impairment or probable indication of cognitive impairment were removed from the study. Subsequently, all participants were screened for the probable presence of mild cognitive impairment through the Addenbrookes Cognitive Examination (ACE-III; Hsieh et al., 2013). After applying the pre-validated cut off point, 10 participants were excluded. Therefore, 45 participants were included in the analysis. Participants were either aged between 18-25 (younger adults) or aged 50 and over (older adults). Out of the 45 participants, there were 23 younger adults (16 women; Mage = 61.62; SDage = 8.70) and 22 older adults (17 women; Mage = 21.75; SDage = 0.68). Materials Inhibitory Control Inhibitory control was measured through an online Stroop task developed and run through Psytoolkit (Stoet, 2010, 2017). Completion of this task required participants to ignore the meaning of the colour word and indicate the print colour. Participants were generally presented with a colour word and a print word that were incongruent to one another. Thus, participants needed to use their ability to inhibit a pertinent response (i.e. the print colour) and indicate the print colour, which would be done more efficiently by competent readers (von Hippel & Gonsalkorale, 2005). Previous scholars have chosen to use the Stroop task, as it offers a good measure of individual variation in inhibition (e.g., Long & Prat, 2002). As this study was conducted remotely, via Microsoft Teams share screen function, participants were asked to verbally indicate the print colour and the researcher pressed the related keys (e.g. r for red, g for green, b for blue, and y for yellow). Participants first completed four practice trials followed by 40 test trials. Cognitive Functioning Cognitive capabilities were measured using an adaptation of the Addenbrooke’s Cognitive Examination (ACE-III; Hsieh et al., 2013). The original version assesses the participants’ attention, memory, fluency, language, and visuospatial abilities and has a combined score of 100. Although the adapted version examines the same five cognitive domains, it has a combined score of 77, the reason being that some questions were removed, as they were not deemed suitable for an online study – the first two questions on attention, the first two questions on language, and the first three questions on visuospatial abilities. The original version's pre-validated cut off point was 88 (88%) and therefore the adapted version's was 68 (88.31%). The participants who scored below the pre-validated cut off point were removed prior to analysis to ensure that the presence of cognitive impairment would not confound the subsequent analysis. Demographic and Health Characteristics Demographic information, including age, ethnicity, and gender, and background health information, including whether the participant had a current or history of a diagnosis of any cognitive, neurological, visual, or psychiatric impairments, was collected through an online Qualtrics Questionnaire. Purchase Intentions Questionnaire Prior to the questionnaire, participants were presented with the name and an image of each of the four cups of coffee. Purchase intentions of the four cups of coffee were then measured using a 7-point Likert scale. Participants were asked to rate on a scale of 1-7, 1 being ‘Extremely unlikely’ to 7 being ‘Extremely likely’, how likely they were to purchase a cup of coffee from: Caffè Nero, Costa Coffee, Greggs, and Starbucks. Comparably, familiarity was also measured using a 7-point Likert scale. Participants were asked to rate on a scale of 1-7, 1 being ‘Extremely unfamiliar’ to 7 being ‘Extremely familiar’ with how familiar they were with each cup of coffee from Caffè Nero, Costa Coffee, Greggs, and Starbucks. Purchase intentions and familiarity were measured using a 7-point Likert scale, rather than the commonly used 5-point Likert scale, as the inclusion of several options enhances the likelihood of acquiring a more accurate response (Joshi et al., 2015). It was important that purchase intention and familiarity of Costa Coffee was assessed alongside alternative brands, so that it was not made apparent that the study was focusing upon the participants' purchase intention ranking of Costa Coffee only. Therefore, Caffè Nero, Greggs, and Starbucks were chosen alongside Costa Coffee, because according to a survey conducted by Lock (2022), they are the UK’s top four leading coffee shop chains. The images were provided by Adobe Stock (2019) and Dreams Time (2019a, 2019b, 2019c). Product Placement Video The British TV Soap Coronation Street was selected, as prior research (e.g. Armstrong, 2018) suggests that it is popular amongst both younger and older adults (YouGov, 2011). The first clip chosen was a scene from 8th January 2018 Part 1, lasting 1 minute 16 seconds. The second clip chosen was a scene from 29th January 2018 Part 1, lasting 1 minute 15 seconds. The third clip chosen was a scene from 7th February 2018 Part 2, lasting 1 minute 23 seconds. It was the second scene shown that included the product placement (Costa Coffee). The researcher screen recorded each clip from https://www.dailymotion.com/gb and saved them into an encrypted file on a password-protected computer. Procedure A member of the psychology department research ethics committee approved the study before it was undertaken. Participants were invited to attend a 40–50-minute online Microsoft Teams meeting on a set date and time agreed on by the participant and the researcher. To commence, the researcher shared their screen and aided the participant in reading the participant information sheet and consent form via an online Qualtrics Questionnaire. At this time, participants were informed of their right to withdraw up to 2 weeks after participating without giving any reason and they were told their personal information would remain confidential and would be stored in encrypted files (that only myself and my supervisor have access to) on password-protected computers. The participants were only able to progress into the study on attainment of verbal consent. Participants were then asked to disclose various demographic characteristics (e.g., age and gender) and details relating to their current health status (e.g., any cognitive or visual impairments). The participants were then presented with four images of cups of coffee from Caffè Nero, Costa Coffee, Greggs, and Starbucks. They were then asked to rank their purchase intentions and familiarity, on a seven-point Likert scale, with these products via an online Qualtrics Questionnaire. Following this, participants were asked to watch three short scenes from Coronation Street. The second clip shown included a product placement of Costa Coffee. The researcher then implemented an online Stroop task using Psytoolkit (Stoet, 2010, 2017). The participants were also screened for the presence of mild cognitive impairments through the ACE. After this, the participants were presented with the same four images and asked to rank their purchase intentions of these products via the online Qualtrics Questionnaire (see Figure 1). Approximately 48 hours after completing the first part of the study, participants were sent an email invitation to complete another online Qualtrics Questionnaire. Participants were first asked to provide their participation number, which could be found in the email. They were then shown the same four images of cups of coffee and asked to rank their purchase intentions. Finally, the participants were provided with a debrief form at the end of the online Qualtrics Questionnaire (see Figure 2). This debrief disclosed the small degree of deception involved. Specifically, it was explained that participants were not informed at the start that the study considered product placement, as this might have influenced the subsequent data. Participants were reminded that they had the right to withdraw up to 2 weeks after participating and were provided with contact details in case they had any questions. The participants' purchase intentions of the four cups of coffee were measured three times throughout the course of the two studies: pre-clip, immediately post-clip, and 48 hours post-clip. This was to see whether the participants' purchase intentions for the four cups of coffee, specifically Costa Coffee, had increased or decreased following the product placement clip and whether their ranking would withstand the test of time (48 hours post-clip). This is why the participant were asked to include their participant number in part two, so that the participants prolonged purchase intention (48 hours post-clip) could be traced back to their earlier purchase intention rankings (pre-clip and immediately post-clip). Figure 1. A flowchart of part one tasks. Figure 2. A flowchart of part two tasks. Data Processing Inhibitory Control Participants raw Stroop data were downloaded from Psytoolkit into a Microsoft Excel file and saved in an encrypted files on a password-protected computer. From this raw data Stroop effect (the average incompatible conditions response time (ms) - compatible conditions response time (ms)) and percentage error rate (which involved adding the total of incorrect and timed out responses and dividing it by 40 (number of trials)) were calculated. Stroop effect and percentage error rate were used as an indicator of the participants inhibitory control capabilities. Specifically, a high Stroop effect would suggest less difficulty in inhibiting interference and a higher error rate would suggest reduced inhibitory capabilities. Cognitive Functioning The scores of the ACE-III were added and entered into the Microsoft Excel file, which was saved in an encrypted files on a password-protected computer. A higher score was indicative of superior cognitive functioning. Demographic and Health Characteristics To ensure all demographic and health data was readable by R-Studio all variables were dummy coded using numerical values. So, for instance, to determine the participants' gender, they were asked ‘What gender do you identify’ and given the option to choose from one of several responses. Each response was