Publication Date
In 2025 | 0 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 11 |
Since 2006 (last 20 years) | 26 |
Descriptor
Nonparametric Statistics | 31 |
Statistical Inference | 31 |
Statistical Analysis | 13 |
Sampling | 11 |
Regression (Statistics) | 10 |
Causal Models | 7 |
Computation | 6 |
Foreign Countries | 6 |
Statistics | 6 |
Comparative Analysis | 5 |
Correlation | 5 |
More ▼ |
Source
Author
Abad, Francisco J. | 1 |
Adam Sales | 1 |
Barker, Gregory | 1 |
Barratt, Monica J. | 1 |
Beasley, T. Mark | 1 |
Beckham, Jean C. | 1 |
Blackburn, Greg | 1 |
Botha, Jo-Anne | 1 |
Brancu, Mira | 1 |
Cai, Li | 1 |
Calhoun, Patrick S. | 1 |
More ▼ |
Publication Type
Education Level
Higher Education | 6 |
Postsecondary Education | 6 |
Adult Education | 1 |
Early Childhood Education | 1 |
Elementary Education | 1 |
Grade 1 | 1 |
High Schools | 1 |
Primary Education | 1 |
Secondary Education | 1 |
Audience
Researchers | 1 |
Laws, Policies, & Programs
Assessments and Surveys
ACT Assessment | 1 |
Early Childhood Longitudinal… | 1 |
What Works Clearinghouse Rating
Ting Ye; Ted Westling; Lindsay Page; Luke Keele – Grantee Submission, 2024
The clustered observational study (COS) design is the observational study counterpart to the clustered randomized trial. In a COS, a treatment is assigned to intact groups, and all units within the group are exposed to the treatment. However, the treatment is non-randomly assigned. COSs are common in both education and health services research. In…
Descriptors: Nonparametric Statistics, Identification, Causal Models, Multivariate Analysis
Duy Pham; Kirk Vanacore; Adam Sales; Johann Gagnon-Bartsch – Society for Research on Educational Effectiveness, 2024
Background: Education researchers typically estimate average program effects with regression; if they are interested in heterogeneous effects, they include an interaction in the model. Such models quantify and infer the influences of each covariate on the effect via interaction coefficients and their associated p-values or confidence intervals.…
Descriptors: Educational Research, Educational Researchers, Regression (Statistics), Artificial Intelligence
Keller, Bryan – Journal of Educational and Behavioral Statistics, 2020
Widespread availability of rich educational databases facilitates the use of conditioning strategies to estimate causal effects with nonexperimental data. With dozens, hundreds, or more potential predictors, variable selection can be useful for practical reasons related to communicating results and for statistical reasons related to improving the…
Descriptors: Nonparametric Statistics, Computation, Testing, Causal Models
Makela, Susanna; Si, Yajuan; Gelman, Andrew – Grantee Submission, 2018
Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to…
Descriptors: Bayesian Statistics, Statistical Inference, Sampling, Probability
Mubarik, Shujaat; Chandran, V. G. R.; Devadason, Evelyn S. – Learning Organization, 2016
Purpose: This study aims to examine the influence of relational capital quality on client loyalty, comprising both behavioral and attitudinal, in the pharmaceutical industry of Pakistan. Design/methodology/approach: The partial least squares technique is used to test the relationship using a sample of 111 pharmaceutical firms. We applied a…
Descriptors: Foreign Countries, Industry, Pharmacology, Behavior
Kim, Yongnam; Steiner, Peter – Educational Psychologist, 2016
When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…
Descriptors: Quasiexperimental Design, Causal Models, Statistical Inference, Randomized Controlled Trials
Kang, Yoonjeong; Harring, Jeffrey R.; Li, Ming – Journal of Experimental Education, 2015
The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with…
Descriptors: Comparative Analysis, Monte Carlo Methods, Statistical Analysis, Robustness (Statistics)
Dorie, Vincent; Harada, Masataka; Carnegie, Nicole Bohme; Hill, Jennifer – Grantee Submission, 2016
When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis…
Descriptors: Bayesian Statistics, Mathematical Models, Causal Models, Statistical Bias
Key-DeLyria, Sarah E. – Journal of Speech, Language, and Hearing Research, 2016
Purpose: Sentence processing can be affected following a traumatic brain injury (TBI) due to linguistic or cognitive deficits. Language-related event-related potentials (ERPs), particularly the P600, have not been described in individuals with TBI history. Method: Four young adults with a history of closed head injury participated. Two had severe…
Descriptors: Sentences, Language Processing, Head Injuries, Neurological Impairments
Barratt, Monica J.; Ferris, Jason A.; Lenton, Simon – Field Methods, 2015
Online purposive samples have unknown biases and may not strictly be used to make inferences about wider populations, yet such inferences continue to occur. We compared the demographic and drug use characteristics of Australian ecstasy users from a probability (National Drug Strategy Household Survey, n = 726) and purposive sample (online survey…
Descriptors: Sampling, Validity, Drug Abuse, Probability
Beasley, T. Mark – Journal of Experimental Education, 2014
Increasing the correlation between the independent variable and the mediator ("a" coefficient) increases the effect size ("ab") for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation caused by…
Descriptors: Statistical Analysis, Effect Size, Nonparametric Statistics, Statistical Inference
Rahmatullah, Mamat – Higher Education Studies, 2016
In this study, the problem is limited factors relating to the learning effectiveness and teacher competence in improving the teacher performance. Therefore, this study will try to get explanations from some main issues which include the learning effectiveness issue, and teacher competence to increase teacher performance in Madrasah Tsanawiyah at…
Descriptors: Foreign Countries, Correlation, Statistical Inference, Statistics
O'Hara, Michael E. – Journal of Economic Education, 2014
Although the concept of the sampling distribution is at the core of much of what we do in econometrics, it is a concept that is often difficult for students to grasp. The thought process behind bootstrapping provides a way for students to conceptualize the sampling distribution in a way that is intuitive and visual. However, teaching students to…
Descriptors: Economics Education, Economics, Sampling, Statistical Inference
Botha, Jo-Anne; Coetzee, Mariette – International Review of Research in Open and Distributed Learning, 2016
This study investigated the relationship between self-directedness (as measured by the Adult Learner Self-Directedness Scale) and biographical factors such as age, race, and gender of adult learners enrolled at a South African open distance learning (ODL) higher education institution. Correlational and inferential statistical analyses were used. A…
Descriptors: Adult Learning, Distance Education, Electronic Learning, Open Universities
Jo, Booil; Vinokur, Amiram D. – Journal of Educational and Behavioral Statistics, 2011
When identification of causal effects relies on untestable assumptions regarding nonidentified parameters, sensitivity of causal effect estimates is often questioned. For proper interpretation of causal effect estimates in this situation, deriving bounds on causal parameters or exploring the sensitivity of estimates to scientifically plausible…
Descriptors: Statistical Analysis, Statistical Inference, Nonparametric Statistics, Intervention