["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://www.johnntowse.com/LUSTRE/items/browse?collection=12&output=omeka-json","accessDate":"2026-05-01T20:47:18+00:00"},["miscellaneousContainer",["pagination",["pageNumber","1"],["perPage","10"],["totalResults","3"]]],["item",{"itemId":"113","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"98"},["src","https://www.johnntowse.com/LUSTRE/files/original/4e2ce0b482bf0e6255a2b135dd3c4ef9.csv"],["authentication","36a7395fbd0a52c6c6bc196d01f764c9"]],["file",{"fileId":"101"},["src","https://www.johnntowse.com/LUSTRE/files/original/ca80a766cdc965260b9e412e77ce5938.doc"],["authentication","b325147f4b4d0e613dbe5a64177ef440"]]],["collection",{"collectionId":"12"},["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":"1136"},["text","linguistic analysis"]]]]]]]],["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":"2471"},["text","How the correlation of Istagram tweets readability and brand hedoism affect audiecne engagment "]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"2472"},["text","Jiehong Wu"]]]],["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":"2473"},["text","Sep 8th 2021"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2474"},["text","Social media marketing is increasing in importance and more and more brands are embracing social media to increase their brand reach and communicate with their audience. However, there is still little empirical research on how brand message features affect consumer engagement. This study focuses on the impact of readability as an influence on consumer engagement while also noting that the effect of hedonic value of a brand may potentially moderate the level of audience engagement. An experiment based on a sample of 20 of the 100 brands covered by Forbes Media was conducted for this study. In total, a sample of 400 Instagram tweets were collected and analysed for their text readability and audience engagement. Still, the results did not indicate a significant interaction between readability and engagement. A careful analysis of the difficulties and shortcomings encountered in this experiment provides some insights for any subsequent research on the readability of short-form communication by brands."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2475"},["text","Readability, Brand hedonism, readbility formula, audience engagment"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"2476"},["text","Research Question & Hypotheses\r\nThe research question for this study is: can the readability of tweets influence the level of audience engagement?\r\n\r\nAs readability increases the perception associated with processing fluency (Rennekamp, 2012), the ability to process information fluently makes the target message more appealing to the audience, and visual fluency in processing information can also increase people's perception of the processing target (Novemsky et al., 2007). Language that can be processed fluently also enhances consumer perceptions (Lee & Aaker, 2004; Lee & Labroo, 2004). Thus, for most brands with low levels of hedonism, higher tweet readability means higher processing fluency which can reduce audience metacognitive difficulties and thus increase tweet engagement levels.\r\n\r\nAt the same time, for products with high hedonic demand, lower familiarity and uniqueness may provide consumers with greater signals of value, with metacognitive difficulties increasing the appeal of the product by making it appear unique or unusual. More easily processed messages reduce the appeal of the product, possibly because they appear too familiar and therefore less consistent with the perception of uniqueness (Pocheptsova, Labroo, 2004；Pocheptsova, Labroo, and Dhar 2010).\r\n\r\nIt is therefore hypothesised that text features associated with greater readability will be positively associated with consumer engagement with the message. However, given the presence of brand hedonistic features, it can be argued that low readability of messages may increase consumer engagement in brand tweets with higher levels of hedonism instead.\r\n\r\nData collection for the experiment\r\nFrom the above, whether the readability of the tweet text and the level of brand hedonism of the brand to which the tweet belongs combine to influence consumer engagement with the brand's social tweets must be determined.\r\n\r\nInstagram was chosen because it is one of the world's most popular social networks, with around one billion active users per month, and over two-thirds of the Instagram audience is under the age of 34, making the platform particularly attractive to marketers. At the same time, Instagram is an open public platform and information on experiments can be easily accessed by searching for the brand name to use for experiments. This included the number of followers of the brand, the history and content of the tweets, the number of comments and the number of likes. To make the experiment practical, 20 tweets from each of the 20 brands (see Step 1 below) were selected for the experiment. The process of collecting information was as follows.