allocated a number, for example, 1 = Man, 2 = Woman, etc, and this was entered into the Microsoft Excel document. Susceptibility to product Placement (change in Purchase Intentions) To investigate the susceptibility to product placement, two difference in purchasing behaviour score were calculated (one for short duration, one for prolonged duration). To calculate these values, the likelihood of purchasing the product value prior to watching the clip was subtracted from likelihood of purchasing the product value after watching the clip (either immediately post-clip or 48 hours after). A positive difference meant that purchase intentions had increased following placement clip. A negative difference meant that purchase intentions had decreased following placement clip. A difference of zero meant that the placement clip had failed to alter purchase intentions Familiarity The familiarity ratings of Costa Coffee were entered into the Microsoft Excel file, which was saved in an encrypted files on a password-protected computer. The higher the score, the more familiar the participant was with the product. Data Analysis To analyse the data, a linear mixed effects model was chosen. The reason being that the current study employs a repeated measures design, and a linear mixed effects model permits an analysis of hierarchically structured data (Baayen et al., 2008)."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3563"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3564"},["text","Data/RStudio.csv"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3565"},["text","Dimeck2022"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3566"},["text","Reece Graham"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3567"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3568"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3569"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3570"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3571"},["text","Lancaster "]]]]]]]],["item",{"itemId":"169","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"173"},["src","https://johnntowse.com/LUSTRE/files/original/707a658c88746eaa6d6045229abd375f.pdf"],["authentication","6efc0d8f0255892001bb9445f96155e7"]],["file",{"fileId":"174"},["src","https://johnntowse.com/LUSTRE/files/original/eda9526471de877c350991c0c8730d08.pdf"],["authentication","af9dc5ca50327bbfad5bf04de9777f11"]],["file",{"fileId":"175"},["src","https://johnntowse.com/LUSTRE/files/original/b1a36360dbc1fcdc7c691b80bc389aa8.pdf"],["authentication","6efc0d8f0255892001bb9445f96155e7"]]],["collection",{"collectionId":"5"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"185"},["text","Questionnaire-based study"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"186"},["text","An analysis of self-report data from the administration of questionnaires(s)"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3428"},["text","An investigation of the influence of individual differences on susceptibility to product placement. "]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"3429"},["text","Ellen Dimeck"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3430"},["text","2022"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3431"},["text","Product placement increased in popularity in 1982 when Reese’s Pieces Chocolate was included in E.T. the film, which led to a 65% increase in sales. Still to this day product placement is omnipresent within our cultural climate and research has supported that it enhances our purchase intentions. However, what remains unknown is how individual differences may influence product placement susceptibility. To address this gap, the current study investigated whether individual differences in cognitive capabilities, inhibitory control, age, familiarity, gender and timepoint enhance/reduce the likelihood of individuals' purchasing intentions being influenced by product placement. To do this, 55 participants (23 younger adults (Mage = 61.62(8.70)) and 22 older adults (Mage = 21.75(0.68)) were presented with images of four cups of coffee and asked to rank their purchase intentions/familiarity with the products. Following this, participants watched three scenes from Coronation Street, with the second clip including a product placement (Costa Coffee). Approximately 48 hours later, participants completed another purchase intentions questionnaire on the same four cups of coffee. The results highlighted that purchase intentions increased immediately post-clip; however they decreased 48 hours post-clip. Therefore, advertisers may use this information to discover ways in which the consumer can easily purchase the product immediately post-clip e.g. through QR codes. In regard to all other variables, no other significant relationships were found. Thus, it cannot be suggested to advertising agencies that product placement targeted to individuals who fulfil a given criteria (e.g. older adults, etc) will achieve optimal results when compared to non-targeted product placement."