Infants' Awareness of Number: Innate Ability or Perceptual Bias?
Addition, subtraction, Number, Object Tracking, object files, Infant perception
In order to identify the origin of our understanding of numerosity and arithmetic abilities, it is essential that such abilities are measured in infants. In Wynn’s (1992) study, a case was made for an innate ability to perform arithmetic operation on small number sets as it was demonstrated that infants would look longer at displays that violated their expectations of number. However, research in the years following this seminal study cast doubt on this interpretation of infants’ behaviour. Other research has suggested that perceptual biases are at play, rather than infants possessing a symbolic understanding of number. To address the contrasting finding in this area of developmental research, this study set out to analyse preexisting data to investigate the factors that influence infants’ abilities to track objects over occlusion and to identify the most appropriate level of interpretation of this ability The present study recruited a sample of 32 infants across two experiments. Adapting the methodology from Wynn (1992), Experiment 1 measured looking time when an object was revealed to be missing from the display, violating infants’ expectation of presence. Experiment 2 measured looking time when an object was revealed to be in the incorrect position on the stage, violating infants’ expectation of position. It was found that infants violation trial had a significant effect on looking time and whether the object missing was the first or last to be placed had a significant effect on looking time in violation of presence conditions
Participants: In this study, participants were 32 infants aged 5- to 7-months, (M = 188.38 days, SD = 10.51, range = 175 – 218). Infants were 15 males and 17 females. 16 participants were used in each experiment. In Experiment 1, participants were 7 males and 9. In Experiment 2, participants were 8 males and 8 females. Participants in each experiment were matched based on age. Apparatus & Stimuli: The experiment took place in a dimly lit test room, with displays presented on a grey stage measuring 64cm wide by 40cm high and 31cm deep. An 8.5cm high black screen located 31.5cm behind the front of the stage was used to occlude the display by being rotated upwards. The display also consisted of a 30cm rotating platform that allowed different configurations of objects to be rotated rapidly. The objects used in this study were two 12.5cm high by 9.5cm wide toy hedgehogs that squeaked when squeezed. These toys were magnetic at the bottom. Procedure: Infants were sat in either a high seat or on a caregiver’s lap, 60cm from the front edge of the stage. In cases where infants were sat on a caregiver’s lap, the caregiver’s eyes were above the stage as to avoid them seeing the display and possibly influencing the infant’s behaviour. After gaze calibration to ensure the accuracy of eye-tracking measures, the procedure closely followed that of Wynn (1992) and Bremner et al (2017). Three pre-test (baseline) trials were presented initially. These resulted in the correct outcome of the operation as well as the two incorrect outcomes in counterbalanced order. The screen was lowered to reveal either one or two toys, depending on the trial, and the observer recorded where the infant looked on the stage. In terms of the location of the toys in trials, when one was presented, it was placed 7.5cm to the right of the stage’s centre. When two toys were presented, the second toy was placed 7.5cm to the left of the stage’s centre. Pre-test trials continued until the infant accumulated at least 2 seconds of looking time and looked away from the display for seconds or more. When this was achieved, the screen was raised and the same procedure was repeated for the displays for the other two outcomes. Test trials were administered in two blocks of four trials. The experimenter’s hand emerged at one side above the screen. The side at which the toy first appears was counterbalanced across participants. The toy squeaked to capture the infant’s attention and continued to squeak to maintain this attention as it was placed on one of the locations used during the correct outcome familiarisation trial. The experimenter then slowly withdrew their hand, clasping and unclasping the hand to show the infant that it was empty, and the screen was then raised to occlude the toy from the infant’s view. The time taken from the appearance of the toy to the withdrawal of the hand took approximately 5 seconds. The experimenter’s hand then reappeared above the screen from the opposite side of the display, holding an identical squeaking toy. Once the infant’s attention had been captures, the toy was placed in the other location used during correct outcome familiarisation trials. The hand was then raised and, again, clasped and unclasped to show the infant the hand was empty. The hand as then slowly withdrawn from the display. The screen was then lowered to reveal either the correct or incorrect outcome. In Experiment 1, conditions involved violation of object presence. In ‘added object absent’ trials, the screen was lowered to reveal the last object to be placed was missing from the display. In ‘original object absent’ trials, the screen was lowered to reveal the first object to be placed, present before the screen was raised, was missing from the display. In Experiment 2, conditions involved violation of object position. In ‘added object in wrong location’ trials, the screen was lowered to reveal the last object to be placed appeared in the centre of the stage rather than on the side of the stage in which it was placed. In ‘original object in wrong location’ trials, the screen was lowered to reveal the original object in the display appeared in the centre of the stage rather than on the side it was in before the screen was raised. These test trials continued until the infant had accumulated at least 2 seconds of looking tie and looked away from the display for 2 seconds or more.
The Impact of Sleep Patterns on Emotion Regulation in Taiwanese Adolescents
sleep patterns, circadian types, morningness-eveningness, on/off-peak time, emotion regulation, cognitive control, adolescents
Emotion regulation has been shown in a number of studies to be related to sleep, which often suggested that good sleep quality leads to better emotion regulation. However, research which has empirically documented the link between individuals’ specific sleep patterns/circadian types and emotion regulation among adolescents is scant. Therefore, the aim of this study attempts to explore whether there is an interaction between circadian types and the corresponding peak time on emotion regulation. Participants were 204 boys and 148 girls, who were from 13 to 16 years of age. The present study involved three questionnaires and two modified emotional Stroop tasks, including Facial-Emotional Stroop task and Lexical-Emotional Stroop task, as the assessment of emotion regulation. The analysis of the questionnaires and experiments was conducted through a series of multivariate ANOVA analyses in order to indicate whether there is a main effect of two independent variables or interactions on two emotion regulation. The results showed three main findings. Firstly, ‘morning people’ committed more error on facial tasks than ‘evening people’. Secondly, participants who attended the tasks in the afternoon had faster reaction times on Lexical task than who were tested in the morning. Thirdly, the interaction between circadian types and the corresponding peak time only showed in the evening group. To sum up, this study might be of importance in explaining the relationship between sleep patterns and emotion regulation in adolescents. Nevertheless, further studies for adolescents in investigating circadian types in relation to emotion regulation are needed.
