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Sampling distributions are fundamental for statistical inference, yet their abstract nature poses challenges for students. This research investigates the development of high school students' conceptions of sampling distribution through informal significance tests with the aid of digital technology. The study focuses on how technological tools contribute to forming these conceptions, guided by an emerging theory that describes this process. A workshop for high school students was organized, involving 36 participants working in pairs across four sessions, each with access to a computer. These sessions involved problem-solving activities, with the teacher introducing key concepts in the initial three sessions. The analysis, employing grounded theory, aimed to characterize the nature of students' conceptions of sampling distribution as evident in their responses. The findings reveal a transition from empirical to informal conceptions of sampling distribution among students, facilitated by computational mediation. This transition is marked by an abstraction process that includes mathematization, processing, uncertainty/randomness, and conditional reasoning. The study underscores the role of digital simulations in teaching statistical concepts, facilitating students' conceptual shift critical for grasping statistical inference.

With an educational issue that has caught the attention of many countries in the world (study load), a population of 8th graders from a typical Chinese metropolitan city (40,536 from 118 schools), and an advanced statistical strategy (multilevel piecewise regression), we examined whether there was a turning point in terms of the effects of study load on science achievement. We did identify a turning point for each and every measure of study load. For weekday learning on science achievement, we identified a turning point of 22.50 hr for the effects of in-school learning, 7.50 hr for the effects of homework, and 12 hr for the effects of after-school learning. For weekend learning on science achievement, we identified a turning point of 1.50 hr for the effects of in-school learning, 5 hr for the effects of homework, and 1 hr for the effects of after-school learning. In each case, the difference in effects before and after the turning point was statistically significant, indicating that the effects of study load on science achievement were nonlinear. All of these turning points offered important implications for science education.

Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or limitations in different students. Consequently, understanding the connections between students' cognitive functions and CT skills assumes pivotal importance in the design of personalized instructional strategies for CT. Despite a general consideration of learning variability in CT education, empirical insights exploring the correlation between cognitive skills and CT competencies remain notably scarce. This study endeavors to bridge this research gap by investigating the links between executive functions and CT skills, as well as the associations between their sub-dimensions. The results reveal a statistically significant correlation coefficient of 0.452 between these two domains, underscoring the notable connection between executive functions and CT abilities. Furthermore, the sub-dimensional analysis offers a comprehensive understanding of how specific executive functions uniquely contribute to certain CT skills. In light of these findings, this research offers a promising pathway for the development of tailored CT education programs that can cater to the unique needs of each individual, ultimately facilitating inclusive CT programs and making significant contributions to broaden STEM education and future workforce.

The purpose of the current study was to conduct a thorough review of the literature on the interview-informed synthesized contingency analysis (IISCA) developed by (Hanley et al., Journal of Applied Behavior Analysis 47:16-36, 2014)) and its subsequent treatment outcomes. A total of 39 articles were identified with 235 participants participating in 293 synthesized contingency analyses (SCAs) and 111 treatment evaluations. Results indicated that 95.56% of identified SCAs were reported to be differentiated. Similarly, reductions in problem behavior were seen in all 111 treatment analyses. Results of the current review, including effect size measures (i.e., Tau-U and Hedge's g), indicate that the IISCA and function-based interventions developed from the results of IISCA produce statistically significant results. Limitations and future directions are also discussed.

School absences can negatively impact a child's schooling, including the loss of teacher-led lessons, peer interactions, and, ultimately, academic achievement. However, little is known about the long-term consequences of school absences for overall educational attainment and labour market outcomes. In this paper, we used data from the 1970 British Cohort Study to examine long-term associations between school absences in late childhood and individuals' educational attainment, social class, unemployment and earnings at age 42 while adjusting for a comprehensive set of confounders. Our findings show that school absences are associated with lower educational attainment but are not associated with social class destination except for increasing the likelihood of being out of the labour force after adjusting for confounders. Individuals who missed five days of school at age 10 were 5.2% more likely to have obtained no qualifications and 4.1% more likely to be out of the labour force. However, we did not find a significant association between school absences and individuals' earnings or duration of unemployment. Our findings suggest that the detrimental consequences of school absences persist beyond schooling into adulthood. Overall, this study highlights the importance of addressing school absenteeism to promote educational and labour market participation over the life course.

