262,270 research outputs found
Examining academic performance across gender differently: Measurement invariance and latent mean differences using bias-corrected bootstrap confidence intervals
No full textThe aim of this study was threefold: First, to examine the dimensionality of the construct of General Academic Ability (GAA) at the subscale level providing additional insights over and above on the conceptualization of the construct. Second, to explore different degrees of measurement invariance of the GAA across gender using more recent advancements in the examination of Measurement Invariance (i.e., Bias-Corrected bootstrap Confidence Intervals). Third, to examine gender differences across the different facets of the GAA at the latent mean level. The sample consisted of 1,800 high school graduates who applied for higher education in Saudi Arabia. The results from the analysis indicated that the hierarchical model with one higher-order factor (i.e., general academic ability) and four lower-order cognitive factors (i.e., verbal ability, quantitative ability, scholastic aptitude, and GPA) exhibited an excellent fit to the data. In terms of the measurement invariance hypothesis, it was found that the hierarchical model exhibits full configural and metric invariance and partial scalar invariance. Finally, using the Latent Mean Difference procedure, the results showed gender differences in the Verbal and GPA domains. Although significant differences were also found in the Scholastic aptitude domain, this finding is not stable due to several non-invariant items within the domain. In both cases, females scored higher than males. Finally, regarding the higher-order factor (GAA), the results showed that females scored higher than males. There were no significant differences in the Quantitative domain. Copyright © 2022 Tsaousis and Alghamdi
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Identifying Student Subgroups as a Function of School Level Attributes: A Multilevel Latent Class Analysis
No full textThe purpose of the present study was to profile high school students’ achievement as a function of their demographic characteristics, parent attributes (e.g., education), and school behaviors (e.g., number of absences). Students were nested within schools in the Saudi Arabia Kingdom. Out of a large sample of 500k, participants involved 3 random samples of 2,000 students measured during the years 2016, 2017, and 2018. Randomization was conducted at the student level to ensure that all school units will be represented and at their respective frequency. Students were nested within 50 high schools. We adopted the multilevel latent profile analysis protocol put forth by Schmiege et al. (2018) and Mäkikangas et al. (2018) that account for nested data and tested latent class structure invariance over time. Results pointed to the presence of a 4-profile solution based on BIC, the Bayes factor, and several information criteria put forth by Masyn (2013). Latent profile separation was mostly guided by parents’ education and the number of student absences (being positive and negative predictors of high achievement classes, respectively). Two models tested whether the proportions of level 1 profiles to level 2 units are variable and whether level 2 profiles vary as a function of level 1 profiles. Results pointed to the presence of significant variability due to schools. © Copyright © 2021 Sideridis, Tsaousis and Al-Harbi
The impact of non-attempted and dually-attempted items on person abilities using item response theory
No full textThe purpose of the present study was to relate response strategy with person ability estimates. Two behavioral strategies were examined: (a) the strategy to skip items in order to save time on timed tests, and, (b) the strategy to select two responses on an item, with the hope that one of them may be considered correct. Participants were 4,422 individuals who were administered a standardized achievement measure related to math, biology, chemistry, and physics. In the present evaluation, only the physics subscale was employed. Two analyses were conducted: (a) a person-based one to identify differences between groups and potential correlates of those differences, and, (b) a measure-based analysis in order to identify the parts of the measure that were responsible for potential group differentiation. For (a) person abilities the 2-PL model was employed and later the 3-PL and 4-PL models in order to estimate upper and lower asymptotes of person abilities. For (b) differential item functioning, differential test functioning, and differential distractor functioning were investigated. Results indicated that there were significant differences between groups with completers having the highest ability compared to both non-attempters and dual responders. There were no significant differences between no-attempters and dual responders. The present findings have implications for response strategy efficacy and measure evaluation, revision, and construction. © 2016 Sideridis, Tsaousis and Al Harbi
Measurement Invariance and Differential Item Functioning Across Gender Within a Latent Class Analysis Framework: Evidence From a High-Stakes Test for University Admission in Saudi Arabia
No full textThe main aim of the present study was to investigate the presence of Differential Item Functioning (DIF) using a latent class (LC) analysis approach. Particularly, we examined potential sources of DIF in relation to gender. Data came from 6,265 Saudi Arabia students, who completed a high-stakes standardized admission test for university entrance. The results from a Latent Class Analysis (LCA) revealed a three-class solution (i.e., high, average, and low scorers). Then, to better understand the nature of the emerging classes and the characteristics of the people who comprise them, we applied a new stepwise approach, using the Multiple Indicator Multiple Causes (MIMIC) model. The model identified both uniform and non-uniform DIF effects for several items across all scales of the test, although, for the majority of them, the DIF effect sizes were negligible. Findings from this study have important implications for both measurement quality and interpretation of the results. Particularly, results showed that gender is a potential source of DIF for latent class indicators; thus, it is important to include those direct effects in the latent class regression model, to obtain unbiased estimates not only for the measurement parameters but also of the structural parameters. Ignoring these effects might lead to misspecification of the latent classes in terms of both the size and the characteristics of each class, which in turn, could lead to misinterpretations of the obtained latent class results. Implications of the results for practice are discussed. © Copyright © 2020 Tsaousis, Sideridis and AlGhamdi
