NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Researchers69
Practitioners14
Teachers8
Policymakers1
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 69 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Weicong Lyu; Peter M. Steiner – Society for Research on Educational Effectiveness, 2021
Doubly robust (DR) estimators that combine regression adjustments and inverse probability weighting (IPW) are widely used in causal inference with observational data because they are claimed to be consistent when either the outcome or the treatment selection model is correctly specified (Scharfstein et al., 1999). This property of "double…
Descriptors: Robustness (Statistics), Causal Models, Statistical Inference, Regression (Statistics)
Peer reviewed Peer reviewed
Direct linkDirect link
Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…
Descriptors: Error of Measurement, Monte Carlo Methods, Data Collection, Simulation
Peer reviewed Peer reviewed
Direct linkDirect link
Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
Dong, Nianbo – Society for Research on Educational Effectiveness, 2012
This paper is based on previous studies in applying propensity score methods to study multiple treatment variables to examine the causal moderator effect. The propensity score methods will be demonstrated in a case study to examine the causal moderator effect, where the moderators are categorical and continuous variables. Moderation analysis is an…
Descriptors: Probability, Statistical Analysis, Case Studies, Intervention
Peer reviewed Peer reviewed
Direct linkDirect link
Dunst, Carl J.; Hamby, Deborah W. – Journal of Intellectual & Developmental Disability, 2012
This paper includes a nontechnical description of methods for calculating effect sizes in intellectual and developmental disability studies. Different hypothetical studies are used to illustrate how null hypothesis significance testing (NHST) and effect size findings can result in quite different outcomes and therefore conflicting results. Whereas…
Descriptors: Intervals, Developmental Disabilities, Statistical Significance, Effect Size
Peer reviewed Peer reviewed
Direct linkDirect link
Henry, Kimberly L.; Muthen, Bengt – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Latent class analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this…
Descriptors: Statistical Analysis, Probability, Classification, Grade 9
Peer reviewed Peer reviewed
Direct linkDirect link
Belov, Dmitry I. – Psychometrika, 2008
In educational practice, a test assembly problem is formulated as a system of inequalities induced by test specifications. Each solution to the system is a test, represented by a 0-1 vector, where each element corresponds to an item included (1) or not included (0) into the test. Therefore, the size of a 0-1 vector equals the number of items "n"…
Descriptors: Educational Practices, Probability, Test Construction, Mathematical Concepts
Peer reviewed Peer reviewed
Direct linkDirect link
Sanchez-Meca, Julio; Marin-Martinez, Fulgencio – Psychological Methods, 2008
One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account…
Descriptors: Intervals, Monte Carlo Methods, Meta Analysis, Effect Size
Peer reviewed Peer reviewed
Direct linkDirect link
Ruscio, John – Psychological Methods, 2008
Calculating and reporting appropriate measures of effect size are becoming standard practice in psychological research. One of the most common scenarios encountered involves the comparison of 2 groups, which includes research designs that are experimental (e.g., random assignment to treatment vs. placebo conditions) and nonexperimental (e.g.,…
Descriptors: Psychological Studies, Effect Size, Probability, Correlation
Peer reviewed Peer reviewed
Direct linkDirect link
Markus, Keith A. – Multivariate Behavioral Research, 2008
One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts…
Descriptors: Research Design, Structural Equation Models, Data Analysis, Causal Models
Peer reviewed Peer reviewed
Direct linkDirect link
Xu, Yonghong Jade; Ishitani, Terry T. – New Directions for Institutional Research, 2008
In recent years, rapid advancement has taken place in computing technology that allows institutional researchers to efficiently and effectively address data of increasing volume and structural complexity (Luan, 2002). In this chapter, the authors propose a new data analytical technique, Bayesian belief networks (BBN), to add to the toolbox for…
Descriptors: Institutional Research, Classification, Researchers, College Faculty
Peer reviewed Peer reviewed
Direct linkDirect link
Roberts, James S. – Applied Psychological Measurement, 2008
Orlando and Thissen (2000) developed an item fit statistic for binary item response theory (IRT) models known as S-X[superscript 2]. This article generalizes their statistic to polytomous unfolding models. Four alternative formulations of S-X[superscript 2] are developed for the generalized graded unfolding model (GGUM). The GGUM is a…
Descriptors: Item Response Theory, Goodness of Fit, Test Items, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Bartlett, James E., II; Bartlett, Michelle E.; Reio, Thomas G., Jr. – Delta Pi Epsilon Journal, 2008
This research examined the issue of nonresponse bias and how it was reported in nonexperimental quantitative research published in the "Delta Pi Epsilon Journal" between 1995 and 2004. Through content analysis, 85 articles consisting of 91 separate samples were examined. In 72.5% of the cases, possible nonresponse bias was not examined in the…
Descriptors: Content Analysis, Probability, Response Rates (Questionnaires), Business Education
Peer reviewed Peer reviewed
Burrell, Quentin L. – Journal of Documentation, 1988
Proposes a probabilistic mechanism to describe various forms of the Bradford phenomenon reported in bibliographic research. The inclusion of a time parameter in the model to allow predictions of dynamic systems is explained. (58 references) (CLB)
Descriptors: Bibliometrics, Mathematical Models, Prediction, Probability
Peer reviewed Peer reviewed
Beck, Kenneth H. – Social Behavior and Personality, 1984
Investigated the effects of different types of risk information in a simulated decision-making task to test the predictions of protection motivation theory. College students (N=226) completed the task. Results showed outcome severity, efficacy of protection, and access to protection were related to protective decisions. (BH)
Descriptors: College Students, Decision Making, Higher Education, Probability
Previous Page | Next Page ยป
Pages: 1  |  2  |  3  |  4  |  5