A growing body of literature suggests manipulating some graphing conventions can affect visual analysis and adherence to graphing conventions varies widely in single case design publications. Yet use of graphing conventions by the wider population of behavior analysts is largely unexplored. The current study examined relations among types of graphing conventions (established versus unestablished functions), demographic variables (e.g., education level, primary job setting), and likelihood of revision ratings reported by 631 board certified behavior analysts (BCBAs, BCaBAs, and BCBA-Ds). Statistically significant differences in ratings were detected across types of conventions and demographic categories. Master's level BCBAs in clinical roles were more likely to revise conventions that affect visual analysis than conventions whose functions are unknown, whereas there were no differences between types of conventions for doctoral level BCBAs in academic roles. We discuss these findings in the context of audience control of graphing conventions.

The purpose of this study was to determine which of the input and output variables made a statistically significant difference in the efficiency status of undergraduate departments offering Health Management education in Turkey, to identify areas where inefficient departments could improve, and to determine which of the input and output variables made a statistically significant difference in the efficiency status of inefficient departments. The output-oriented data envelopment analysis model was employed in the efficiency analysis. As a decision-making unit, there were 43 Health Management departments. Mann-Whitney U test was used to analyse whether or not there was a statistically significant difference between the efficiencies of the departments based on the input and output variables. In Turkey, 15 of 43 state universities providing formal undergraduate education in the field of Health Management are fully efficient. Also, there was a significant difference in terms of the "number of completed projects" and the "number of papers in journals screened within the scope of Web of Science Core Collection (WOSCC)" variables. No study examining the efficiency of Health Management departments has been found in the literature. Department administrators are encouraged to increase their publications and look into ways to design more initiatives.

Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and non-parametric tests when each Likert-type item was analyzed independently in survey studies. The main purpose of the study is to examine the statistical power of Wilcoxon-Mann-Whitney, Welch's t, and Student's t tests for Likert-type data, which are pairwise comparison tests. For this purpose, a Monte Carlo simulation study was conducted. The statistical significance of the selected tests was examined under the conditions of sample size, group size ratio, and effect size. The results showed that the Wilcoxon-Mann-Whitney test was superior to its counterparts, especially for small samples and unequal group sizes. However, the Student's t-test for Likert-type data had similar statistical power to the Wilcoxon-Mann-Whitney test under conditions of equal group sizes when the sample size was 200 or more. Consistent with the empirical results, practical recommendations were provided for researchers on what to consider when collecting and analyzing Likert-type data.

This study aims at highlighting the relationship between mastery learning models and academic performance in mathematics, moderated by the number of hours allotted to studying mathematics. There are 305 first to eighth-grade students who learn at "Nae A. Ghica Middle School" in Romania. Students in sixth, seventh, and eighth grades (n=101) were selected to participate in this research because they study both algebra and geometry. We have used purposive sampling to control certain variables, i.e., mastery learning strategies and academic performance. The data were analyzed in Jamovi (2022). The level of performance was measured according to the progress students made. Data analysis revealed that the moderation effect of the time each student allocated to studying mathematics individually is not statistically significant in the relationship between the mastery learning model and academic performance in mathematics. This study was conducted on a small number of students, so it is recommended to be expanded on a larger scale.

Using data from multiple informants has long been considered best practice in education. However, multiple informants often disagree on similar constructs, complicating decision-making. Polynomial regression and response-surface analysis (PRA) is often used to test the congruence effect between multiple informants on an outcome. However, PRA assumes the congruence effect on the outcome is homogeneous across individuals. To investigate unknown heterogeneity in the congruence effect, we introduce polynomial regression mixture modeling (PRMix). We demonstrate how PRMix enhances congruence research by allowing researchers to investigate individual differences in the congruence effect. The differential congruence effects between latent classes are illustrated with response-surface plots. We provide practical suggestions for using PRMix and discuss future research directions in methodology.

The 0.05 boundary within Null Hypothesis Statistical Testing (NHST) "has made a lot of people very angry and been widely regarded as a bad move" (to quote Douglas Adams). Here, we move past meta-scientific arguments and ask an empirical question: What is the psychological standing of the 0.05 boundary for statistical significance? We find that graduate students in the psychological sciences show a "boundary effect" when relating p-values across 0.05. We propose this psychological boundary is learned through statistical training in NHST and reading a scientific literature replete with "statistical significance". Consistent with this proposal, undergraduates do not show the same sensitivity to the 0.05 boundary. Additionally, the size of a graduate student's boundary effect is not associated with their explicit endorsement of questionable research practices. These findings suggest that training creates distortions in initial processing of p-values, but these might be dampened through scientific processes operating over longer timescales.

