The goals of both exploratory and confirmatory factor analysis are described and procedural guidelines for each approach are summarized, emphasizing the use of factor analysis in developing and refining clinical measures For exploratory factor analysis, a rationale is presented for selecting between principal components analysis and common factor analysis depending on whether the research goal involves either identification of latent constructs or data reduction Confirmatory factor analysis using structural equation modeling is described for use in validating the dimensional structure of a measure Additionally, the uses of confirmatory factor analysis for assessing the invariance of measures across samples and for evaluating multitrait-multimethod data are also briefly described Suggestions are offered for handling common problems with item-level data, and examples illustrating potential difficulties with confirming dimensional structures from initial exploratory analyses are reviewed

http://psycnet.apa.org/journals/pas/7/3/286.pdf

Factor analysis in the development and refinement of clinical assessment instruments.

In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I(2) index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I(2) index are compared by means of a Monte Carlo simulation. The results show the utility of the I(2) index as a complement to the Q test, although it has the same problems of power with a small number of studies.

/pdf/assessing-heterogeneity-in-meta-analysis-q-statistic-or-i2-57zh1r444i.pdf

Assessing heterogeneity in meta-analysis: Q statistic or I2 index?

Procrastination is a prevalent and pernicious form of self-regulatory failure that is not entirely understood. Hence, the relevant conceptual, theoretical, and empirical work is reviewed, drawing upon correlational, experimental, and qualitative findings. A meta-analysis of procrastination’s possible causes and effects, based on 691 correlations, reveals that neuroticism, rebelliousness, and sensation seeking show only a weak connection. Strong and consistent predictors of procrastination were task aversiveness, task delay, selfefficacy, and impulsiveness, as well as conscientiousness and its facets of self-control, distractibility, organization, and achievement motivation. These effects prove consistent with temporal motivation theory, an integrative hybrid of expectancy theory and hyperbolic discounting. Continued research into procrastination should not be delayed, especially because its prevalence appears to be growing.

/pdf/the-nature-of-procrastination-a-meta-analytic-and-546ofkfr8u.pdf

The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure.

Learning style instruments are widely used but not enough is known about their reliability and validity and their impact on pedagogy in post-16 learning. This report documents work from a project commissioned by the Learning and Skills Development Agency (LSDA) to carry out an extensive review of research on post-16 learning styles, to evaluate the main models of learning styles, and to discuss the implications of learning styles for post-16 teaching and learning. The following research questions were addressed: What models of learning styles are influential and potentially influential? What empirical evidence is there to support the claims made for these models? What are the broad implications for pedagogy of these models? What empirical evidence is there that models of learning styles have an impact on students’ learning? The project identified the range of models that are available and influential or potentially influential in research and practice, located these models within identifiable ‘families’ of ideas about learning styles, evaluated the theories, claims and applications of these models, with a particular focus on evaluating the authors’ claims for reliability and validity, evaluated the claims made for the pedagogical implications of the selected models of learning styles, identified what gaps there are in current knowledge and what future research is needed in this area, and made recommendations and drew conclusions about the research field as a whole. In conclusion, the implications for pedagogy are drawn out and recommendations and conclusions are offered for practitioners, policymakers and the research community. The report concludes that it matters fundamentally which model is chosen. A second report (indexed at TD/TNC 79.72) discusses the appeal of learning styles as well as offering an overview of ways in which political and institutional contexts in the learning and skills sector affect the ways that learning styles might be put into practice.

/pdf/learning-styles-and-pedagogy-in-post-16-learning-a-2smhmr7m15.pdf

Learning styles and pedagogy in post-16 learning: a systematic and critical review

Previous meta-analytic examinations of group cohesion and performance have focused primarily on contextual factors. This study examined issues relevant to applied researchers by providing a more detailed analysis of the criterion domain. In addition, the authors reinvestigated the role of components of cohesion using more modern meta-analytic methods and in light of different types of performance criteria. The results of the authors’ meta-analyses revealed stronger correlations between cohesion and performance when performance was defined as behavior (as opposed to outcome), when it was assessed with efficiency measures (as opposed to effectiveness measures), and as patterns of team workflow became more intensive. In addition, and in contrast to B. Mullen and C. Copper’s (1994) meta-analysis, the 3 main components of cohesion were independently related to the various performance domains. Implications for organizations and future research on cohesion and performance are discussed.

https://psycnet.apa.org/journals/apl/88/6/989.pdf

Cohesion and performance in groups: A meta-analytic clarification of construct relations

lpsative measures are multiple measures, where the data are collected, or are modified, in such a way that all subject totals across the measures are equal. Much has been written about factor analysis with such data, however, no clear consensus has been reached regarding the suitability of ipsative measures for factor analysis. The purpose of the present article is to show analytically the fundamental problems that ipsative measures impose for factor analysis. The expected value of the correlation between ipsative measures is shown to equal - 1/ ( k - I), where k is the number of measures. The rank of the resulting correlation matrix is reduced by one to k - 1, and ipsativity alone produces k - 1 artifactual bipolar factors, which will obscure any actual interrelations among the measures. If the data are known to be ipsative or if the tell-tale signs of ipsativity are seen, factor analysis should not be done.

Factor Analysis of Ipsative Measures

Saville & Willson (1991), in this journal, concluded, contrary to many researchers and methodologists, that ipsative scales can be soundly factored and can be viewed as equally valid alternatives to normative scales. We present evidence based on analysis of theoretical and actual data to demonstrate that ipsative scales, although sometimes highly correlated with their normative counterparts, cannot be factored and, when factored, result in predictable factor loading patterns not related to their normative counterparts' factor patterns, and, consequently, will result in invalid factor interpretations. An example is presented where ipsative scores are appropriately and successfully analysed using multinomial statistical techniques. These techniques are more appropriate for ipsative data because ipsative scores contain only categorical information across individuals.

On the questionable soundness of factoring ipsative data: A response to Saville & Willson (1991)

Kolb's earlier (1976) Learning Style Inventory (LSI) has been criticized for its poor reliability and validity. Even after revisions (1985) were made and a new instrument was developed, the test-re...

Factor analysis of the 1985 revision of Kolb's learning style inventory

The learning status of 95 diabetic boys and girls and 97 matched controls was evaluated using the Wechsler Intelligence Scale for Children--Revised IQ factors and school histories. Of interest was whether diabetic boys would evidence more learning difficulties. Results indicated that diabetic boys had significantly lower Freedom From Distractibility scores compared with scores of diabetic girls and control Ss and lower Perceptual Organization scores compared with scores of control boys. Although group scores were still within the average range of functioning, a significantly high percentage of diabetic boys (40%) compared with diabetic girls (16%) had learning problems that warranted either special instructional services or grade retention. Diabetic children experienced more learning difficulties (24%) than controls (13%), supporting research findings that diabetes is associated with increased risk of learning problems.

Gender differences in the learning status of diabetic children.

Many Monte Carlo studies have evaluated the Schmidt and Hunter (SH thus, the S&H procedures do not find variance among rhos when it exists (Type II error) and also have a high Type I error rate under all conditions simulated. These re...

Power and Accuracy of the Schmidt and Hunter Meta-Analytic Procedures:

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