Materials Each participant was asked to complete three online questionnaires about sleep and mood as well as two experimental tasks about emotion regulation. Three questionnaires had been translated into a Chinese version and inspected by a native Chinese-speaking professor in the Department of Psychology at Lancaster University. Sleep Measures. Circadian Types Questionnaire. Participants were given a Morningness-Eveningness Questionnaire (MEQ) (Horne & Östberg, 1976) to assess when their biological clock can achieve peak alertness, which indicates the better timing for people to behave more efficiently in their work and cognitive, behavioural and emotional functioning (see Appendix A). Three groups were be categorized based on the MEQ score: score > 58 for the morning type, 42 < score < 58 for the Intermediate type and score < 42 for the evening type. Sleep Quality Questionnaire. To assess whether participants have sleep dysfunction, participants were also asked to fill out the Pittsburgh Sleep Quality Index (PSQI) (Buysse, Reynolds, Monk, Berman & Kupfer, 1989), which elicited information concerning their sleep quality (see Appendix B). The higher score the participants gain, the poorer sleep quality they have. This score can be used to examine whether people's sleep quality can influence their emotion regulation ability. Mood Measurements. Emotional Problems Questionnaire. Participants needed to fill out the Depression Anxiety Stress Scale-21 (DASS-21) (Antony, Bieling, Cox, Enns & Swinson, 1998), which is a self-reported measure to record their mood during over one recent week (see Appendix C). There were three dimensions of negative mood in this questionnaire, including depression, anxiety and stress. Each dimension had an independent score, with a higher score indicating more emotional problems. In this study, three sub-scores were added together to produce a composite measure of emotional difficulties. Emotion Regulation. In addition to the questionnaires, participants were requested to complete two modified “Emotional Stroop Tasks”, including the Lexical-Emotional Stroop Task and the Facial-Emotional Stroop Task, as the assessment of their cognitive control in response to emotional stimuli (Isaac, Vrijsen, Eling, van Oostrom, Speckens & Becker, 2012). Lexical-Emotional Stroop Task. The experiment stimuli consisted of three kinds of emotional words, namely positive, negative and neutral words, each of which had five presentative words (see Table 1), and each word was printed in four colours (blue, green, red and yellow). In order to assess the emotion regulation ability, participants were asked to classify the colour by pressing a different button as fast as they can. For example, when participants see a blue or green word they have to press “Q”, whereas when they see a red or yellow word they have to press “P”. Before presenting the stimulus, a fixpoint lasted 200 ms and was followed by the presented stimulus, which lasted 2000ms to make sure that participants had enough time to react. All emotional-colour words were randomly presented during this task. After participants press the key, feedback showed whether the response was correct, which lasted 500 ms (see Figure 1). Before the 30 real trials, there was a clear instruction about this task and then each participant had six trials for practise to ensure that they indeed understood how to operate this task. All stimuli were translated into Chinese and appeared in font DFKai_SB and in font size 96. The projected stimuli came out on the computer screen and colour words appeared against a black background. Facial-Emotional Stroop Task. A total number of stimulus was 160 emotional faces which were composed of 10 different identities (5 males and 5 females) x 4 emotions (happy, neutral, angry and sad) x 4 Stroop colours (blue, green, red and yellow) (see Figure 2). Emotional faces were selected from Taiwan Corpora of Chinese emotions and relevant psychophysiological data (Chen, Zhou & Zeng, 2013). It could reduce the cultural difference effectively when Taiwanese participants took the Facial-Emotional Stroop task. As the same as the execution in the Lexical-Emotional Stroop task, participants were also requested to do colour classification by pressing different buttons as fast as they can. For instance, when participants see a blue or green facial expression, they have to press “A”, whereas when they see a red or yellow emotional face they have to press “L”. Before stimulus appeared, a fixpoint showed and lasted 200 ms, which was then followed by the presented stimulus, which lasted 2000ms, to ensure that participants had enough time to respond the stimuli. The Stroop trials consisted of 30 real trials and were randomized per participant. Participants had six extra trials to practice as well before the real trials. Within the trials, participants saw feedback to tell them whether the response was correct for the last trial, which lasted 500 ms (see Figure 3). All facial stimuli were cropped, free from hair or other external accessories that could prevent any distractions during the task. The projected stimuli showed on the computer screen and the coloured facial expression appeared against a black background. Both Lexical and Facial stimulus presentation and response collection were programmed by using PsyToolkit on the website (Stoet, 2010) (see Appendix D and E) and run on Windows computers. Table 1 Stimuli from the Lexical-Emotional Stroop Task Positive Neutral Negative 快樂 (Happy) 被愛 (Beloved) 滿足 (Satisfaction) 自豪 (Pride) 舒服 (Comfort) 無聊 (Boredom) 平靜 (Calmness) 驚訝 (Surprise) 疑惑 (Confusion) 害羞 (Shyness) 生氣 (Anger) 焦慮 (Anxiety) 厭惡 (Disgust) 恐懼 (Fear) 悲傷 (Sadness) Figure 1. The diagram of Lexical-Emotional Stroop Task. In this example, the stimulus is a word of Blue Happy. Figure 2. Sample happy male stimuli used from the Facial-Emotional Stroop Task. Figure 3. The diagram of Facial-Emotional Stroop Task. In this example, the stimulus is a male’s face of Blue Happy. Procedure This study was approved by the director of the Counselling Department in Mingder High school and combined with the counselling curriculum. All students’ parents were provided with the information sheets (see Appendix F) about this study and an opt-out consent form (see Appendix G) one week prior to it. Only parents who did not want their child to participate in this study needed to sign and return the opt-out consent form. However, no opt-out consent form was returned. Participants were tested in a computer lab, with the researcher and their counselling teacher present. In order to balance the number of classes with the time of test, half of the classes per grade were tested in the morning (8 a.m. to 9 a.m. or 9 a.m. to 10 a.m.), and the others were tested in the afternoon (2 p.m. to 3 p.m. or 3 p.m. to 4 p.m.) (see Table 2). The duration of participation lasted around 45 minutes. Before the beginning of the study, the research topic and aims were presented on each computer screen. Participants were provided an opportunity to ask questions, and then the researcher asked whether anyone was not willing to attend this study. None of the participants were blind as to the aim of this study. Then, participants were given the links to the experiments and questionnaires; they needed to key the links onto the browser and start the study. In order to effectively use their time, participants were requested firstly to complete two Emotional Stroop tasks. Following the experiments, participants were instructed to fill out three questionnaires.
The Impact of Spatial Locations Involving Schema Representations on False Memories
Schema, false memory, source monitoring, distinctiveness heuristic, object-location binding.