\r\n\r\nStep 1 involved the selection of the experimental subject brands. The results of a hedonistic study of the TOP 100 most valuable brands in the world on the Forbes list (Davis et al., 2019) were used to rank the brands from the highest to lowest level of hedonism using the hedonism index (from Davis et al 2019 survey, for detail see Degree of brand hedonism) as the key indicator. A computer generated a random series of 20 numbers from 1 to 100, and the numbers in this series were used to correspond to the serial numbers of the brands in the hedonism table. The following 20 brands for this experiment were selected: Goldman, Sachs, HSBC, Walmart, Thomson Reuters, IBM, Subway, Verizon, HP, Hyundai USA, Boeing, Chanel, Coach, ESPN, Starbuck coffee, Nike, Gucci, amazon, Mercedes-Benz, Google, Porsche（For the logic behind the selection of these brands, please see Degree of brand hedonism）. \r\n\r\nIn Step 2, text samples and audience engagement data were collected. In order to control the variables of the experiment as much as possible, text samples of tweets were collected from August 12 to August 13, 2021, and only tweets with 30-150 words were selected to control the discrete nature of the sample. To avoid the influence of rich media such as video/audio on audience engagement, tweets in the form of rich media were also excluded from the sample, ensuring that all samples contained only images and textual content. The number of likes and comments on each tweet was also recorded. To ensure that the selected sample of tweets accumulated enough likes and comments, all samples were posted before 7 August, ensuring that they had five days to accumulate interaction data with the audience. According to the official Twitter report (Twitter，2016), due to the instantaneous nature of the social media platform, in general tweets were largely ignored by audiences a week after they were posted and they therefore found it difficult to accumulate further feedback data.\r\n\r\nStep 3 was the readability analysis of the text samples. Considering that some of the tweet samples were less than 100 words, and that The Flesch Reading Ease formula recommends a text count of 100 words or more, and considering the validity of the formula, this experiment combined two or more samples for tweets with a text count of fewer than 100 words to obtain at least 100 words before using the formula for analysis , so as to the average readability score for this group of samples was calculated (See Message readability for details of the Flesch Reading Ease formula)\r\n\r\nVariables and measures\r\nMessage readability \r\nReadability formulas have evolved to the point where there are now over 40 readability formulas (Heydari, 2012). The most widely known of these is Rudolph Flesch's formula, created in 1948 and published in the Journal of Applied Psychology in his article ' A New Readability Yardstick'. This formula is considered to be one of the oldest and most accurate formulas for readability, and has made Flesch an authority on readability scholarship. It was originally created to assess the readability of readers at grade level and is widely regarded as an accurate measure without much scrutiny. The formula is best suited to school texts, but it is also widely used by US government agencies (including the US Department of Defense) to assess the readability of their published documents and forms, and some states even require insurance policies to achieve a Flesch reading-ease score of 45 or higher. The Readability Formula is even installed in Microsoft Office Word, where the program checks the spelling and grammar of a text as well as its readability level (Heydari, 2012).\r\n\r\nThe specific mathematical formula is as follows: \r\nRE = 206.835 – (1.015 x ASL) – (84.6 x ASW)\r\nRE = Readability Ease the output is a number ranging from 0 to 100. The higher the number, the easier the text is to read\r\nASL = Average Sentence Length (i.e., the number of words divided by the number of sentences)\r\nASW = Average number of syllables per word (i.e., the number of syllables divided by the number of words)\r\n\r\n \r\n\r\nTable1: Description and predicted reading grade for Flesch Reading Ease Score (Stein, 1984)\r\nScore\tSchool level (US)\tNotes\r\n100.00–90.00\t5th grade\tVery easy to read. Easily understood by an average 11-year-old student.\r\n90.0–80.0\t6th grade\tEasy to read. Conversational English for consumers.\r\n80.0–70.0\t7th grade\tFairly easy to read.\r\n70.0–60.0\t8th & 9th grade\tPlain English. Easily understood by 13- to 15-year-old students.\r\n60.0–50.0\t10th to 12th grade\tFairly difficult to read.\r\n50.0–30.0\tCollege\tDifficult to read.\r\n30.0–10.0\tCollege graduate\tVery difficult to read. Best understood by university graduates.\r\n10.0–0.0\tProfessional\tExtremely difficult to read. Best understood by university graduates.\r\n\r\nAs can be deduced, the text samples should ideally contain short sentences and words. As most texts on social media are short sentences or words, the Flesch Reading Ease Score was considered to be the most suitable tool for measuring the readability of tweets in this experiment. The Flesch Reading Ease readability formula in the online automatic readability checker was used in this study (https://readabilityformulas.com/free-readability-formula-tests.php).