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Swank, Jacqueline M.; Mullen, Patrick R. – Measurement and Evaluation in Counseling and Development, 2017
The article serves as a guide for researchers in developing evidence of validity using bivariate correlations, specifically construct validity. The authors outline the steps for calculating and interpreting bivariate correlations. Additionally, they provide an illustrative example and discuss the implications.
Descriptors: Correlation, Construct Validity, Guidelines, Data Interpretation
Boedeker, Peter – Practical Assessment, Research & Evaluation, 2017
Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…
Descriptors: Hierarchical Linear Modeling, Maximum Likelihood Statistics, Bayesian Statistics, Computation
Creswell, John W. – Pearson Education, Inc., 2015
"Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research" offers a truly balanced, inclusive, and integrated overview of the processes involved in educational research. This text first examines the general steps in the research process and then details the procedures for conducting specific types…
Descriptors: Educational Research, Qualitative Research, Statistical Analysis, Research Methodology
Goedert, Kelly M.; Ellefson, Michelle R.; Rehder, Bob – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014
Individuals have difficulty changing their causal beliefs in light of contradictory evidence. We hypothesized that this difficulty arises because people facing implausible causes give greater consideration to causal alternatives, which, because of their use of a positive test strategy, leads to differential weighting of contingency evidence.…
Descriptors: Causal Models, Inferences, Beliefs, Attitude Change
Kotrlik, Joe W.; Williams, Heather A.; Jabor, M. Khata – Journal of Agricultural Education, 2011
The Journal of Agricultural Education (JAE) requires authors to follow the guidelines stated in the Publication Manual of the American Psychological Association [APA] (2009) in preparing research manuscripts, and to utilize accepted research and statistical methods in conducting quantitative research studies. The APA recommends the reporting of…
Descriptors: Agricultural Education, Statistical Significance, Effect Size, Educational Research
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Douzenis, Cordelia; Rakow, Ernest A. – 1987
Outliers, extreme data values relative to others in a sample, may distort statistics that assume internal levels of measurement and normal distribution. The outlier may be a valid value or an error. Several procedures are available for identifying outliers, and each may be applied to errors of prediction from the regression lines for utility in a…
Descriptors: Correlation, Data Analysis, Data Interpretation, Statistical Analysis
Hoyt, William T.; Leierer, Stephen; Millington, Michael J. – Rehabilitation Counseling Bulletin, 2006
Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…
Descriptors: Effect Size, Regression (Statistics), Correlation, Researchers
Bracey, Gerald W. – Education Digest: Essential Readings Condensed for Quick Review, 2006
It is curious that so many people are accepting of statistics despite Disraeli's famous aphorism concerning "three kinds of lies." This acceptance certainly seems to hold for education statistics, especially when they imply something negative about American public schools. Sometimes people accept statistics because they are not in a position to…
Descriptors: Data Interpretation, Statistics, Correlation, Rhetoric
PDF pending restorationZwick, William R.; Velicer, Wayne F. – 1984
A common problem in the behavioral sciences is to determine if a set of observed variables can be more parsimoniously represented by a smaller set of derived variables. To address this problem, the performance of five methods for determining the number of components to retain (Horn's parallel analysis, Velicer's Minimum Average Partial (MAP),…
Descriptors: Behavioral Science Research, Comparative Analysis, Correlation, Data Interpretation
Peer reviewedGoldstein, Miriam D.; Strube, Michael J. – Teaching of Psychology, 1995
Describes two QuickBASIC programs that provide students direct experience with interpreting correlation scatter-plots. Maintains that the programs can be used in classroom exercises to highlight factors that influence the size of a Pearson correlation coefficient. (CFR)
Descriptors: Computer Software Development, Computer Uses in Education, Correlation, Data Analysis
Peer reviewedStrube, Michael; Goldstein, Miriam D. – Teaching of Psychology, 1995
Describes a QuickBASIC program for demonstrating the differences between main effects and interactions in factorial designs. The program can be used in conjunction with a traditional lecture to improve student understanding and develop skills in recognizing main effects and interactions from graphic displays. (CFR)
Descriptors: Computer Software Development, Computer Uses in Education, Correlation, Data Analysis
Hafner, Arthur W. – 1998
A thorough understanding of the uses and applications of statistical techniques is integral in gaining support for library funding or new initiatives. This resource is designed to help practitioners develop and manipulate descriptive statistical information in evaluating library services, tracking and controlling limited resources, and analyzing…
Descriptors: Correlation, Data Interpretation, Libraries, Library Education
Brossart, Daniel F.; Parker, Richard I.; Olson, Elizabeth A.; Mahadevan, Lakshmi – Behavior Modification, 2006
This study explored some practical issues for single-case researchers who rely on visual analysis of graphed data, but who also may consider supplemental use of promising statistical analysis techniques. The study sought to answer three major questions: (a) What is a typical range of effect sizes from these analytic techniques for data from…
Descriptors: Research Design, Effect Size, Evaluation Methods, Researchers
Creighton, Theodore B. – Corwin Press, 2006
Since the first edition of "Schools and Data", the No Child Left Behind Act has swept the country, and data-based decision making is no longer an option for educators. Today's educational climate makes it imperative for all schools to collect data and use statistical analysis to help create clear goals and recognize strategies for…
Descriptors: Federal Legislation, Program Evaluation, Educational Technology, Decision Making

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