While numerous studies have investigated the effects of schema on false memories, few have looked at how schematic framework involving spatial locations have influenced levels of true and false memories in different age groups. For this study, two separate analyses were conducted; both analyses required participants to study four environment scenes, which contained schema- consistent objects that were placed in either schema-expected or schema-unexpected locations and schema- irrelevant objects. After each scene, a distractor task was presented, followed by the test scene. In the first analysis, false memory rates were examined by adding objects, which were not present during study, into test scenes; in the second analysis, false memory rates were assessed by shifting schema-consistent objects from a schema-expected to a schema-unexpected location or vice versa between study and test scene. In both analyses, target objects that remained in the same location for both study and test scenes assessed for true memories. Three different age groups were studied; younger children aged seven and eight, older children aged nine and ten, and adults who were university students. Results revealed that overall, adults were more schema-bound, and had significantly higher levels of true memories as well as significantly lower levels of false memories compared to younger and older children. Furthermore, schema-inconsistent objects attracted lower levels of false memories across all age groups. However, objects that shifted from a schema-unexpected to a schema-expected location yielded high false memories for object-location pairing. This study is of particular significance to the field of forensic psychology.
The experiment was programmed using a computer software called Psyscript and was run on a Mac laptop. Four different environments were used during the experiment, which were a kitchen, a living room, an office and a bathroom. For the practice run, a separate image, which was a seminar room, was used. All the photographs used were standardized across all four environments, with each photograph being 1300 x 864 pixels, to ensure that the quality and clarity of each photograph was the same. To every environment image, three different versions were prepared for the study scene, ensuring that all six of the schema-relevant target objects had the opportunity to appear in a schema-unexpected location, a schema-expected location, or not being present at all. Moreover, to every version, two test scenes were prepared, to create a variation between which of the target objects that were initially placed in schema-relevant or schema-irrelevant locations during study phase would be shifted during the test scene. Figure 1a is an example of a bathroom scene during study phase and Figure 1b is an example of the test scene for that version. The program had been set to ensure that the sequence of the four different environment images would be pseudo-randomized for counterbalancing purposes, in which all the scenes were presented once, whereas the versions and test scenes selected were randomized. Moreover, the target objects that were circled during the test scenes were also pseudo-randomized, in which each object would only be circled once. For the practice run, both the study scene and the test scene were presented in a hardcopy form, which was laminated. Two separate slips of paper were prepared, one being “Was this object anywhere in this picture before?” for the participants allocated to the Presence condition, and “Was this object in this place before?” for the participants allocated to the Location condition. The paper slips containing the questions were left on the table for participants to refer to. Figure 1a The above image depicts version 1 of the bathroom scene. The two target objects in schema-expected locations are the shampoo and toothpaste, whilst the two target objects in the schema-unexpected locations are the mirror and toilet brush, and the schema-irrelevant objects are the file, glove, toy. Figure 1b The above image depicts Test 1 of Version 1 of the bathroom scene. The mirror has now been shifted from a schema-unexpected location to a schema-expected location whilst the toothpaste remains in the same position. The shampoo has now been shifted to a schema-unexpected location. The toilet paper and weighing scale, which was previously not present during the study scene is now present in the schema-expected and schema-unexpected location respectively, with the toilet paper being circled for the participant to respond to. The schema-irrelevant objects that were added were the jacket, pencil case and handbag. Design: This study consists of two analyses; to address the two research questions. First regarding whether location affects true and false memories, and second, to see what shifts in location do to memory for the original object memory (condition 1) and object-location pairing (condition 2). The first analysis investigates the true and false memories involving objects that were present and not present at study, whilst the second analysis investigates the true and false memories toward objects that were present at study and were later shifted during the test scene. Hence, a mixed ANOVA design was used to address the first of these questions. The within-subject independent variables include the study (present, not present), and the schema appropriateness of the object location (schema-expected, schema-unexpected, irrelevant). The between- subjects factors include the conditions (presence, location) and age groups (younger children, older children, adults). The “yes” responses for the objects that were present in both scenes but not shifted and objects that were not present during the study scenes but were present in the test scene were analyzed. For objects that were shifted during the study and test scenes, the within-subjects factor was schema (schema-expected, schema-unexpected), and the shifting of objects (shift, no shift). The between-subjects factors include the conditions (Presence, Location) and age groups (younger children, older children and adults). The dependent variable was the accuracy of responses given, to compare the difference between objects that shifted and objects that did not shift. Procedure: The experiment consisted of a study phase, a distracter task and a test phase, which took an estimated 10 minutes to complete and was conducted in an unoccupied learning classroom, in the Burnley Primary School, whereby participants were individually tested. Each participant was required to undergo a practice run before the actual experiment took place, to ensure that the participant had understood what he or she had to do. In the practice run, the laminated image of the seminar room was presented alongside the paper slip with either the Presence question or the Location question, depending on which condition the participant had been assigned to. The participants were given 12 seconds to study the image. After 12 seconds, the participant was presented with another image with several target objects circled, in which the objects would be pointed to one by one by the researcher. The participant would then be prompted to verbally respond if they had either seen that object anywhere before during the study scene or if that object had been in that location before during the study scene. For both conditions, the participants were instructed to press either the “Y” or “N” key on the keyboard in response to whether they had seen the circled object anywhere in the picture before during the study phase (Presence condition), or if they had seen the circled object in that particular location before were it the Location condition. Once the participants acknowledged that they had understood, they were presented with the actual experiment. Each participant was required to study four different environments, in which one of three versions would be selected for every environment. Each study scene would last for 12 seconds for the participant to study, and then a distracter task would immediately appear. The distracter task, which lasted for 30 seconds, required the participant to hit any key on the keyboard whenever a specified animal (eg: giraffe, frog, hippopotamus) appeared. A green tick would appear every time the participant successfully presses a key before the specified animal disappears. Once 30 seconds was up, the distracter task would end, and one of two of the test scenes for that environment would appear. A total of twelve objects would be circled sequentially, with the next object only being circled 0.5s after the participant had given a response. Depending on which condition the participant was in, once every object had been circled, the participant would be required to respond to the question “was this object anywhere in this picture before?” (Presence condition), or “was this object in this place before?’ (Location condition). If the participant, who was in the ‘presence’ condition, deemed that the object was somewhere in the picture before, he or she would respond by pressing the “Y” for Yes on the keyboard; if it was deemed to not be in the picture before, the participant would then press the “N” for No on the keyboard. The same thing was conducted for the Location condition. Once the participant had responded to all 12 objects, a different environment scene would appear and the participant would be required to repeat the process until all four scenes had been shown.