\r\n\r\nConsumer engagement with brands\r\nAs Instagram retweets can only be sent to friends or groups of friends and not to the user's public page, this experiment only measured the number of \"likes\" (users click on the red love button below the tweet or double click on the tweet to like it) and comments on the tweet, as retweet data is difficult to collect. As described in the data collection process, the collected tweets were given at least 5 days to accumulate comments and likes. These two numbers (comments+likes) were then added together and divided by the number of brand trackers and multiplied by 10,000 to obtain the final audience engagement level score.\r\n\r\nDegree of brand hedonism\r\nAs this experiment was limited by resources and practicability, the results of the Davis et al 2019 survey on the level of brand hedonism were used directly here. The following is an introduction to the process of Davis et al.'s 2019 survey on levels of brand hedonism which measured the level of hedonism of 100 brands primarily by human judges on a rating scale (four non-social media active brands were finally excluded, giving a final total of 96 brands).\r\n\r\nIn the Davis et al. experiment, a total of 200 human judges participated in scoring the level of brand hedonism. Each judge was randomly assigned to 10 brands and they scored each brand on four hedonism-related indicators: fun, excitement, thrill and pleasure, on a scale of 1 'not at all' to 7 'very much'. The final brand hedonism index was derived from these four indicators and then averaged across the 10 judges. The judges who participated in the experiment were recruited from the Amazon Mechanical Turk online panel. A total of 200 judges participated in the experiment, 61% of whom were male and the remainder female, all aged 35 years and of unknown ethnic background, but all participants were US residents. Detailed results of the original experiment can be found in Appendix A.\r\n\r\nIn this particular experiment, the brands were ranked from the highest to lowest hedonism level using the hedonism index of Davis et al. A computer generated a random series of 20 numbers from 1-96, and the numbers in this series were used to correspond to the serial numbers of the brands in the hedonism table as the experimental subjects. Table 2 shows the average hedonism scores of the 20 brands selected. Figure 1 shows the conceptual model for this experiment, the relevant experimental variables and the control variables.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nTable 2 the brand hedonism scores\r\n\r\nNO\tBrands\tMean\tSD\r\n1\tPorsche\t6.05 \t1.26 \r\n2\tGoogle\t5.92 \t0.95 \r\n3\tMercedes-Benz\t5.68 \t1.13 \r\n4\tAmazon\r\n5.41 \t1.59 \r\n5\tGucci\t5.29 \t1.13 \r\n6\tNike\t5.05 \t1.40 \r\n7\tStarbucks Coffee\t4.89 \t1.19 \r\n8\tESPN\t4.75 \t1.93 \r\n9\tCoach. Inc\t4.53 \t1.60 \r\n10\tChanel\t4.40 \t1.26 \r\n11\tBoeing\t4.27 \t1.77 \r\n12\tHyundai USA\t4.12 \t1.45 \r\n13\tHP\t3.86 \t1.75 \r\n14\tSubway\t3.75 \t1.67 \r\n15\tVerizon\t3.75 \t1.36 \r\n16\tIBM\t3.45 \t1.47 \r\n17\tWalmart\t3.15 \t1.39 \r\n18\tWalmart\t3.15 \t1.39 \r\n19\tHSBC\t2.89 \t1.35 \r\n20\tGoldman Sachs\t2.14 \t1.23 \r\n\r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"2477"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2478"},["text","Data/Excel.xlsx"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"2479"},["text","Wu 2021"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"2480"},["text","Chloe Keung, Elena Ball"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"2481"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"2482"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2483"},["text","English "]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2484"},["text","Word"]]]],["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":"2485"},["text","LA1 4YZ"]]]]]],["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":"2486"},["text","Robert Davies"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"2487"},["text","MSC"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"2488"},["text","Marketing "]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"2489"},["text","20 tweets from each of the 20 brands\r\n"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"2490"},["text","Regression"]]]]]]]],["item",{"itemId":"38","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"13"},["src","https://www.johnntowse.com/LUSTRE/files/original/ea52745ea1b0fa26c4425be6d7c88489.pdf"],["authentication","9714f2508e84b0bbeedf06ad1c912c48"]],["file",{"fileId":"14"},["src","https://www.johnntowse.com/LUSTRE/files/original/84385ffd21820f91dfe2ad323611b937.pdf"],["authentication","2d5a1ebe0d3968bd48beaa3d1ba2275f"]]],["collection",{"collectionId":"12"},["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":"1136"},["text","linguistic analysis"]]]]]]]],["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":"1195"},["text","What impact does the model statement have on future intentions for liars and truth tellers?"