The performance of White students and Hispanic students from California, Texas, and Arizona on the advanced placement (AP) biology exam was compared using archival data from the College Board from 2016 through 2019. Pearson Chi-square tests yielded statistically significant differences in all 4 year comparisons of white and Hispanic students in all three states. White students from California had the highest percentage of students earn a 3 or higher for all 4 years, Arizona had the second highest percentage of white students earn a 3 or higher for all 4 years, and Texas had the lowest percentage of white students earn a 3 or higher for all 4 years of comparison. Hispanic students from Arizona had the highest percentage of students earn a 3 or higher for all 4 years, California had the second highest percentage of Hispanic students earn a 3 or higher for all 4 years, and Texas had the lowest percentage of Hispanic students earn a 3 or higher for all 4 years of comparison. It was discovered a majority of White students who took an AP biology exam, from 2016 to 2019, earned a passing score (3 or higher), while the majority of Hispanic students who took an AP Biology exam failed to earn a passing score (3 or higher), that would result in college credit. Implications of these findings are discussed.

Early intervention efforts have been effective in reducing disruptive behaviors and the probability of poor developmental outcomes. Early interventions include common practice elements to improve social functioning and decrease problem behaviors that disrupt the teaching-learning process. The First Step Next intervention has been well validated across early childhood and early elementary settings. In the present article, we provide a best-evidence synthesis of five randomized controlled replication studies between 2009 and 2021. Collectively, these studies show the intervention has resulted in small to large effect sizes and statistically significant improvements, compared with students randomized to control conditions, on multiple indicators of prosocial and problem behavior. The current synthesis focuses on a range of outcomes across elementary and preschool populations, as well as implementation fidelity and social validity ratings, to help school professionals in determining whether First Step Next might be useful in their efforts to support students at risk of school failure.

To detect differential item functioning (DIF), Rasch trees search for optimal split-points in covariates and identify subgroups of respondents in a data-driven way. To determine whether and in which covariate a split should be performed, Rasch trees use statistical significance tests. Consequently, Rasch trees are more likely to label small DIF effects as significant in larger samples. This leads to larger trees, which split the sample into more subgroups. What would be more desirable is an approach that is driven more by effect size rather than sample size. In order to achieve this, we suggest to implement an additional stopping criterion: the popular Educational Testing Service (ETS) classification scheme based on the Mantel-Haenszel odds ratio. This criterion helps us to evaluate whether a split in a Rasch tree is based on a substantial or an ignorable difference in item parameters, and it allows the Rasch tree to stop growing when DIF between the identified subgroups is small. Furthermore, it supports identifying DIF items and quantifying DIF effect sizes in each split. Based on simulation results, we conclude that the Mantel-Haenszel effect size further reduces unnecessary splits in Rasch trees under the null hypothesis, or when the sample size is large but DIF effects are negligible. To make the stopping criterion easy-to-use for applied researchers, we have implemented the procedure in the statistical software R. Finally, we discuss how DIF effects between different nodes in a Rasch tree can be interpreted and emphasize the importance of purification strategies for the Mantel-Haenszel procedure on tree stopping and DIF item classification.

The modality and redundancy principles are two fundamental principles used to inform the design of multimedia instruction. They are based on a variety of experimental studies that utilized different types of multimedia lessons to compare input modes of graphics+audio, graphics+text, and graphics+audio+text with each other. However, a lack of control of multimedia lessons in previous studies creates a threat to validity because a single case scenario without following certain principles is not sufficient to represent a construct. Therefore, this study addressed this inherent validity threat and reinvestigated the applicability of the modality and redundancy principles when students learned during a controlled multimedia lesson. In this study the multimedia lesson was developed to follow a series of multimedia learning principles. These principles ensured that the lesson was representative of different types of multimedia lessons. Additionally, they ensured that the multimedia lesson was conducive to learning, since those that were not helpful would not be utilized for instruction in the first place. Eighty-six students in a research university in the US took a prior knowledge survey. They were then randomly assigned to the three input mode conditions and watched the multimedia lesson about the formation of lightning. Subsequent retention and transfer tests revealed that there were no statistically significant differences among the three input mode conditions. Therefore, both the redundancy and modality effects disappeared. This study provided an updated understanding of the applicability of the two important principles for multimedia instruction. Limitations and implications were discussed.