The validity of traditional readability tests on accurately predicting people’s comprehension of health information
Vocabulary knowledge, health literacy, reading comprehension skill, reading strategy
Tons of evidence indicated that readers benefit from clear and understandable health information in various contexts. Authors have been looking forward to utilizing a wide range of readability formulas so that they can produce comprehensible texts for readers. Both traditional readability formulas and the new Coh-Metrix algorithms have been widely used for decades and the utilities for the new tool were more likely to be supported by theoretical evidence. Nevertheless, there is still a lack of empirical evidence supporting the utilities of the two kinds of readability formulas. In this paper, a secondary data analysis was utilized to give empirical evidence to whether the widely used readability tests can predict participants’ comprehension responses effectively. By using Bayesian generalized linear mixed-effects models, variation in both traditional readability formulas and two of the new Coh-Metrix algorithms were tested having little or no effect on variation in participants’ comprehension accuracy. In this case, it is suggested that researchers in the future should think twice before utilizing the readability tests to analyse text difficulty.
Participants Participants recruited in the original study were through the Prolific online platform. Participants recruited were all UK nationals who were aged eighteen or over and spoke English as their first language. Participants who completed the test battery were awarded £12.50 (equalling £6.25 per hour). All participants who volunteered were tested, with exclusion of participants whose reading times for the health-related information texts were recorded being below 30s. The reading time includes reading the text and answering the questions relating to that text, including the self-rated evaluation-of understanding probe. While participant recruitment was administered through the Prolific platform, response data collection was conducted through a Qualtrics survey for each study. Design Two studies were conducted in the original research. In Study One, participants were presented with a sample of written health information texts on a range of topics. The observation was replicated and extended in Study Two by presenting a sample of texts on a range of health topics, together with a sample of guidance texts on COVID-19. In both studies, participants were asked to complete four multiple-choice questions, each with three answer options, in response to each stimulus health text. After the comprehension test questions, participants were asked to rate how well they thought they understood the information in the guidance. The original dataset also included individual differences, including reading skill and knowledge, and collected information on text attributes. Responses by participants in terms of the comprehension of the four multiple-choice questions for each text and the individual differences, such as reading skills and knowledge, would be utilized in the current study analysis with more kinds of text attributes included. In sum, except the heath-text materials picked to test participants’ comprehension responses and participants chosen, all other variables and procedures were identical in both studies. Since the difference between the two datasets in the two studies was the inclusion of texts on COVID-19 in Study Two, and all variables included in both datasets were identical, both data were renamed as Dataset One and Dataset Two in order to more easily distinguish between the two. Material For Dataset One (Study One in the original data), 25 health-related information texts were collected from those available on NHS trust organization webpages. The texts collected were chosen from 115 candidate texts from those available among the web resources of a quasi-random sampling of 23 NHS England trusts (10% of the 228 total in England). For Dataset Two (Study Two in the original data), 14 texts concerning a range of health matters and 15 texts concerning COVID-19 or guidance relating to the public health response to the pandemic were collected. As in Dataset One, the general health texts were selected as a sub-set of a (fresh) pool of 115 candidate texts extracted from those available among the web resources of a (new) sample of 23 NHS England trusts. The COVID texts were selected from a pool of 115 candidate texts extracted from those available from gov.uk, charity (British Heart Foundation, Cancer Research UK), NHS UK, and NHS England trust webpages. The selection of texts, for both general health and COVID-19 information, was made so that the sub-set of items varied as widely as possible across the distribution of values (for each pool of candidates) on each critical text feature. For each text chosen, a set of four multiple-choice questions (MCQs) was constructed, each with three answer options, to testify participants’ comprehension levels. Individual differences measured: vocabulary knowledge, health literacy, reading comprehension skill, and reading strategy: Vocabulary knowledge. The Shipley vocabulary sub-test was used to estimate vocabulary knowledge (Kaya et al., 2012). Participants were required to choose the synonymous word from four alternatives to a target stimulus word in The Shipley test (the other three alternatives are semantically related or unrelated distractor words). Participants were associated with a test result corresponding to the total number of correct answers out of 40 multiple-choice items. Health literacy. The Health Literacy Vocabulary Assessment (HLVA) was used to estimate health literacy. Participants were required to choose the synonymous word from four alternatives to a target stimulus word and all the items are under health contexts. Since the vocabularies presented were drawn from the health-care profession, the HLVA is designed to test participants’ background knowledge of health matters and is considered an index of health literacy. Participants were associated with a test result corresponding to the total number of correct answers out of 16 multiple-choice items. Reading skill. The Qualitative Reading Inventory (Leslie & Caldwell, 2017) was used to assess reading skills. Participants were asked to read a short factual text (compromised of 802 words) about the life cycle of stars and then answer two sets of 10 open-class questions related to the text, respectively. The questions not only included information that can be found explicitly in the text but also information that requires inference from background knowledge. Participants were associated with a QRI score corresponding to the total number of correct answers out of 20 open-class questions. Reading strategy. A Reader-based standards of coherence measure published in a doctoral paper by Calloway (2019) was used to assess reading strategy. Participants were asked to complete a 5-point Likert scale based on their reading experience ranging from very untrue to very true. The scale includes 87 items and is supported to measure readers’ reading goals and learning strategies effectively. Participants were associated with a scale score corresponding to their response on the 87-item scale. Text features measures: traditional readability tests scores, coh-metrics scores of the health-related information texts presented to participants: Referential Cohesion. The Coh-Metrix tool was used to calculate the referential cohesion (co-reference) of texts. Referential cohesion emphasises the overlap degree of concepts, words, and pronouns between sentences and paragraphs. With the increase of the similarities of sentences and conceptual ideas within a text, it is easier for readers to make connections between ideas and sentences (Coh-Metrix, 2012). Nevertheless, low referential texts sometimes are necessary when readers are required to be more actively involved in comprehending a text (Coh-Metrix, 2012). Deep cohesion. The Coh-Metrix tool was used to calculate the deep cohesion of texts. Deep cohesion refers to how well a text is tied together by an efficient number of cohesion ties, also called connectives (Coh-Metrix, 2012). The calculation of deep cohesion in a text is determined by the number of the connectives including time, causal, additive, logical and adversative connectives, which connect ideas and propositions and clarify relations in a text (R-Kintsch & Walter Kintsch, 1998). Being able to utilize the connectives effectively helps to tie the information together; thus, it facilitates the readers’ understanding. Flesch Reading Ease Score (FRE). The FRE (Badarudeen & Sabharwal, 2010) is one of the traditional readability tests. The formula for the FRE is 206.835 - (1.015 * ASL) - (84.6 * ASW), where ASL represents the average sentence length and ASW represents the average number of syllables per word. The FRE evaluates texts on a 100-point scale and higher scores means that it is more difficult to comprehend the text. The Gunning Frequency of Gobbledygook (FOG). The FOG (Roberts et al., 1994) is one of the traditional readability tests. The formula for the FOG is 0.4*(ASL + % polysyllabic words), where ASL represents the average sentence length. There is a minimum word count for the passages tested using FOG, more than 100 words, and the results given correspond to the education level that a reader needs to comprehend a text. The Flesch–Kincaid Grade Level (FKG). The FKG (Woodmansey, 2010) is one of the traditional readability tests. The formula for the FKG is (0.39*ASL) + (11.8*ASW) - 15.59, where ASL represents the average sentence length and ASW represents the average number of syllables per word. The results given from the FKG provide a number indicating the specific grade that readers should achieve to comprehend the text, which ranges from grades 3 to 12. Simple Measure of Gobbledygook (SMOG). The SMOG (McLaughlin, 1969) is one of the traditional readability tests. The formula provided is 1.043 * square root of (number of polysyllabic words * [30/number of sentences] + 3.1291). The SMOG also provides a school grade as a result, indicating the specific education level a reader should have to understand a text, and it was recommended by the National Cancer Institute as having a better performance than the other tests. Demographic attributes. Participants’ demographic characteristics were recorded, including gender (coded: Male, Female, non-binary, prefer not to say), education (coded: Secondary, Further, Higher), and ethnicity (coded: White, Black, Asian, Mixed, Other).