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3432"},["text","Product placement, individual differences, cognitive capabilities, inhibitory control, age, familiarity, gender, timepoint, purchase intentions"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"3433"},["text","Design \r\n\r\nThe present quantitative study adopted a repeated measures design. There were several predictor variables: overall cognitive capabilities (including executive functioning; as assessed by the ACE-III; Hsieh et al., 2013), inhibitory control (as assessed by the Stroop effect), age, familiarity, gender, and timepoint. The dependent variable was susceptibility to product placement as measured by change in purchase intention.\r\n\r\nParticipants\r\n\r\nAt the time of the current studies design no published studies had investigated the influence of individual differences on product placement susceptibility, therefore the required sample size was modelled on the most comparable study the authors could source. Specifically, Hoek et al. (2022) investigated the influence of inhibitory control on advertising literacy activation and advertising susceptibility. Hoek et al. (2022) recruited 57 participants. Given the time restraints of data collection, the authors elected to recruit 55 participants.\r\n\r\nA total of 55 participants volunteered to participate in part one of the study. All participants were recruited via opportunity sampling through word of mouth and through advertisements placed on various Lancaster University Facebook pages (e.g. the Perception and Action Lab group).\r\n\r\nParticipants were either aged between 18-25 (younger adults) or aged 50 and over (older adults). Out of the 55 participants, there were 27 younger adults (19 women; Mage = 60.93; SDage = 8.26) and 28 older adults (18 women; Mage = 21.78; SDage = 0.85). \r\n\r\nNo participant had a known diagnosis of a psychiatric, neurological, or visual impairment, thus psychiatric, neurological, and visual impairments were not included in the analysis. All participants were White British/Irish. Therefore, there was no variation between ethnicities, thus ethnicity was not included in the analysis either. \r\n\r\nGiven that cognitive capabilities was a key predictor variable within this study, it was necessary to ensure that participants with a known cognitive impairment or probable indication of cognitive impairment were removed from the study. Subsequently, all participants were screened for the probable presence of mild cognitive impairment through the Addenbrookes Cognitive Examination (ACE-III; Hsieh et al., 2013). After applying the pre-validated cut off point, 10 participants were excluded. Therefore, 45 participants were included in the analysis.\r\n\r\nParticipants were either aged between 18-25 (younger adults) or aged 50 and over (older adults). Out of the 45 participants, there were 23 younger adults (16 women; Mage = 61.62; SDage = 8.70) and 22 older adults (17 women; Mage = 21.75; SDage = 0.68).\r\n \r\nMaterials \r\n\r\nInhibitory Control\r\n\r\nInhibitory control was measured through an online Stroop task developed and run through Psytoolkit (Stoet, 2010, 2017). Completion of this task required participants to ignore the meaning of the colour word and indicate the print colour. Participants were generally presented with a colour word and a print word that were incongruent to one another. Thus, participants needed to use their ability to inhibit a pertinent response (i.e. the print colour) and indicate the print colour, which would be done more efficiently by competent readers (von Hippel & Gonsalkorale, 2005). Previous scholars have chosen to use the Stroop task, as it offers a good measure of individual variation in inhibition (e.g., Long & Prat, 2002). \r\n\r\nAs this study was conducted remotely, via Microsoft Teams share screen function, participants were asked to verbally indicate the print colour and the researcher pressed the related keys (e.g. r for red, g for green, b for blue, and y for yellow). Participants first completed four practice trials followed by 40 test trials.\r\n\r\nCognitive Functioning \r\n\r\nCognitive capabilities were measured using an adaptation of the Addenbrooke’s Cognitive Examination (ACE-III; Hsieh et al., 2013). The original version assesses the participants’ attention, memory, fluency, language, and visuospatial abilities and has a combined score of 100. Although the adapted version examines the same five cognitive domains, it has a combined score of 77, the reason being that some questions were removed, as they were not deemed suitable for an online study – the first two questions on attention, the first two questions on language, and the first three questions on visuospatial abilities. The original version's pre-validated cut off point was 88 (88%) and therefore the adapted version's was 68 (88.31%). The participants who scored below the pre-validated cut off point were removed prior to analysis to ensure that the presence of cognitive impairment would not confound the subsequent analysis.