As a replication in the Chinese context of previous aptitude testing validation studies, this research aims at exploring the validity of an interpreting aptitude test battery, composed of the Logical Memory Test (LMT), the Scale of Language Anxiety (SLA) and the Achievement Motivation Test (AMT), in the prediction of consecutive interpreting performance of Chinese interpreting trainees. A quasi-experimental and correlational research design was adopted. The aptitude test battery was administered at the beginning of the first semester for 48 post-graduate interpreting trainees, and English-Chinese (E-C) and Chinese-English (C-E) consecutive interpreting (CI) tests, used to measure the interpreting proficiency of the subjects, were conducted at the end of the semester. Scores from the aptitude tests and consecutive interpreting tests were subjected to parametric correlation analysis as well as multiple linear regression analysis. The results indicated that (a) the LMT scores enjoyed a statistically significant positive correlation with C-E CI; (b) the SLA scores had a statistically significant negative correlation with both E-C and C-E CI; (c) there was no statistically significant correlation between the AMT scores and CI in either direction. Implications of the findings are discussed in relation to interpreting candidate screening and admission testing.

This paper presents the importance of the visual expression of factor decomposition in regression analysis, which is particularly worthwhile for undergraduate students whose majors are not mathematics but social science. The conventional purpose of regression analysis is to examine specific hypotheses empirically. In particular, the statistical significance of the explanatory variable was tested, which may have been difficult for many students to understand mathematically. To remedy this, factor decomposition is introduced in the same way that human body composition is broken down into water, fat, and muscle. As an illustrative example, multiple regression was applied to the determinants of housing rents in Japan. The explanatory variables were the living area, building age, and walking time from the nearest station. The findings suggest that, with the help of visual expression, a student can easily appreciate which variable significantly affects housing rents.

The residual differential item functioning (RDIF) detection framework was developed recently under a linear testing context. To explore the potential application of this framework to computerized adaptive testing (CAT), the present study investigated the utility of the RDIF[subscript R] statistic both as an index for detecting uniform DIF of pretest items in CAT and as a direct measure of the effect size of uniform DIF. Extensive CAT simulations revealed RDIF[subscript R] to have well-controlled Type I error and slightly higher power to detect uniform DIF compared with CATSIB, especially when pretest items were calibrated using fixed-item parameter calibration. Moreover, RDIF[subscript R] accurately estimated the amount of uniform DIF irrespective of the presence of impact. Therefore, RDIF[subscript R] demonstrates its potential as a useful tool for evaluating both the statistical and practical significance of uniform DIF in CAT.

Randomized controlled trials have proliferated in education, in part because they provide an unbiased estimator for the causal impact of interventions. It is increasingly recognized that many such trials in education have low power to detect an effect if indeed there is one. However, it is less well known that low powered trials tend to systematically exaggerate effect sizes among the subset of interventions that show promising results (p < [alpha]) We conduct a retrospective design analysis to quantify this bias across 22 such promising trials, finding that the estimated effect sizes are exaggerated by an average of 52% or more. Promising trial bias can be reduced ex-ante by increasing the power of the trials that are commissioned and guarded against ex-post by including estimates of the exaggeration ratio when reporting trial findings. Our results also suggest that challenges around implementation fidelity are not the only reason that apparently successful interventions often fail to subsequently scale up. Instead, the effect from the initial promising trial may simply be exaggerated.

It is time to reform the quantitative methods courses in leadership programs -- typically, these are statistics courses with arcane statistics textbooks. There is growing evidence that these "rigorous" scientific methods actually mislead practice because the vast majority of practices found to be "effective" or "evidence-based" using these methods do not work in schools -- including those validated by the federal What Works Clearinghouse. Fortunately, there are other quantitative methods that are more intuitive, and more accessible to leaders, leadership students, and leadership faculty -- methods that are more relevant for improving practice and identifying "evidence-based" practices that are likely to actually improve schools. However, those who control the content of quantitative methods courses tend to be those with the most technical expertise in the traditional methods and will oppose any methodological alternatives as being "non-rigorous." The newer quantitative methods will not be emphasized in EdD and Masters programs until all leadership faculty exert greater voice on the content of such courses. A first step is to stop calling quantitative methods courses "Statistics" courses. They should be "Applied Quantitative Methods" courses. This non-technical article (a) outlines the problems with traditional statistics, (b) highlights some of the newer and simpler quantitative methods that are more relevant for improving schools, and (c) describes an alternative textbook as a key resource for transforming the quantitative methods course.