How the correlation of Istagram tweets readability and brand hedoism affect audiecne engagment
Readability, Brand hedonism, readbility formula, audience engagment
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.
Research Question & Hypotheses The research question for this study is: can the readability of tweets influence the level of audience engagement? As 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. At 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). It 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. Data collection for the experiment From 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. Instagram 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. Step 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）. In 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. Step 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) Variables and measures Message readability Readability 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). The specific mathematical formula is as follows: RE = 206.835 – (1.015 x ASL) – (84.6 x ASW) RE = Readability Ease the output is a number ranging from 0 to 100. The higher the number, the easier the text is to read ASL = Average Sentence Length (i.e., the number of words divided by the number of sentences) ASW = Average number of syllables per word (i.e., the number of syllables divided by the number of words) Table1: Description and predicted reading grade for Flesch Reading Ease Score (Stein, 1984) Score School level (US) Notes 100.00–90.00 5th grade Very easy to read. Easily understood by an average 11-year-old student. 90.0–80.0 6th grade Easy to read. Conversational English for consumers. 80.0–70.0 7th grade Fairly easy to read. 70.0–60.0 8th & 9th grade Plain English. Easily understood by 13- to 15-year-old students. 60.0–50.0 10th to 12th grade Fairly difficult to read. 50.0–30.0 College Difficult to read. 30.0–10.0 College graduate Very difficult to read. Best understood by university graduates. 10.0–0.0 Professional Extremely difficult to read. Best understood by university graduates. As 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). Consumer engagement with brands As 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. Degree of brand hedonism As 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). In 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. In 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. Table 2 the brand hedonism scores NO Brands Mean SD 1 Porsche 6.05 1.26 2 Google 5.92 0.95 3 Mercedes-Benz 5.68 1.13 4 Amazon 5.41 1.59 5 Gucci 5.29 1.13 6 Nike 5.05 1.40 7 Starbucks Coffee 4.89 1.19 8 ESPN 4.75 1.93 9 Coach. Inc 4.53 1.60 10 Chanel 4.40 1.26 11 Boeing 4.27 1.77 12 Hyundai USA 4.12 1.45 13 HP 3.86 1.75 14 Subway 3.75 1.67 15 Verizon 3.75 1.36 16 IBM 3.45 1.47 17 Walmart 3.15 1.39 18 Walmart 3.15 1.39 19 HSBC 2.89 1.35 20 Goldman Sachs 2.14 1.23
A secondary data analysis: How will the effects on accuracy differ when measuring individual differences in word reading skill in Spanish?
Individual differences, Dyslexia, Word property effects, Language orthographies, Reading accuracy
A deficit in accuracy has been found to correlate to reading difficulties (Davies et al., 2007). Effects of psycholinguistic factors and differences in language orthographies contribute to reading skills, predominantly in children with reading impairments such as dyslexia. The present study is a secondary data analysis of the original research conducted by Davies et al. (2007). The effects on accuracy of individual differences demonstrated by nonword reading skill and word property measures were examined in Spanish children. Participants were 110 students differing in reading ability from schools located in A Coruńa, Lugo, Orense and Pontevendra in northern Spain. The subjects were required to take standardized and experimental reading ability and intelligence tests. Eight lists consisting of 15 words each were created. The words were presented in five rows of three columns. Participants were asked to read the words as quickly and accurately as they could. Words which were incorrectly pronounced were identified as errors. Word property measures suggested to affect reading ability were selected and updated from an online database of Spanish words ‘EsPal’. Variables of frequency, length of words, neighbourhood size (Levenshtein distance), RAN, PROLEC-R nonword reading were investigated in the present analysis. Accuracy of reading scores was found to be significantly high for the sample. Effects of individual differences on accuracy were noted. Word property measures of frequency and neighborhood size were found to significantly affect reading accuracy. Effects of fluency (RAN) and nonword reading (PROLEC-R) were also observed. The analysis provides insight into plausible factors which contribute to reading impairments in a rule governed orthography such as Spanish. Results suggest that perhaps nonword reading skill could serve as an marker for reading difficulties.