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"1196"},["text","Eleanor Evans"]]]],["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":"1197"},["text","2017"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1198"},["text","Current literature states that there is a marked difference between statements given by truth tellers in comparison to liars. This difference is seemingly determined from when the cognitive load for participants is increased and liars struggle more. There is also evidence from distinctions in the linguistic make-up of the statements. Thirty-six undergraduate participants took part in a study exploring the effect of the model statement on truth tellers compared to liars when discussing a future event. All participants gave their first statement, then listened to the model statement before giving their second statement. Participants also filled out a questionnaire after completing the interview. All interviews were transcribed and analysed using CBCA, WMatrix and ANOVA. Results indicated that while there was a clear effect of the model statement, there was no significant effect of veracity from the CBCA and ANOVA analysis. On the other hand, WMatrix indicated differences in veracity.  In conclusion, both truth tellers and liars were able to increase the amount of information between their first and second statements, thus providing an effect of the model statement. However, there were nevertheless distinct differences between the language used by participants under both conditions; suggesting that there are in fact marked differences between truth tellers and liars when discussing a future event."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1199"},["text","liars\r\nmanipulation\r\nmodel statement\r\ntruth tellers\r\nveracity"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"1200"},["text","The model statement (Appendix A) is a 734-word document and was replicated from Leal et al. (2015). It is known that the model statement had an effect in the Leal et al. (2015) experiment and therefore it seemed appropriate to remain consistent with using the exact same model statement for this study. The interview questions (Appendix B) were also adapted from the Leal et al. (2015) study. \r\nQuestionnaires (Appendix C and Appendix D) were fashioned for each condition. The material for the questionnaires was largely developed for this experiment with the questions specifically tailored to relate to the interview. Both questionnaires contained a total of nine questions, a mix of Likert-scale and open-ended questions. \r\nA digirecorder was used to record all participant interviews. \r\nThis study is a 2 (within factor: the manipulation through using the model statement) x 2 (between factor: the veracity, participants are either in the truth telling condition or the lying condition) mixed effects ANOVA.  The experiment was carried out in two sections, the first being the interview and the second involving a questionnaire. \r\nProcedure\r\nBefore completing the experiment, all participants were provided with an information sheet (Appendix E). Participants met with the researcher so that they could be briefed about their task: to discuss the day in which the participants would go and collect their degree results. In addition, the participants were given a consent form (Appendix F), which they were required to fill out in order to participate. Participants took part in the experiment individually. \r\nOnce consent was given the participants were further briefed about which condition they would be participating in – either the truth telling condition or the lying condition. Participants were randomly assigned to one of the conditions. All participants were told as a baseline for the interview that the normality for collecting their degree results was that they would go to their subject department on the day, show their library card, and be given an envelope with their degree results in. Those participants placed in the Truth Telling condition (N = 16) were told to answer the questions asked by the interviewer as truthfully as possible to the best of their knowledge. Participants in the Lying condition (N = 17) were informed to lie when answering the interviewer’s questions. Participants in the lying condition were told that they could either lie about one element of the intended event or all aspects of it. \r\nA third party member, who was unaware of the participants’ veracity status, carried out the interview; this is how the interview was conduced in the Leal et al. (2015) experiment. During the interview the participants were asked the first question of “OK, just so I can understand, I am going to need you to take me to the day that you will collect your degree results, and tell me in as much detail as possible everything that will happen from before you collect your results through to you receiving your results”. Participants then gave their first statement in response to the question. Preceding this, all participants in both conditions were exposed to an example statement after the interviewer said, “I know that sometimes people are not sure just how much detail to include. In order to give you an idea of what I am looking for I'd like to play you an example of what we consider a detailed answer”. This example statement, known as the model statement was a recording of someone dictating the details of an event that has no relevance to what the