The present study aimed at clarifying the various shortcomings of the Cohen's criteria for the interpretation of the values of the practical significance indicators. The hypothetical data were used for two experimental and control groups and calculating the paired-samples t-test. To clarify the inadequacy of Cohen's criteria in interpreting practical significance indicators, it was compared with another criterion which is Black's Modified Gain Ratio. Through the compatibility of mathematical equations to calculate the practical significance and the values of the interpretations of the correlation coefficient, the present study suggested that a criterion for the practical significance should be as follows: small when the values of the index (d) are less than (0.631), medium when the values are between 0.631 and 1.50 and large when the values are equal to or greater than (1.51). The study showed the justifications that distinguish this criterion from the Cohen criterion.

This study aimed to recognize the differences in multiple intelligences among the students of Jordan University of Science and Technology with its relationship to gender, academic year, and achievement. The study sample consisted of 349 male and female participants. It used a descriptive analysis, the MacKenzie scale for multiple intelligences, which consisted of 9 dimensions and 10 paragraphs per dimension. According to its findings, the average range for all domains of multiple intelligences was 3.70. This value implied that the average students' responses were accepted. It showed there was not any statistically significant differences at x [greater than or equal to] 0.05 in the students' results which could be attributed to gender and academic year. Also, there were not statistically significant differences at x [greater than or equal to] 0.05 in the students' results for the musical and existential dimension attributed to achievement and there were statistically significant differences for the other dimensions.

We investigate two non-iterative estimation procedures for Rasch models, the pair-wise estimation procedure (PAIR) and the Eigenvector method (EVM), and identify theoretical issues with EVM for rating scale model (RSM) threshold estimation. We develop a new procedure to resolve these issues--the conditional pairwise adjacent thresholds procedure (CPAT)--and test the methods using a large number of simulated datasets to compare the estimates against known generating parameters. We find support for our hypotheses, in particular that EVM threshold estimates suffer from theoretical issues which lead to biased estimates and that CPAT represents a means of resolving these issues. These findings are both statistically significant (p < 0.001) and of a large effect size. We conclude that CPAT deserves serious consideration as a conditional, computationally efficient approach to Rasch parameter estimation for the RSM. CPAT has particular potential for use in contexts where computational load may be an issue, such as systems with multiple online algorithms and large test banks with sparse data designs.

Some Cochrane reviews were declared stable or closed, that is, not in need of updating. For some of them, it has been declared that conclusions will not (or it is unlikely they will) change with further studies. We explored whether there is a discernable decision-making pattern for decisions about the conclusiveness and stabilization of these reviews. We analyzed Cochrane reviews published until April 2020 labeled as stable or closed. We extracted the rationale leading to the decision declaring that the conclusion is not expected to change with further studies. Furthermore, we assessed whether the reviews used GRADE analysis. We extracted data from summary of findings (SoF) tables on the direction of effect, statistical significance, and I[superscript 2] values for the first and primary outcomes in SoFs, conclusions in the abstract and review, and implications for practice and future research. We included 40 stable/closed Cochrane reviews. Rationales for their stabilization did not enable any insight into the Cochrane's decision-making algorithm for considering the evidence as conclusive. Among 191 outcomes presented in the SoFs, 70% were rated with either low or very low certainty evidence. None of the reviews mentioned in the text that the review should be stabilized or closed, or that there is sufficient evidence on the subject. Reasons for stabilizing/closing Cochrane reviews were unclear, and we could not discern any pattern of "conclusive review" traits. Definition of systematic review conclusiveness is still lacking, which may contribute to research waste.

Formal empirical assessments of replication have recently become more prominent in several areas of science, including psychology. These assessments have used different statistical approaches to determine if a finding has been replicated. The purpose of this article is to provide several alternative conceptual frameworks that lead to different statistical analyses to test hypotheses about replication. All of these analyses are based on statistical methods used in meta-analysis. The differences among the methods described involve whether the burden of proof is placed on replication or nonreplication, whether replication is exact or allows for a small amount of "negligible heterogeneity," and whether the studies observed are assumed to be fixed (constituting the entire body of relevant evidence) or are a sample from a universe of possibly relevant studies. The statistical power of each of these tests is computed and shown to be low in many cases, raising issues of the interpretability of tests for replication.