Participants In the original study (Davies et al.,2007) researchers selected and identified three groups of children from an initial sample of 110. Children who indicated clear reading disabilities (DYS/ dyslexia), a control group consisting of children matched by reading ability level (RA matched group) to the DYS group and a chronological age control group (CA matched group). The present analysis investigated the whole sample of 110 participants and no group selection was conducted. Participants were students from schools located in A Coruńa, Lugo, Orense and Pontevendra in northern Spain. 110 children differing in reading ability and age were selected. These children did not obtain any prior diagnoses of impaired neurological or sensory-motor functioning. The sample of 110 children was required to take standardized and experimental reading ability and intelligence tests on different school days during a 3-month time. Experimental data was gathered in a single session focusing primarily on the experimental test, whereas the standardized reading test was given in a separate session. Measures Reading performance was measured across a series of ability tests (PROLEC-R, RAN). PROLEC-R Battery Tests of Literacy Skills Evaluation of reading processes for children is assessed through the use of the PROLEC-R battery constructed by Cuetos, Rodriguez, Ruano & Arribas (1996). The battery consists of Spanish tests analyzing reading processes such as lexical, semantic etc. Subjects were required to read from a list of 40 words as quickly and accurately as possible. Words differed on properties such as frequency and length. The scores obtained consist of a score relating to accuracy and reading speed when assessing words and nonwords. It has been suggested that the results of the test provide significantly more information when combining the PROLEC-R scores of accuracy and PROLEC-R reading times. This is why PROLEC-R nonword reading was computed into a combined measure. This was done by dividing accuracy by time. Rapid Automatized Naming Tests (RAN) Rapid automatized naming (RAN) refers to how quickly a child can read aloud a set of previously known items. These items can include numbers, pictures, letters, colors etc. A child’s performance on the tests is assessed by comparing their reading times to the norm scores of children in the same age. RAN tests are designed to predominantly assess fluency of reading. It is suggested that RAN influences reading scores as it requires the retrieval of stored phonological information (Johnson & Eden, 2014). Children were presented with a sequence of rows consisting of sets with different items (colors, letters, pictures etc.). The subjects were required to read aloud all the items from the list starting from top to bottom. Accuracy of reading and time it took for the child to name the words were recorded. Children with reading difficulties will be expected to present a delay in reading speed and accuracy, thus scoring low on the RAN tests. Word Property Measures In the original study (Davies et al.,2007), words were chosen varying on lexical frequency (high or low frequency word), orthographic neighbourhood size (many or few neighboring words) as well as word length (short or long in length) (factorial design 2x2x2). Updated word property measures were derived from the EsPal (“Español Palabras” meaning “Spanish words”) repository consisting of properties for Spanish words. The new word property measures derived from the database (frequency, length of words and neighbourhood size) were compiled together with the old data. The system is able to process different corpora in the same way. It combines a corpus which is derived from movie subtitles and one from previously written text such as Web pages, fiction, nonfiction writing etc. The updated measure of frequency is reported within the analysis with the databases original name “esp.count”. The ‘count’ refers to the number of times in which the word appears within the selected corpus. For orthographic neighbourhood size, all words are counted within EsPal and are in turn compared to other words within the corpus. Yarkoni et al (2008) argued that the orthographic neighbourhood metric (ON) developed by Coltheart et al.(1977) is limited due to the nature of its definition. ON is the number of words which can be developed by substituting one letter in the other word given that it is the same length. As a result, researchers have developed a new measure of orthographic neighbourhood size which is less restricted than the previous metric. The new measure is coded as Levenshtein distance 20 (Lev_N) (Duchon et al.,2013). Levenshtein distance refers to the average distance of 20 words which are found closest in text. LD is calculated as the number of edits to words (substitutions, insertions, deletions) which are needed to change one word into another. For example, the Levenshtein distance between the word “SMILE” to “SIMILES” is two, as it differs from the original by adding the letters “I” and “S” (Yarkoni et al., 2008). An updated measure of length of words was also derived from the EsPal database and is coded as “esp.num_letters”. This refers to the word length which is expressed in number of letters. Procedure Eight lists consisting of 15 words each were created. Participants were shown each list of words on a A4 sheet of paper. The words were presented in five rows of three columns. Participants were tested individually and were asked to read the words as quickly and accurately as they could. Words which were incorrectly pronounced were identified as errors. Three types of errors were identified: word substitution, nonword and stress errors. An example of word substitutions would be the word “nube” (cloud) which would turn into "neuve “(nine). For nonwords: “bigote” (mustache) would be “bixote”. For errors relating to stress “cáfe” would be “café”. All responses from 110 participants were computed and are present in the file: “SpanishR”. Accuracy is presented as the subject responses scored as correct and incorrect (0,1). Analysis Item level and subject level data about word properties and subject attributes were extracted. An analysis of the accuracy of responses as well as the effects of word properties on reading was conducted. Errors were scored as 0,1; correct and incorrect. Random and experimental variables were identified. Random effects were specified as “palabra” (words) and “subject identifier” (participant name). The experimental/fixed effects were specified as frequency, length, neighbourhood size, RAN, PROLEC-R nonword reading. To investigate correlations between the experimental variables a correlation matrix was constructed. Generalized linear mixed effects modeling (GLMM; Baayen, 2007) was used in order to analyze the accuracy of responses made by children to reading words. The distribution of variables included in the model relate to person characteristics and word characteristics. Moreover, GLMM was used to capture the randomness of the sample to increase accuracy of estimates for the effects of individual differences on word properties. The model explains the variation of accuracy by incorporating experimental and random variables. Model development followed a stepwise process, adding one variable to each model at a time. The primary model specification was as follows: accuracy~(1|palabra) + (1|subj_identifier), data = spanishr. A table of estimates of both random and fixed effects were created and analyzed in order to assess the variation in the models.
Data set belongs to Robert Davies who is the author of the original published study (Davies et al.,2007, “Reading development and dyslexia in a transparent orthography: a survey of Spanish children”.)
The present work is a secondary data analysis of the original research conducted by Davies et al. (2007), “Reading development and dyslexia in a transparent orthography: a survey of Spanish children”.
English and Spanish (Spanish participants, words, database)
Carer adaptation to childhood epilepsy: The role of the Epilepsy Specialist Nurse.
Positive carer adaptation to childhood epilepsy is essential as poor adaptation can be detrimental to child behaviour outcomes. Fulfilment of carer psycho social and informational need is important to facilitate successful adaptation to childhood epilepsy. The role of the Epilepsy Nurse Specialists (ESN) is well suited to meet psychosocial need and so ESNs are hypothesised to improve carer adaptation and in turn child behaviour. This study investigated carer adaptation in geographical areas in the north of England with and without ESN provision using telephone interviews with carers of children with epilepsy. It was found that ESN provision had no significant effect on carer adaptation, psycho social needs of the carer, or child behaviour. Reasons for why no effect was found is attributed to the significant difference in condition severity and comorbidity between the groups that require more complex care needs. Limitations and future research directions are discussed.