Protecting Animals 36: Author Witi Ihimaera
No full textIn this very special episode of Knowing Animals I am joined by beloved New Zealand author Witi Ihimaera. Witi has written many books featuring nonhuman animals. He offers us a non-colonial lens through which to think about the human/nonhuman relationship
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Examining differences in within-and between-person simple structures of an engineering qualification test using multilevel mimic structural equation modeling
No full textThe current study sought to meet three aims: (a) to understand the optimal factor structure of the Professional Engineering (ProfEng) test, a measure aiming to assess competency in engineering, within a multilevel (nested) perspective; (b) to examine the psychometric measurement invariance of the ProfEng test across levels due to nesting and across gender at the person level, and, (c) to examine the internal consistency of the engineering competency measure at both levels in the analysis. Data involved 1,696 individuals across 21 universities who took a national licensure test as part of the professional accreditation process to obtain a work permit and practice the engineering profession in the Kingdom of Saudi Arabia. Data were analyzed by use of Multilevel Structural Equation Modeling (MLSEM). Results indicated that a 2-factor model at both levels of analysis provided the best fit to the data. We also examined violation of measurement invariance across clusters (cluster bias). Results showed that all factor loadings were invariant across levels, suggesting the presence of strong measurement invariance. Last, invariance across gender was tested by use of the MIMIC multilevel model. Results pointed to the existence of significant differences between genders on levels of personal and professional skills with females having higher levels on personal skills and males on professional. Estimates of internal consistency reliability also varied markedly due to nesting. It is concluded that ignoring a multilevel structure is associated with errors and inaccuracies in the measurement of person abilities as both measurement wise and precision wise the multilevel model provides increased accuracy at each level in the analysis. © 2018 Tsaousis, Sideridis and Al-Harbi
An IRT-Multiple Indicators Multiple Causes (MIMIC) approach as a method of examining item response latency
No full textThe analysis of response time has received increasing attention during the last decades, since evidence from several studies supported the argument that there is a direct relationship between item response time and test performance. The aim of this study was to investigate whether item response latency affects person's ability parameters, in that it represents an adaptive or maladaptive practice. To examine the above research question data from 8,475 individuals completing the computerized version of the Postgraduate General Aptitude Test (PAGAT) were analyzed. To determine the extent to which response latency affects person's ability, we used a Multiple Indicators Multiple Causes (MIMIC) model, in which every item in a scale was linked to its corresponding covariate (i.e., item response latency). We ran the MIMIC model within the Item Response Theory (IRT) framework (2-PL model). The results supported the hypothesis that item response latency could provide valuable information for getting more accurate estimations for persons' ability levels. Results indicated that for individuals who invest more time on easy items, their likelihood of success does not improve, most likely because slow and fast responders have significantly different levels of ability (fast responders are of higher ability compared to slow responders). Consequently, investing more time for low ability individuals does not prove to be adaptive. The opposite was found for difficult items: individuals spending more time on difficult items increase their likelihood of success, more likely because they are high achievers (in difficult items individuals who spent more time were of significantly higher ability compared to fast responders). Thus, it appears that there is an interaction between the difficulty of the item and person abilities that explain the effects of response time on likelihood of success. We concluded that accommodating item response latency in a computerized assessment model, can inform test quality and test takers' behavior, and in that way, enhance score measurement accuracy. © 2018 Tsaousis, Sideridis and Al-Sadaawi
Assessing construct validity in math achievement: An application of Multilevel Structural Equation Modeling (MSEM)
No full textThe purpose of the present study was to model math achievement at both the person and university levels of the analyses in order to understand the optimal factor structure of math competency. Data involved 2,881 students who took a national mathematics examination as part of their entry at the university public system in Saudi Arabia. Four factors from the National math examination comprised the math achievement measure, namely, numbers and operations, algebra and analysis, geometry and measurement, and, statistics and probabilities. Data were analyzed using the aggregate method and by use of Multilevel Structural Equation Modeling (MSEM). Results indicated that both a unidimensional and a 4-factor correlated model fitted the data equally well using aggregate data, where for reasons of parsimony the unidimensional model was the preferred choice with these data. When modeling data including clustering, results pointed to alternative factor structures at the person and university levels. Thus, a unidimensional model provided the best fit at the University level, whereas a four-factor correlated model was most descriptive for person level data. The optimal simple structure was evaluated using the Ryu and West (2009) methodology for partially saturating the MSEM model and also met criteria for discriminant validation as described in Gorsuch (1983). Furthermore, a university level variable, namely the year of establishment, pointed to the superiority of older institutions with regard to math achievement. It is concluded that ignoring a multilevel structure in the data may result in erroneous conclusions with regard to the optimal factor structure and the tests of structural models following that. © 2018 Sideridis, Tsaousis and Al-Sadaawi
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