We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm--and the p-value thresholds intrinsic to it--as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to "ban" p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.

Using the Hartung-Knapp method and 95% prediction intervals (PIs) in random-effects meta-analyses is recommended by experts but rarely applied. Therefore, we aimed to reevaluate statistically significant meta-analyses using the Hartung-Knapp method and 95% PIs. In this methodological study, three databases were searched from January 2010 to July 2019. We included systematic reviews reporting a statistically significant meta-analysis of at least four randomized controlled trials in advanced cancer patients using either a fixed-effect or random-effects model. We investigated the impact of switching from fixed-effect to random-effects meta-analysis and of using the recommended Hartung-Knapp method in random-effects meta-analyses. Furthermore, we calculated 95% PIs for all included meta-analyses. We identified 6234 hits, of which 261 statistically significant meta-analyses were included. Our recalculations of these 261 meta-analyses produced statistically significant results in 132 of 138 fixed-effect and 114 of 123 random-effects meta-analyses. When switching to a random-effects model, 19 of 132 fixed-effect meta-analyses (14.4%) were no longer statistically significant. Using the Hartung-Knapp method in random-effects meta-analyses resulted in 34 of 114 nonsignificant meta-analyses (29.8%). In the full sample (N = 261), the "null effect" was included by the 95% PI in 195 (74.7%) and the "opposite effect" (e.g., hazard ratio 0.5, opposite effect 2) in 98 meta-analyses (37.5%). Using the Hartung-Knapp method and PIs substantially influenced the interpretation of many published, statistically significant meta-analyses. We strongly encourage researchers to check if using the Hartung-Knapp method and reporting 95% PIs is appropriate in random-effects meta-analyses.

Many experimental designs in educational and behavioral research involve at least one level of clustering. Clustering affects the precision of estimators and its impact on statistics in cross-sectional studies is well known. Clustering also occurs in longitudinal designs where students that are initially grouped may be regrouped in the following year and regrouped again in subsequent years. The purpose of this article is to explore the clustering effects of multiple waves of grouping on the effect sizes of downstream outcomes; that is, outcomes that are measured after one or more waves of regrouping. We center our framework on the longitudinal cluster randomized trial in which treatments are initially assigned by clusters (such as classrooms) and students are then regrouped into different clusters in subsequent years. This article illustrates how information on the intraclass correlations in longitudinal studies can be used to correct for biases in the effect sizes and variances, and how it can also be used to adjust the significance test for the effects of longitudinal clustering. This article also provides the first empirical evidence of the magnitude of longitudinal clustering effects using data from a state longitudinal data system.

School accountability systems increasingly hold schools to account for their performances using value-added models purporting to measure the effects of schools on student learning. The most common approach is to fit a linear regression of student current achievement on student prior achievement, where the school effects are the school means of the predicted residuals. In the literature, further adjustments are usually made for student sociodemographics and sometimes school composition and "non-malleable" characteristics. However, accountability systems typically make fewer adjustments: for transparency to end users, because data are unavailable or of insufficient quality, or for ideological reasons. There is therefore considerable interest in understanding the extent to which simpler models give similar school effects to more theoretically justified but complex models. We explore these issues via a case study and empirical analysis of England's "Progress 8" secondary school accountability system.

A growing segment of the U.S. workforce--older workers--face challenges such as a greater chance of being unemployed for a longer period of time than younger workers and skill obsolescence in contemporary societies. In an age of fast-paced economic and technological change, ongoing investment in human capital in the form of skill training represents a potential strategy for improving older workers' employment prospects. However, empirical evidence is lacking on the relationship between basic skill competencies and employment outcomes for older workers in general. This study analyzed nationally representative data of adults aged 45 to 65 years (n = 2,169) from the Program for the International Assessment of Adult Competencies. Literacy skills were used as the main measure of competencies. Results from multinomial logistic regression identified statistically significant positive associations between literacy skills and employment among older workers. Implications for research, policy, and practice are discussed.