1. Parent Response to Child Illness (PRCI). Participants were assessed in three outcome measures. 2. Parent Report of Psychosocial Care Scale (PRPCS). The second area of assessment was carers' perceived need for information and support. 3. Child Behaviour Checklist (CBCL). The third measure is a measure of child behaviour using the CBCL (Achenbach & Rescorla, 2001). 4. Hague Seizure Severity Scale (HASS). Participants were also asked to provide information relating to their child's seizure severity, their mental health and demographic information so intergroup comparisons could be made. 5. General Health Questionnaire (GHQ). Mental health of the carer was assessed using the short form of the GHQ (Goldberg & Hillier, 1979). 6. Demographic Information. Additionally participants were asked to complete a questionnaire containing demographic information, and also information relating to age of seizure onset, time of last seizure, seizure frequency, anti-epileptic drug (AED)therapy and adherence to this medication.
Do inward and outward consonants and vowels have different effects on customer’s liking rates towards the brand names?
Consonants, vowels, MacNeilage syllables, brand names, liking rates
The origin of speech development starts with the way that infants or children produce their first words. In the early stage of speech acquisition, children tend to produce particular syllables that are low in energy to produce, such as intrasyllabic and intersyllabic consonant-vowel co-occurrence patterns (MacNeilage et al., 2000). Such patterns may have an effect on individual’s preference for words later in life, such as for brand names. More pointedly, according to Topolinski et al. (2014), there is an in-out effect which significantly affect individual’s liking rates towards the brand names that contain inward and outward consonants. However, previous findings have only focused on such effects on consonants, whereas there is insufficient research on the combination effects of consonants and vowels on brand names. Therefore, this study is designed to investigate whether such in-out effects of both consonants and vowels of English brand names have association with customer’s emotional response to the words, as well as whether the involvement of MacNeilage syllables in the brand names are associated with customer’s liking rate. The whole experiment was conducted through an online questionnaire consisting of 360 sound stimuli to test on participant’s liking rate towards the brand names which are non-words with the combination of inward and outward consonants and vowels, and Macneilage syllables. Results of the study showed that liking rates towards the brand names are significantly increased for the ones that include inward consonants and vowels, while lower liking rates were associated with outward consonants and vowels. Not to mention, no significant relationship was found between the number of MacNeilage syllables and one’s preference towards the brand names, yet individuals had higher preference for brand names that contained MacNeilage syllables as the first syllable of the word.
Participants A total of 51 participants who spoke different first languages were recruited through researcher’s family and friends as well as invited via SONA. They were all healthy individuals with normal vision and hearing, all aged 18 or above with no health conditions. The participants included 23 males and 28 females, with the age range from 22 to 28 and a mean age of 23.33, SD=. Materials The study was carried out as an online questionnaire which consisted of four open ended questions at the beginning and 360 questions with a 10-point Likert scale to display the answers. The whole questionnaire was based on the liking rate of the brand names that were presented as sound stimuli displayed in the questionnaire. The first four open ended questions were designed to ask participants’ age, gender, first language and whether they speak other languages (see Appendix D). Next, 360 questions each containing an audio of a sound stimulus that was between one to three seconds were presented in the questionnaire (see Appendix D). All sound stimuli were recorded by the researcher’s supervisor who was a native English speaker with a Northern English accent with training in phonology beforehand, which were also produced in a monotone. Within the 360 sound stimuli, they were divided into six different sets which included six combinations of inward and outward consonants and vowels. The total six sets of stimuli included nonwords that contained consonants that required the articulation from front to the middle to back of the mouth (inward) (FMB), from front to back to middle (FBM), from middle to front to back (MFB), from middle to back to front (MBF), from back to middle to front (outward) (BMF) and from back to front to middle (BFM). There was a total of 60 stimuli with the same articulation of consonants and different articulation of vowels in each set, and 10 stimuli with the same articulation of both consonants and vowels in each set. Within each set of the same articulation of consonants, six possible combinations of front/middle/back vowels were paired up with the consonants to create the stimuli so that every possible arrangement of front/middle/back consonants and vowels was tested in the questionnaire. Moreover, among the 360 stimuli, 120 of them contained zero MacNeilage syllables, 178 of them contained one MacNeilage syllables while 62 of them contained three MacNeilage syllables. To ensure that there was no personal bias towards the brand names, all stimuli were nonwords that were created by the researcher so that participants would not be familiar with any of the brand names. Procedure Before the study began, all participants were sent a participant information sheet and consent form through email (see Appendix A & B). Participants were then also given a link to the online questionnaire which was attached in the same email. At the beginning of the questionnaire, four open-ended questions on personal information were presented and participants were asked to answer their age, gender, first language and whether they speak other languages (see Appendix D). After completing the four questions, participants had to answer 360 questions with each containing an audio of a sound stimuli, which were referred as brand names in this survey. Each question was displayed as ‘how much do you like this brand name’ and participants were asked to rate each sound stimuli according to their preference on the 10-point Likert scale, labelled as 1 as the lowest and 10 as the highest (see Appendix D). There was a ‘play’ button in every question where participants could play the sound stimulus and they were allowed to play the audio as many times as they prefer if they wished. In the questionnaire, five questions were presented on each page and there was 73 pages in total, including one page in the beginning for the four open-ended questions. The 360 questions on the sound stimuli were presented in randomised order for each participant to ensure there were no order effects relating to individual stimuli in the data. The whole study took around 20 to 30 minutes depending on whether the participants replayed the audios or not. After completing the questionnaire, all participants were delivered a debrief sheet via email, allowing them to ask any questions regarding the study (see Appendix C). Ethics The study was granted ethics approval on 19/05/2022. Both a participants information sheet and consent form were delivered to all participants before the study began to indicate their rights to withdraw up to three weeks after participating in the experiment if they had changed their minds. After completion of the questionnaire, a debrief sheet was sent out to participants to allow them to raise questions regarding the study. They were also informed that their participation was confidential, with all data stored in encrypted files.
Assessing comprehension of health-related texts in non-native and native English speakers
reading comprehension, health literacy, individual differences, language groups.