participants were asked to talk about during the experiment. Following the model statement, the interviewer asked “OK, I know that wasn’t too relevant to your story but hopefully you have an idea of the amount of detail it takes for us to get a clear rounded idea of how the event will go! Could you now please tell me in as much detail as possible everything that will happen from before you collect your results through to you receiving your results” and the participants proceeded to give their second statement. \r\nOnce the participants had completed the interview section of the experiment, they were given a questionnaire to fill out. The questionnaire was tailored to whichever condition they were allocated – there was a separate questionnaire for the Truth Tellers and the Liars. All participants in the lying condition were asked as part of their questionnaire to state the element(s) they had lied about. \r\nData Analysis\r\nAfter the data collection, the participants’ interviews were transcribed and a primary analysis was performed using Criteria-Based Content Analysis (CBCA). There are a total of 19 possible criteria for analysing statements, however only a certain number were selected for the purpose of this study. The chosen criteria can be seen in table 1. Each statement was given a score corresponding with each individual criterion, leading to an overall CBCA score. Each criterion was scored between 0 and 2: 0, if the criterion is not found in the statement; 1 if it is a present, but only a small amount; and 2, if the criterion was found frequently throughout the statement. Following CBCA coding, the scores were analysed in SPSS using a repeated measures ANOVA, in which the overall scores for each of the statements could be compared. \r\nTable 1\r\nList of criteria used for CBCA coding and the descriptions \r\nGeneral Characteristics\r\n\r\n1.Logical Structure\r\nCoherency of the statement in terms of not containing logical inconsistences or contradictions\r\n2. Unstructured Production\r\nThe presentation of the information in a (non) chronological order\r\n3. Quantity of Details\r\nThe inclusion of specific descriptions of place, time, persons, objects and events\r\nSpecific Contents\r\n\r\n4. Contextual Embedding \r\nEvents being placed in time and location, and actions being connected with other daily activities and/or customs\r\n5. Description of Interactions\r\nInformation that interlinks at least the alleged perpetrator and witness \r\n7. Unexpected Complications During the Incident \r\nElements incorporated in the statement that are somewhat expected\r\n8. Unusual Details\r\nDetails of people, objects or events that are unique, unexpected or surprising but meaningful in the context\r\n9. Superfluous Details\r\nDetails in connection with the allegations that are not essential for the accusation \r\n11. Related External Associations\r\nEvents are reported that are not actually part of the alleged offence but are merely related to the alleged offence\r\n12. Accounts of Subjective Mental State\r\nDevelopment and change in feelings experienced at the time of the incident \r\nMotivated-Related Contents\r\n\r\n14. Spontaneous Corrections\r\nCorrections that are made or information that is added to material previously provided in the statement without having been prompted by the interviewer\r\n15. Admitting Lack of memory\r\nAn unprompted interviewee admitting lack of memory either by saying “I don’t know” or “I don’t remember” \r\nNote. This list of criteria was adapted from Vrij, A. (2005). Criteria-Based Content Analysis: A Qualitative Review of the First 37 Studies. Psychology, Public Policy and Law. 11(1), 3-41\r\n\r\nWMatrix (Rayson, 2008) was used in addition to CBCA and SPSS. All the statements were separated into files and uploaded onto WMatrix so that linguistic analysis could commence. WMatrix is a software program that allows for corpus linguistic analysis and comparison.  It provides frequencies and percentages of how the words are distributed in a given text; it also lists the concordances for reference. It produces tables from the output for each comparison and ranks the words based on their log-likelihood. The log-likelihood is an indicator based on the difference among frequencies, in this instance how often a particular word is used by participants. The word count and interview duration were also calculated and used in the analysis. \r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"1201"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1202"},["text","data/SPSS.sav"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"1203"},["text","Evans2017"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"1204"},["text","John Towse"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"1205"},["text","Open"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1206"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1207"},["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":"1208"},["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":"1209"},["text","Lara Warmelink"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"1210"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"1211"},["text","Cognitive