This study aims to develop a competitiveness assessment index for global aviation training organizations. The study utilizes the four factors of the Diamond model developed by Michael Porter. To select candidate indices, expert consultations were made, and two stages of Delphi survey were conducted on 24 experts in the area of global aviation training and education. Finally, 19 indices were selected for analysis. In order to assess the reliability of the final indices and justify selection, an empirical study was conducted with 100 participants, yielding statistically significant results. The 19 indices were organized into a three-level hierarchy system, and an analytical hierarchy process (AHP) was conducted to evaluate the importance of each index. The results of the AHP indicated that 'quality of instructors' was the most important index, followed by 'feedback from trainees for the course (level of satisfaction with the course)' and 'director's management skills and leadership'. This study will contribute to enhancing the competitiveness of global aviation training organizations.

Existential intelligence is often neglected in literature, especially at the tertiary level. Therefore this study aims to identify the degree of existential intelligence in a sample of graduate students at the World Islamic Sciences University in Jordan. In addition, the study aims to find out whether this degree differs according to a number of variables. The study sample comprised 56 male and female Faculty of Educational Sciences graduate students of 2017/2018 academic year. In order to achieve the objectives of the study, existential intelligence scale was used after the scale's validity and reliability was examined. The results indicated that the total degree of existential intelligence among graduate students at the Faculty of Educational Sciences at World Islamic Sciences University was average (medium). Furthermore, the results revealed that there were no statistically significant differences in the degree of existential intelligence due to gender, specialization and years of experience, marital status, and work position variables at the significance level of p = 0.05.

This study aimed to identify the attitudes of university faculty members and students towards the use of the Learning Management System (LMS) in teaching and learning. The descriptive analytical approach was used, and the data were collected using two different tools constructed by the researchers. The first is related to the attitudes of faculty members towards the use of the Learning Management System in teaching, and the other is linked to the students' attitudes towards using the Learning Management System in learning. The sample included 95 university faculty members and 307 students. Calculation Means, standard deviations and ANOVA were calculated. The study revealed that the attitudes of university faculty members and students towards using the Learning Management System in teaching and learning were positive. The results showed statistically significant differences in the attitudes of university faculty members due to gender and in favor of the males. The results did not show significant statistical differences due to the faculty members' experience and the academic track. The results also indicated that there were no statistically significant differences in students' attitudes towards using the Learning Management System in learning due to their gender. Nonetheless, the results indicated that there were statistically significant differences in students' attitudes due to the academic year and for the first-year students. The positive attitudes were decreasing as the years gradually progressed. The results also evidenced differences of statistical significance in students' attitudes towards the Learning Management System attributed to the academic track and for the benefit of applied and humanities studies and then the medical track and finally the engineering track. In the light of these findings, the researchers recommended intensive training of university faculty members and students in the employment of the Learning Management System in an optimal way to increase its effectiveness in teaching and learning. They also pressed the need for conducting further studies on the impediments facing the parties in the use of the Learning Management System and revealing the ways to solve them.

The aim of this study is twofold. First, it aims to investigate the impact of the Flipped Classroom (FC) Model on students' academic achievement. Second, it reveals the students' opinions about the model itself. For four weeks, the students in the experimental group were taught in a blended learning context where the FC Model was applied, while the lessons in the control group were carried out through traditional blended learning. Both groups were administered a test before and after the Flipped Classroom sessions. To analyze the data, a two-way ANOVA for Mixed Measures was conducted to compare the means of test scores of each group. The results showed that there were no statistically significant differences between the scores of the two groups. Coming to classes prepared and completing the assignments in class, so that students did not need to do assignments at home, were among the positive aspects of the FC Model. The problems encountered in this model, however, are categorized under three main titles: Motivation, Content, and Learning. At the end of the study, the advantages and disadvantages of the FC Model are identified in accordance with the participants' opinions, and necessary suggestions made.

In recent years, researchers have shown an increased interest in learner styles as one of the key factors influencing learning generally. During the last decade we have also noticed rapid advances in the field of technology enhanced learning and growing trend towards its application in formal education. Introduction of different forms of e-learning (e.g. computer assisted learning, blended learning, massive online open courses (MOOCs), etc.) has become reality at most universities. The principal objective of the research presented in the study was to find out whether learning styles of students and the form of teaching generate interaction effects on their learning achievements. Grasha-Riechmann Student Learning Style Scales (GRSLSS) inventory was applied to define the learner styles of the sample subjects and pedagogical quasi experiment was conducted within the period of one semester. The research used a convenience sample of 81 fifth year university students (pre-service English language teachers) split into control (n = 55) and experimental (n = 27) groups. The results of a two-way analysis of variance (ANOVA) showed no significant findings. The results indicate that the learning styles and form of teaching have no effect on academic achievement.