Background — Health written materials are more often complex to comprehend if they mismatch the reading ability of people in the target audience. We need to consider how to make text accessible, by considering individual differences that affect comprehension of written health materials. Surprisingly, there are very few studies that indicate how non-native English speakers and native English speakers differ in comprehension of written health texts. Methods — A total of 557 participants were studied in the present study. In the study, participants were asked to respond to multiple-choice questions that were designed to examine understanding of 25 health texts with different text properties. Each participant responded to tests measuring individual differences in demographics, reading strategy, vocabulary, and health literacy. Findings — Using mixed effects logistic regression analysis, we found that non-native English speakers and native English speakers have different accuracy of responses for written health texts. Effects of vocabulary skills and text readability were significant. These effects were different for different language groups. Native speakers of English with higher scores on vocabulary were more likely to make correct responses to written health texts. Native speakers of English were more likely to make correct responses to written health texts as text readability increased. Conclusion — In future, experimental studies should look at the effects of training to improve vocabulary on reading comprehension for different language groups. Alongside consider sources of variances due to individual differences and text properties for different language groups.
Design We conducted experimental research on factors that influence the response to written health information, aiming to answer the research question: RQ.1 How does the reader’s attributes such as age, vocabulary skills, health literacy, reading strategy skills, along with text features, interact to predict the comprehension of health information in written texts for native language English speaker and non-native English speaker? We conducted the study to test the hypotheses: 1. Comprehension will be better for people with higher scores on reading skill, vocabulary and health literacy. Comprehension will be lower as age increases. 2. Comprehension will be better for responses to texts which are higher on measures of readability, cohesion, word frequency, referential cohesion, and passive sentences. 3. There will be differences between native and non-native speakers. Comprehension will be better for native speakers. The effect of age, reading strategy, vocabulary skills, and health literacy will be different for different language groups. The effect of cohesion, readability, word frequency, referential cohesion, and passive sentences will be different for different language groups. Ethical approval. The data collection plan and study design were reviewed and approved by a member of the Psychology Department Research Ethics Committee. Pre-registration. The study has not been pre-registered. Participants Participants were recruited using primarily opportunity and snowball sampling. Participants were invited using social media such as Facebook, Instagram, and WhatsApp. We aimed at recruiting Bilingual/Multilingual Indian Residents (18+) who have access to the internet. We collected 201 responses, but only 112 participants were included in our analysis due to incomplete forms by other respondents. Our criterion for including participant data in analyses was that they had to complete 80 percent or more of the survey. We had 112 responses, but we did not test any of the respondents who were aged 100. We had three respondents who were aged 100 removed from our data set, leaving us with 109 observations. To enable a comparison between native and non-native speakers of English, we combined the data on responses from Indian and Chinese non-native speakers with data on responses from native speakers of English collected previously by supervisor Rob Davies. Thus, we had a large sample size of 557 participants for analysis. We did our final analysis on 557 participants with minimum age of 18 and maximum age of 81. Average age range in sample was 28, skewing towards younger population. The sample consisted of 392 females, 160 males, 1 non-binary, and 4 prefer not to say. There were 273 participants who spoke English as their first language and 284 participants who spoke English as their second language. All participants were debriefed, and steps were taken to ensure confidentiality and anonymity. Materials We collected information on attributes of participants and linguistic properties of texts to see its influences on accuracy of responses made by participants to questions related health information. To measure participants attributes, we assessed demographic details, and participant’s vocabulary knowledge, health literacy, and reading strategy. Health texts differed in their linguistic properties, as measured by word frequency, readability (Flesch score and grade level), number of passive voice sentences, cohesion, and referential cohesion (Coh-Metrix). Vocabulary knowledge. The Shipley Vocabulary Test (Shipley et al., 2009) was used to test participants' vocabulary knowledge as it predicts 39-45% of variance in reading comprehension (Landi, 2010). The test includes questions in a multi choice question format, with incorrect and correct answers. Each question contains a word followed by four options—one of which is the correct meaning of the word. The higher the points, the higher the level of vocabulary. Health Literacy. The Health Literacy Vocabulary Assessment (HLVA) developed by Ratajczak (2020), adapted for online presentation by Chadwick (2020) was used to test participants’ health literacy. The adapted version of the HLVA contains 16 multiple-choice word items. The test consists of multiple-choice questions with incorrect and correct answers. Each question contains a word followed by four options. The participant must select the correct meaning of that word. High scores on HLVA indicate high health literacy vocabulary. Reading strategy. To determine participants’ motivation for reading and understanding reading strategies, we used Calloway’s (2019) third sub-test: Desire for Understanding and Reading Regulation Strategies. The items have been developed to measure the extent to which readers are willing to expend cognitive effort to understand a written text (Van den Broek et al., 2001). A higher score on this measure predicts better comprehension (Calloway, 2019). Demographics. We collected participants’ demographic characteristics: gender (coded: Male, Female, non-binary, prefer not to say); education (coded: Secondary, Further, Higher); and ethnicity (coded: White, Black, Asian, Mixed, Other); age; native language. Health information stimulus text sampling. Comprehension passages are selected based on previous research paper by Davies and colleagues (in prep.) In total there are 25 comprehension passages. However, reading 25 passages in one sitting could lead to fatigue in the reader. Therefore, we created 5 sets of 5 comprehension passages. Each set contained 5 passages, which were randomly given to participants. The comprehension passages were then followed by questions in a multiple-choice question format. The response to each question is either right or wrong, which indicates whether the reader understands the passage. The questions have been constructed in ways to ensure that questions probed for the most important information in each text, such as who the information was relevant to, who was involved in diagnostic or treatment procedures, and the risks and benefits of different options. The questions were constructed in a manner that could not be answered by matching or referring to the text but required text-level and interpretation-level comprehension processing to correctly choose answer options (Kintsch, 1994).
Huzaifah Adam, Coco, Alex Myroshnychenko
This work is based on Kintsch, W. (1994). Text Comprehension, Memory, and Learning. American Psychologist, 10. White, S., Happé, F.M., Hill, E., & Frith, U. (2009). Revisiting the Strange Stories: Revealing McNamara, D., & Magliano, J. (2009). Chapter 9 Toward a Comprehensive Model of Comprehension. Psychology of Learning and Motivation, 51, 297–384. https://doi.org/10.1016/S0079-7421(09)51009-2 O’reilly, T., & Mcnamara, D. S. (2007). Reversing the Reverse Cohesion Effect: Good Texts Can Be Better for Strategic, High-Knowledge Readers. Discourse Processes, 43(2), 121–152. https://doi.org/10.1080/01638530709336895
Excel spreadsheets - .csv R Script - .r
reading comprehension, health literacy, individual differences, language groups