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Tomek, Sara; Robinson, Cecil – Measurement: Interdisciplinary Research and Perspectives, 2021
Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to…
Descriptors: Growth Models, Statistical Analysis, Hierarchical Linear Modeling, Computation
Kuha, Jouni; Mills, Colin – Sociological Methods & Research, 2020
It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the…
Descriptors: Comparative Analysis, Regression (Statistics), Research Problems, Computation
Ferguson, Sarah L.; Moore, E. Whitney G.; Hull, Darrell M. – International Journal of Behavioral Development, 2020
The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable modeling but not LPA specifically. A general procedure for conducting LPA is provided in six steps: (a) data inspection, (b) iterative evaluation of models,…
Descriptors: Statistical Analysis, Computer Software, Data Analysis, Goodness of Fit
Koster, Jeremy; Leckie, George; Aven, Brandy – Field Methods, 2020
The multilevel social relations model (SRM) is a commonly used statistical method for the analysis of social networks. In this article and accompanying supplemental materials, we demonstrate the estimation and interpretation of the SRM using Stat-JR software. Multiple software templates permit the analysis of different response types, including…
Descriptors: Statistical Analysis, Computer Software, Hierarchical Linear Modeling, Social Networks
Calver, Michael; Fletcher, Douglas – American Biology Teacher, 2020
Data collected in many biology laboratory classes are on ratio or interval scales where the size interval between adjacent units on the scale is constant, which is a critical requirement for analysis with parametric statistics such as t-tests or analysis of variance. In other cases, such as ratings of disease or behavior, data are collected on…
Descriptors: Statistical Analysis, Data Collection, Biology, Science Laboratories
Slayter, Elspeth M. – Journal of Social Work Education, 2017
Existing research suggests a majority of faculty include social justice content in research courses but not through the use of existing quantitative data for in-class activities that foster mastery of data analysis and interpretation and curiosity about social justice-related topics. By modeling data-driven dialogue and the deconstruction of…
Descriptors: Statistical Analysis, Social Justice, Class Activities, Social Work
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
Abel, Todd; Poling, Lisa – Teaching Statistics: An International Journal for Teachers, 2015
Working with practicing teachers, this article demonstrates, through the facilitation of a statistical activity, how to introduce and investigate the unique qualities of the statistical process including: formulate a question, collect data, analyze data, and interpret data.
Descriptors: Statistical Analysis, Concept Formation, Data Collection, Data Interpretation
Rankin, Jenny Grant – Online Submission, 2015
Data is critical, but data has not always lead to success. If data is not communicated clearly, the results can be disastrous. Design lets us communicate data so it can be understood. Dr. Rankin believes that "when we're fluent in visualizing our ideas, we can communicate them across global boundaries, across spoken language barriers, and…
Descriptors: Data, Information Utilization, Relevance (Education), Data Interpretation
Hays, Danica G.; Wood, Chris – Measurement and Evaluation in Counseling and Development, 2017
We present considerations for validity when a population outside of a normed sample is assessed and those data are interpreted. Using a career group counseling example exploring life satisfaction changes as evidenced by the Quality of Life Inventory (Frisch, 1994), we showcase qualitative and quantitative approaches to explore how normative data…
Descriptors: Data Interpretation, Scores, Quality of Life, Life Satisfaction
Bennett, Kimberley Ann – Teaching Statistics: An International Journal for Teachers, 2015
Students may need explicit training in informal statistical reasoning in order to design experiments or use formal statistical tests effectively. By using scientific scandals and media misinterpretation, we can explore the need for good experimental design in an informal way. This article describes the use of a paper that reviews the measles mumps…
Descriptors: Statistical Analysis, Thinking Skills, Research Design, Data Interpretation
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
Porter, Kristin E.; Balu, Rekha – MDRC, 2016
Education systems are increasingly creating rich, longitudinal data sets with frequent, and even real-time, data updates of many student measures, including daily attendance, homework submissions, and exam scores. These data sets provide an opportunity for district and school staff members to move beyond an indicators-based approach and instead…
Descriptors: Models, Prediction, Statistical Analysis, Elementary Secondary Education
Van Duzer, Eric – Online Submission, 2012
The purpose of this paper is to introduce the process and value of Random Qualitative Validation (RQV) in the development and interpretation of survey data. RQV is a method of gathering clarifying qualitative data that improves the validity of the quantitative analysis. This paper is concerned with validity in relation to the participants'…
Descriptors: Research Methodology, Validity, Mixed Methods Research, Surveys
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