\r\n\r\nDemographic and Health Characteristics \r\n\r\nDemographic information, including age, ethnicity, and gender, and background health information, including whether the participant had a current or history of a diagnosis of any cognitive, neurological, visual, or psychiatric impairments, was collected through an online Qualtrics Questionnaire. \r\n\r\nPurchase Intentions Questionnaire\r\n\r\nPrior to the questionnaire, participants were presented with the name and an image of each of the four cups of coffee. Purchase intentions of the four cups of coffee were then measured using a 7-point Likert scale. Participants were asked to rate on a scale of 1-7, 1 being ‘Extremely unlikely’ to 7 being ‘Extremely likely’, how likely they were to purchase a cup of coffee from: Caffè Nero, Costa Coffee, Greggs, and Starbucks. \r\n\r\nComparably, familiarity was also measured using a 7-point Likert scale. Participants were asked to rate on a scale of 1-7, 1 being ‘Extremely unfamiliar’ to 7 being ‘Extremely familiar’ with how familiar they were with each cup of coffee from Caffè Nero, Costa Coffee, Greggs, and Starbucks.\r\n\r\nPurchase intentions and familiarity were measured using a 7-point Likert scale, rather than the commonly used 5-point Likert scale, as the inclusion of several options enhances the likelihood of acquiring a more accurate response (Joshi et al., 2015). \r\n\r\nIt was important that purchase intention and familiarity of Costa Coffee was assessed alongside alternative brands, so that it was not made apparent that the study was focusing upon the participants' purchase intention ranking of Costa Coffee only. Therefore, Caffè Nero, Greggs, and Starbucks were chosen alongside Costa Coffee, because according to a survey conducted by Lock (2022), they are the UK’s top four leading coffee shop chains. The images were provided by Adobe Stock (2019) and Dreams Time (2019a, 2019b, 2019c).\r\n\r\nProduct Placement Video\r\n\r\nThe British TV Soap Coronation Street was selected, as prior research (e.g. Armstrong, 2018) suggests that it is popular amongst both younger and older adults (YouGov, 2011). The first clip chosen was a scene from 8th January 2018 Part 1, lasting 1 minute 16 seconds. The second clip chosen was a scene from 29th January 2018 Part 1, lasting 1 minute 15 seconds. The third clip chosen was a scene from 7th February 2018 Part 2, lasting 1 minute 23 seconds. It was the second scene shown that included the product placement (Costa Coffee). The researcher screen recorded each clip from https://www.dailymotion.com/gb and saved them into an encrypted file on a password-protected computer.\r\n\r\nProcedure\r\n\r\nA member of the psychology department research ethics committee approved the study before it was undertaken. Participants were invited to attend a 40–50-minute online Microsoft Teams meeting on a set date and time agreed on by the participant and the researcher. To commence, the researcher shared their screen and aided the participant in reading the participant information sheet and consent form via an online Qualtrics Questionnaire. At this time, participants were informed of their right to withdraw up to 2 weeks after participating without giving any reason and they were told their personal information would remain confidential and would be stored in encrypted files (that only myself and my supervisor have access to) on password-protected computers. The participants were only able to progress into the study on attainment of verbal consent. \r\n\r\nParticipants were then asked to disclose various demographic characteristics (e.g., age and gender) and details relating to their current health status (e.g., any cognitive or visual impairments). The participants were then presented with four images of cups of coffee from Caffè Nero, Costa Coffee, Greggs, and Starbucks. They were then asked to rank their purchase intentions and familiarity, on a seven-point Likert scale, with these products via an online Qualtrics Questionnaire. Following this, participants were asked to watch three short scenes from Coronation Street. The second clip shown included a product placement of Costa Coffee. The researcher then implemented an online Stroop task using Psytoolkit (Stoet, 2010, 2017). The participants were also screened for the presence of mild cognitive impairments through the ACE. After this, the participants were presented with the same four images and asked to rank their purchase intentions of these products via the online Qualtrics Questionnaire (see Figure 1).  \r\n\r\nApproximately 48 hours after completing the first part of the study, participants were sent an email invitation to complete another online Qualtrics Questionnaire. Participants were first asked to provide their participation number, which could be found in the email. They were then shown the same four images of cups of coffee and asked to rank their purchase intentions. Finally, the participants were provided with a debrief form at the end of the online Qualtrics Questionnaire (see Figure 2). This debrief disclosed the small degree of deception involved. Specifically, it was explained that participants were not informed at the start that the study considered product placement, as this might have influenced the subsequent data. Participants were reminded that they had the right to withdraw up to 2 weeks after participating and were provided with contact details in case they had any questions. \r\n\r\nThe participants' purchase intentions of the four cups of coffee were measured three times throughout the course of the two studies: pre-clip, immediately post-clip, and 48 hours post-clip. This was to see whether the participants' purchase intentions for the four cups of coffee, specifically Costa Coffee, had increased or decreased following the product placement clip and whether their ranking would withstand the test of time (48 hours post-clip). This is why the participant were asked to include their participant number in part two, so that the participants prolonged purchase intention (48 hours post-clip) could be traced back to their earlier purchase intention rankings (pre-clip and immediately post-clip).\r\n\r\nFigure 1. \r\n\r\nA flowchart of part one tasks. \r\n\r\nFigure 2. \r\n\r\nA flowchart of part two tasks. \r\n\r\nData Processing \r\n\r\nInhibitory Control\r\n\r\nParticipants raw Stroop data were downloaded from Psytoolkit into a Microsoft Excel file and saved in an encrypted files on a password-protected computer. From this raw data Stroop effect (the average incompatible conditions response time (ms) - compatible conditions response time (ms)) and percentage error rate (which involved adding the total of incorrect and timed out responses and dividing it by 40 (number of trials)) were calculated. Stroop effect and percentage error rate were used as an indicator of the participants inhibitory control capabilities. Specifically, a high Stroop effect would suggest less difficulty in inhibiting interference and a higher error rate would suggest reduced inhibitory capabilities.  \r\n\r\nCognitive Functioning \r\n\r\nThe scores of the ACE-III were added and entered into the Microsoft Excel file, which was saved in an encrypted files on a password-protected computer. A higher score was indicative of superior cognitive functioning. \r\n\r\nDemographic and Health Characteristics \r\n\r\nTo ensure all demographic and health data was readable by R-Studio all variables were dummy coded using numerical values. So, for instance, to determine the participants' gender, they were asked ‘What gender do you identify’ and given the option to choose from one of several responses. Each response was allocated a number, for example, 1 = Man, 2 = Woman, etc, and this was entered into the Microsoft Excel document.\r\n\r\nSusceptibility to product Placement (change in Purchase Intentions)\r\n\r\nTo investigate the susceptibility to product placement, two difference in purchasing behaviour score were calculated (one for short duration, one for prolonged duration). To calculate these values, the likelihood of purchasing the product value prior to watching the clip was subtracted from likelihood of purchasing the product value after watching the clip (either immediately post-clip or 48 hours after). A positive difference meant that purchase intentions had increased following placement clip. A negative difference meant that purchase intentions had decreased following placement clip. A difference of zero meant that the placement clip had failed to alter purchase intentions\r\n\r\nFamiliarity\r\n \r\nThe familiarity ratings of Costa Coffee were entered into the Microsoft Excel file, which was saved in an encrypted files on a password-protected computer. The higher the score, the more familiar the participant was with the product. \r\n\r\nData Analysis \r\n\r\nTo analyse the data, a linear mixed effects model was chosen. The reason being that the current study employs a repeated measures design, and a linear mixed effects model permits an analysis of hierarchically structured data (Baayen et al., 2008). \r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"3434"},["text","Lancaster University "]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3435"},["text","Data/RStudio.csv"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"3436"},["text","Dimeck2022"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"3437"},["text","Alex Myroshnychenko"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"3438"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"3439"},["text","N/A"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3440"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"3441"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"3442"},["text","LA1 4YF\r\n"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"3443"},["text","Megan Readman"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"3444"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"3445"},["text","Marketing, Cognitive, Capabilities "]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"3446"},["text","55 participants (18 male and 37 females) "]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"3447"},["text","Linear Mixed Effects Modelling "]]]]]]]]]