Psychology"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"1212"},["text","Thirty-six participants (males 12 and females 24) were recruited to take part in the study. All participants were Undergraduate Lancaster University Students over the age of 18 (Mage = 20.29, SD = 1.36)"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"1213"},["text","ANOVA"]]]]]]]],["item",{"itemId":"36","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"4"},["src","https://www.johnntowse.com/LUSTRE/files/original/0d6706cce7274f7f666b6fecace2eee7.doc"],["authentication","aaf495cd4b8ada201e0f36bc6cb8f19b"]]],["collection",{"collectionId":"12"},["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":"1136"},["text","linguistic analysis"]]]]]]]],["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":"1118"},["text","The Social Functionality of Language Coordination: Linguistic Alignment in Children with and Without Autism."]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"1119"},["text","Elizabeth Osborn"]]]],["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":"1120"},["text","2013"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1121"},["text","Linguistic alignment between conversationalists is a well documented phenomenon; however, the underlying motivational basis for this tendency remains to be established. This study explored the extent to which language convergence in terms of both lexical choice and syntactic structure is mediated by feelings of affiliation toward an interactional partner. In Experiment 1, children with Autistic Spectrum Disorder (ASD) and typically developing (TD) children completed a ‘Snap!’ game in which they alternated turns to name picture cards with a partner. In actuality, the partner was an experimental confederate who utilised non-preferred lexical choices to name the pictures. Results found that all children aligned their word choices with the lexical selections of the experimenter to an equivalent extent. However, evidence to link this tendency toward liking for an interactional partner could not be substantiated. Experiment 2 sought to further investigate evidence for syntactic convergence in children and employed a replication of the paradigm utilised by Allen et al. (2011). Again, there were no differences between the alignment abilities of children with ASD and the performance TD controls. Taken together, the results of this study add more support for the notion of automated low-level priming as one explanation of convergent functioning. Identified implications of these findings and proposals for future research are discussed. "]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1122"},["text","linguistic alignment\r\nautism"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"1123"},["text","\tLexical Snap Cards\r\nThe experimental materials comprised of 16 paired experimental items and 50 filler picture cards. An initial pool of 55 items which could be named by two different lexical choices was compiled by images provided by Snodgrass and Vanderwart (1980) and experimental items utilised by Branigan et al. (2011). The images were presented in a list alongside two lexical choices to name each picture; one lexical choice was a highly preferred name for the picture which was paired with a second less-preferred but equally appropriate word to name the picture. For example, an image of a mushroom was presented adjacent to the names ‘Mushroom’ and ‘Toadstool’. Ten adult participants were then asked to individually rate the appropriateness of each lexical choice for naming the pictures on a seven point Likert scale, with ‘1’ indicative that the lexical choice was completely inappropriate for naming the picture and ‘7’ indicative that the lexical choice was completely appropriate for naming for the picture. Additionally, using a forced choice paradigm each participant was required to indicate their preferred word choice for naming the picture from the two options provided.  \r\n\tFrom this initial pool, twenty-four items were identified where both lexical choices had acceptability ratings above five and where there appeared a distinct majority preference for one word choice to name the picture (above 80%). Ten typically developing children (mean age: 9.7 years, range 9.2-10.1 years) were then asked to spontaneously provide names for these pictures in the absence of written or verbal prompts in order to further confirm the existence of a distinct lexical preference for each picture in child participants. A final list of 16 experimental items (see Appendix 1) was then selected where over 80% of children spontaneously used the word choices that had been preferentially indicated by adults to name the pictures. The final card set therefore comprised of 82 cards: the 16 paired experimental item picture cards (consisting of an experimenter prime card and subsequent matching participant target card), two sets of six matching filler ‘Snap!’ cards and 38 filler cards which pictured random objects.