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Chi Kit Jacky Ng; Lok Yin Joyce Kwan; Wai Chan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
In the past decade, moderated mediation analysis has been extensively and increasingly employed in social and behavioral sciences. With its widespread use, it is particularly important to ensure the moderated mediation analysis will not bring spurious results. Spurious effects have been studied in both mediation and moderation analysis, but this…
Descriptors: Mediation Theory, Social Sciences, Behavioral Sciences, Predictor Variables
Bodner, Todd E. – Journal of Educational and Behavioral Statistics, 2016
This article revisits how the end points of plotted line segments should be selected when graphing interactions involving a continuous target predictor variable. Under the standard approach, end points are chosen at ±1 or 2 standard deviations from the target predictor mean. However, when the target predictor and moderator are correlated or the…
Descriptors: Graphs, Multiple Regression Analysis, Predictor Variables, Correlation
McGill, Ryan J.; Dombrowski, Stefan C. – Communique, 2017
Factor analysis is a versatile class of psychometric techniques used by researchers to provide insight into the psychological dimensions (factors) that may account for the relationships among variables in a given dataset. The primary goal of a factor analysis is to determine a more parsimonious set of variables (i.e., fewer than the number of…
Descriptors: Factor Analysis, School Psychology, Psychometrics, Predictor Variables
Wise, Alyssa Friend; Shaffer, David Williamson – Journal of Learning Analytics, 2015
It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the…
Descriptors: Learning Theories, Predictor Variables, Data, Data Analysis
Sole, Marla A. – Mathematics Teacher, 2016
Every day, students collect, organize, and analyze data to make decisions. In this data-driven world, people need to assess how much trust they can place in summary statistics. The results of every survey and the safety of every drug that undergoes a clinical trial depend on the correct application of appropriate statistics. Recognizing the…
Descriptors: Statistics, Mathematics Instruction, Data Collection, Teaching Methods
Letkowski, Jerzy – Journal of Case Studies in Education, 2014
Descripting Statistics provides methodology and tools for user-friendly presentation of random data. Among the summary measures that describe focal tendencies in random data, the mode is given the least amount of attention and it is frequently misinterpreted in many introductory textbooks on statistics. The purpose of the paper is to provide a…
Descriptors: Statistical Data, Data Interpretation, Statistics, Qualitative Research
Hagedorn, Linda Serra; Cabrera, Alberto; Prather, George – Journal of College Student Retention: Research, Theory & Practice, 2011
Using a newly developed software application entitled "The Community College Transfer Calculator"[C], this article both quantifies the effect of specific course-taking patterns and stresses the need for an easy to understand tool for community college academic advisors, faculty, and students. The "Calculator" calculates the…
Descriptors: Community Colleges, Course Selection (Students), College Transfer Students, Computer Software
Paris, Scott G.; Luo, Serena Wenshu – Educational Researcher, 2010
The National Early Literacy Panel (2008) report identified early predictors of reading achievement as good targets for instruction, and many of those skills are related to decoding. In this article, the authors suggest that the developmental trajectories of rapidly developing skills pose problems for traditional statistical analyses. Rapidly…
Descriptors: Reading Achievement, Effect Size, Emergent Literacy, Data Interpretation
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains
Enders, Craig K.; Tofighi, Davood – Psychological Methods, 2007
Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in…
Descriptors: Predictor Variables, Item Response Theory, Statistical Analysis, Research
Wurtz, Keith – Journal of Applied Research in the Community College, 2008
The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…
Descriptors: Regression (Statistics), Predictor Variables, Educational Background, Grades (Scholastic)

Carr, James E.; Austin, John – Teaching of Psychology, 1997
Provides a brief overview of single-subject research designs. This method exercises its power by examining changes in single subjects' responses over time across experimental conditions. Describes a classroom project in which students collect repeated measures of their own behavior and graph the data. (MJP)
Descriptors: Causal Models, Data Collection, Data Interpretation, Demonstrations (Educational)
Shah, Chandra; Burke, Gerald – Centre for the Economics of Education and Training, Monash University, 2004
This report provides estimates of job and occupational mobility by demographic, educational and labour market variables using data from the Australian Bureau of Statistics (ABS) "Labour Mobility" survey for 2002. The report provides information on the effects of these variables on the probability of job separation. It also identifies the…
Descriptors: Labor Market, Education Work Relationship, Occupational Mobility, Probability