The study aimed to investigate the effects of project-based learning on student teacher self-efficacy and achievement. This study used a quasi-experimental research design with pre-test and post-test control groups. The study sample consisted of 79 students, 42 in the control group and 37 in the experimental group. To collect data, two instruments were used: Teachers' Sense of Efficacy Scale and Academic Achievement Test. The results showed that statistically significant differences between experimental and control group in self-efficacy and achievement score due to the Project-Based Learning in favor of the experimental group. Based on the results, the researchers recommended adopting the Project-Based Learning in teaching and learning situations, besides conducting more studies to verify the use of Project-Based Learning versus traditional methods in Arab countries.

In this study, the job satisfaction levels of teachers working with individuals in need of special education were examined with regard to certain variables. General survey model was used in the study and the study group of this research was comprised of teachers from all over Turkey who work at special education schools, integration and special education classrooms. The job satisfaction scale developed by Sahin (1999) was used in the study. The scale was developed using Google and administered online on December 08, 2017 and December 30, 2017. A total of 465 teachers participated in the study. SPSS 22 software was used for data analysis. Besides, descriptive statistics, independent sample t-test and one-way ANOVA test were used for data analysis. As for the results of the study, while no statistically significant difference was found in terms of gender, number of working years, considered income level, and age, statistically significant differences were observed according to salary, receiving support from colleagues, receiving praise from administrators, finding working at special education schools convenient variables.

This study aims to examine the World Islamic Sciences and Education University students' attitude toward the inclusion of handicapped students within the university environment. The sample of the study consists of 247 male and female undergraduate students of the World Islamic and Education University at the academic year 2017/2018. To achieve the objectives of the study, a valid and reliable scale of World Islamic University students' attitudes toward special needs students' inclusion in campus was prepared and applied. The data analysis revealed that the level of the attitudes of World Islamic University students toward handicapped students' inclusion within campus was medium. The study findings revealed that there was a statistically significant difference in the participants' attitudes toward the handicapped inclusion in the mainstream of World Islamic University based on gender in favor of male students; and there were statistically significant differences based on academic level in favor of third and fourth year students regarding the inclusion of handicapped in the mainstream campus. In addition, there is a statistically significant difference in students' attitudes based on faculty variable. The study findings revealed there are no statistically significant differences in students' attitude based on social status variable.

This study was conducted to determine the effects of I Can Problem Solve Program (ICPS) on preschool children's perspective taking skills. Participants were 51 children who were attending two public preschool. In the study, pretest-posttest-retention test control group desing was used. There were an experimental group and two control groups in the study. As data collection tool, "Perspective Taking Test for Children (PTC)" was utilized. Firstly, PTC was applied to children as pretest. Then "I Can Problem Solve Program" was applied to children in experimental group. Children in control groups were attend the regular program. After the completing the program application PTC was implemented to experimental group and control groups as posttest. Finally, PTC was applied to experimental group as retention test after a month of posttest. At the end of the study, it was determined that I Can Problem Solve Program had a significant impact on children's perspective taking skills. There was a statistically significant difference between experimental group and control groups in posttest, while there was no difference between the groups in prettest. Children in experimental group performed better than children in control groups in posttest. Moreover, there was no significant difference in experimental group's scores between posttest and retention test scores.

Teacher-mediated classroom assessment might have significant impacts on learners' academic achievements and teachers' development. The current study investigated teachers' assessment literacy and its impact on their current assessment practices and learners' writing outcomes. The study sought to gain an understanding of the extent to which teachers' assessment literacy affects their practices and their learners' outcomes. To conduct the study and gather the required data, the researchers employed teachers' assessment literacy inventory, semi-structured interview, non-participatory observation, and Writing Competence Rating Scale (WCRS). Ten male EFL instructors and 75 male sophomores from Iranian EFL contexts were selected from four language schools in Iran. The results of the study indicated that teachers' assessment literacy has a statistically significant impact on learners' writing achievements and teachers' assessment awareness leads teaching environments into effective and motivated assessment design. These findings suggest language educators considering teachers' assessment awareness in their teacher education programs.