\r\n\tEach participant received the sixteen experimental items in a different order, split randomly each time between two experimental conditions that were introduced to assess both the presence and strength of language coordination over time and to eliminate the potential of immediate echolalia as an experimental confound in participants with ASD. In accordance with the design utilised by Slocombe et al. (2013), eight of the paired experimental items were split by two filler card interventions between the experimenter’s prime card and the participant’s target card, whilst the other eight paired experimental items were split by four filler card interventions between the prime and target cards (see Figure 1). Cards were also colour coded so that ‘Snap!’ was only possible when both the colour and the pictures on the experimenter’s prime and participant’s target cards matched, in order to avoid distractions in responses to the experimental items. The order of the filler and ‘SNAP!’ cards remained fixed throughout the trials. \r\n          ‘Reading the Mind in the Eyes’ Task\r\nThe first fourteen experimental items from the official ‘Reading the Mind in the Eyes’ test (Baron-Cohen, 2001 – child version) were utilised as measures of emotion recognition and social sensitivity; abilities that have been recurrently taken as indicators of Theory of Mind (ToM) functioning in children. \r\nToM Book\r\nAdditional to the ‘Reading the Mind in the Eyes’ task, children were also given a higher -order ToM assessment in order to obtain a more advanced measure of both social understanding and the abilities of participant’s to make inferences about the mental states of others. The story ‘The School Football Team’ developed by Liddle & Nettle (2006, story number 4) to investigate higher-order ToM functioning was presented pictorially to children in a story-book format and contained two scripted memory questions and a ToM question at the end (see Appendix 2).  \r\nShopping list game\r\nA commercially available child-appropriate board game was selected where it was possible that the experimenter could systematically manipulate the resultant winner of the game. The ‘Shopping List’ game by Orchard Toys is a picture-matching game designed for children with Verbal Mental Ages between three and six years and served as a quick experimental task where the outcomes could be reliably manipulated. \r\n\tLiking Scale\r\n\tIn order to assess the resultant outcomes of the positive and neutral conditions on children’s affiliation to the experimenter, a picture sorting task was employed. Ten photographs that varied in content to include food, animals, people and events (e.g. baked beans, Spiderman and a giraffe) were obtained from an online picture database and constituted filler card items. The experimental item in this task was a head and shoulders photograph of the experimenter. Five line-drawing pictures of faces that varied in degrees of emotion from one (very unhappy face) through to five (very happy face) were then utilised as a pictorial adaptation of a Likert scale that was understandable to child participants. All participants received the pictures in same order, with the experimental item being placed at number eight out of the ten picture cards.\r\nProcedure\r\nEach participant was tested individually in a quiet room, away from distractions. Testing was divided between two sessions that were held approximately twenty-one days apart. During the first session participants completed the BPVS which took approximately ten minutes to administer and required children to select (either verbally or via pointing) a picture from a choice of four that depicted a word spoken by the experimenter. During this session children also completed the two ToM assessment tasks. For the Baron-Cohen ‘Reading the Mind in the Eyes’ task (2001-child version) each participant was firstly shown a practice example sheet depicting a photograph of a pair of eyes with the names of four different emotions surrounding them. Each child was asked to look at the eyes whilst the experimenter read aloud the four names in turn and was then asked to choose the emotion that they thought best described the eyes. After the practice trial had been successfully completed the same procedure was repeated for the fourteen experimental items, taking approximately five minutes in total. \r\nFor the second higher-order ToM task, children were asked if they would like to read a story about two friends, Johnny and Bob. If the participant agreed then the experimenter and the child looked at the picture book together, with the experimenter reading the story aloud to each child. At the end of the story book children were then asked two scripted memory comprehension questions about the story in order to gain an indication of overall attention and comprehension of the story and a third scripted question that assessed higher-order ToM functioning. This task took less than five minutes to administer. \r\nIn the second session participants were asked if they would like to play some more fun games with the experimenter. If the child agreed they were informed that the first game they would be playing was a race to find all of the items on their ‘shopping list’ and that the winner of this game would receive a prize. In this board game task both the experimenter and participant received a ‘shopping list’ and alternated attempts to turn over cards from a pile in the middle of the table in order to correctly identify items on their list. The first person to complete their ‘shopping list’ and identify all of their items was determined the winner, however by removing a card either on the experimenter’s shopping list or on the participant’s shopping list meant that the ‘winner’ of the game could be systematically manipulated. Six children in both the ASD and TD groups were allowed to win, whereas the other six children in each group played and lost. When children ‘won’ the game they received positive verbal reinforcement and praise from the experimenter (e.g. “Wow! You were brilliant at that game! You must be very clever”) and were allowed to choose a sticker as a reward (positive affiliation condition). In contrast, when children ‘lost’ the game the experimenter retained a strictly neutral manner towards the child and continued with the next task (e.g. “okay, shall we play the next game?” neutral affiliation condition).  \r\nImmediately following this game, children were asked to sort some photographs according to how much they liked the things depicted in the pictures. Five images of faces that displayed varying emotional expressions were placed in a line on the table, going from one (a really unhappy face) through to five (a really happy face). Children were then given three examples of picture sorting by the experimenter e.g. “This is a picture of broccoli, I really hate broccoli and so I would give it a number one and put it in this pile”, “This is a picture of a cupcake, I really like cupcakes and so I would give it a number five and put it in this pile” and finally “This is a picture of the Queen, I don’t really like or really dislike the Queen and so I will give her a number three and put her in this pile”. Each child was then asked to sort the ten photographs in turn according to how much they liked the things depicted in the pictures whilst the experimenter busied herself ‘preparing the next task’. \r\nFinally, children were asked if they would like to play a fun game of ‘Snap!’ with the experimenter. If the child agreed then the experimenter explained the rules of the game; that ‘Snap!’ in this game occurred when cards were both the same picture and the same colour and that before deciding if it was ‘Snap!’ each player was firstly required to name the picture depicted on their card. In order to further establish these rules each child was then shown four sets of example picture cards; the first pair of cards had the same picture but were not the same colour (a pink penguin and a blue penguin), the second pair of cards were the same colour but did not have the same picture (a blue bell and a blue tie), the third pair had different colours and different pictures (a green carrot and a blue star) and the final pair had the same colour and the same picture (two green shoes) depicting ‘Snap!’. The child and the experimenter then played with these example cards until it became clear that the child understood the conditions that constituted ‘Snap!’ in this game.\r\nFollowing indication that the participant understood how to play the game, the experimenter and child took turns in taking the top card from their pre-ordered card pile, naming the picture on the card, before placing the card on the table and deciding if it was ‘Snap!’ The experimenter always began the game and utilised pre-scripted non-preferred word choices to name the pictures on the sixteen experimental item prime cards. When both the experimenter’s prime card and participant’s target cards both had the same picture and were the same colour it was ‘Snap!’ and the first person to shout this won the cards. At the end of the game the person who had won the most cards was determined the winner (the experimenter let all children win the game). This task took 5-10 minutes dependent upon the participant’s age and concentration and was digitally recorded for later transcription."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"1124"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1125"},["text","data/SPSS.sav"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"1126"},["text","Osborn2013"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"1127"},["text","John Towse"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"1128"},["text","Open"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1129"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1130"},["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":"1131"},["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":"1132"},["text","Melissa Allen"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"1133"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"1134"},["text","Developmental Psychology"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"1135"},["text","Twelve participants with Autistic Spectrum Disorder (mean chronological age: 9.2 years, range 5.7 to 13.5 years) were recruited from a Special Educational Needs (SEN) school in the North West area of England\r\nParticipants with Autism were then paired with a group of twelve typically developing (TD) children (mean chronological age: 5.3 years, range 3.11 to 7.8 years) recruited from both a mainstream primary school and a pre-school centre in Lancashire. "]]]]]]]]]