Peter M Steiner

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Case matching and the reduction of selection bias in quasi-experiments: The relative importance of pretest measures of outcome, of unreliable measurement, and of mode of data analysis.

Peter M. Steiner, T. Cook, Thomas D Cook, 2010, Psychological methods.

A randomized experiment comparing random and cutoff-based assignment.

Vivian C. Wong, Vivian C Wong, W. Shadish, 2011, Psychological methods.

Experimental Vignette Studies in Survey Research

Peter M. Steiner, C. Atzmüller, Peter M Steiner, 2010 .

Graphical Models for Quasi-experimental Designs

Dan Su, Peter M. Steiner, Courtney E. Hall, 2017, Sociological methods & research.

Designing Valid and Reliable Vignette Experiments for Survey Research: A Case Study on the Fair Gender Income Gap

Peter M. Steiner, C. Atzmüller, Dan Su, 2017 .

The importance of covariate selection in controlling for selection bias in observational studies.

Peter M. Steiner, W. Shadish, T. Cook, 2010, Psychological methods.

Unbiased Causal Inference From an Observational Study: Results of a Within-Study Comparison

Thomas D. Cook, Steffi Pohl, Peter M. Steiner, 2009 .

Regression Discontinuity Designs With an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English-Language Learners

Peter M. Steiner, J Kim, Y Suk, 2021 .

Quasi-Experimental Designs for Causal Inference

Peter M. Steiner, Yongnam Kim, Peter M Steiner, 2016, Educational psychologist.

Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables

Vivian C. Wong, Thomas D. Cook, Peter M. Steiner, 2013 .

Research Designs for Program Evaluation

Coady Wing, Vivian C. Wong, Thomas D. Cook, 2012 .

How Bias Reduction Is Affected by Covariate Choice, Unreliability, and Mode of Data Analysis: Results From Two Types of Within-Study Comparisons

Thomas D. Cook, Steffi Pohl, Peter M. Steiner, 2009, Multivariate behavioral research.

Random and Cutoff-based Assignment 1 Running Head: Random and Cutoff-based Assignment a Randomized Experiment Comparing Random to Cutoff-based Assignment

Vivian C. Wong, Peter M. Steiner, W. Shadish, 2010 .

Adding Design Elements to Improve Time Series Designs: No Child Left Behind as an Example of Causal Pattern-Matching

Thomas D. Cook, Peter M. Steiner, Manyee Wong, 2015 .

Gain Scores Revisited: A Graphical Models Perspective

Peter M. Steiner, Yongnam Kim, Yongnam Kim, 2019, Sociological Methods & Research.

Matching and Propensity Scores

David Cook, Peter M. Steiner, Peter M Steiner, 2013 .

Working Paper : Replication Designs for Causal Inference

Vivian C. Wong, Peter M. Steiner, Peter M Steiner, 2018 .

Experimentelle vignettendesigns in faktoriellen surveys

Peter M. Steiner, C. Atzmüller, Peter M Steiner, 2006 .

An introduction to modeling longitudinal data with generalized additive models: applications to single-case designs.

William R Shadish, Peter M. Steiner, Kristynn J Sullivan, 2015, Psychological methods.

A Primer on Propensity Score Analysis

William R. Shadish, Peter M. Steiner, W. Shadish, 2010 .

Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments

Peter M. Steiner, W. Shadish, M. H. Clark, 2008 .

Classification of large data sets with mixture models via sufficient EM

Peter M. Steiner, M. Hudec, Peter M. Steiner, 2007, Comput. Stat. Data Anal..

Multilevel Propensity Score Methods for Estimating Causal Effects: A Latent Class Modeling Strategy

Jee-Seon Kim, Peter M. Steiner, Jee-Seon Kim, 2015 .

Comments: Using Design Elements for Increasing the Severity of Causal Mediation Tests

Peter M Steiner, 2012 .

What Can Be Learned From Empirical Evaluations of Nonexperimental Methods?

Vivian C. Wong, Peter M. Steiner, Kylie L. Anglin, 2018, Evaluation review.

Design-Based Approaches to Causal Replication Studies

Vivian C. Wong, Peter M. Steiner, Vivian C. Wong, 2021, Prevention Science.

An introduction to modeling longitudinal data with generalized additive models: applications to single-case designs.

W. Shadish, K. J. Sullivan, Peter M Steiner, 2015, Psychological methods.

Design‐ and Model‐Based Analysis of Propensity Score Designs

Peter M. Steiner, Peter M Steiner, 2016 .

The Role of Sample Size to Attain Statistically Comparable Groups – A Required Data Preprocessing Step to Estimate Causal Effects With Observational Data

Peter M Steiner, A. Kolar, 2021, Evaluation review.

An Evaluation of Experimental Designs for Constructing Vignette Sets in Factorial Surveys

Peter M. Steiner, Dan Su, Peter M Steiner, 2020 .

The Generalized Regression Discontinuity Design: Using Multiple Assignment Variables and Cutoffs to Estimate Treatment Effects.

Vivian C. Wong, T. Cook, Peter M Steiner, 2009 .

Randomized Experiments and Quasi-Experimental Designs in Educational Research

Thomas D. Cook, Peter M. Steiner, T. Cook, 2009 .

A Causal Replication Framework for Designing and Assessing Replication Efforts

Vivian C. Wong, Peter M. Steiner, Kylie L. Anglin, 2019, Zeitschrift für Psychologie.

Estimating the causal effect of randomization versus treatment preference in a doubly randomized preference trial.

Peter M. Steiner, W. Shadish, E. Stuart, 2012, Psychological methods.

The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases

Peter M. Steiner, Yongnam Kim, Yongnam Kim, 2016, Journal of causal inference.

Pretest Measures of the Study Outcome and the Elimination of Selection Bias: Evidence from Three Within Study Comparisons

Peter M. Steiner, T. Cook, K. Hallberg, 2016, Prevention Science.

The Role of Pretest and Proxy-Pretest Measures of the Outcome for Removing Selection Bias in Observational Studies.

Peter M. Steiner, T. Cook, K. Hallberg, 2011 .

Matching Strategies for Observational Data with Multilevel Structure.

Peter M. Steiner, Peter M Steiner, 2011 .

Matching Strategies for Observational Multilevel Data

Jee-Seon Kim, Peter M. Steiner, Felix Thoemmes, 2012 .

Symposium Justification Title: Towards a Reproducibility Framework Background Recent efforts to promote evidence-based practices in medicine and the social sciences (i.e. What Works Clearinghouse) assume that scientific findings are of sufficient validity to warrant its use in decision making. Repro

Vivian C. Wong, Peter M. Steiner, A. Claessens, 2017 .

Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy

S. Park, D. Kaplan, Peter M Steiner, 2018, Psychometrika.

Identifying Causal Estimands for Time-Varying Treatments Measured with Time-Varying (Age or Grade-Based) Instruments

S. Park, Yongnam Kim, Peter M Steiner, 2016, Multivariate behavioral research.

Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

Peter M. Steiner, T. Cook, Wei Li, 2015 .

Propensity Score Methods for Causal Inference: On the Relative Importance of Covariate Selection, Reliable Measurement, and Choice of Propensity Score Technique

Peter M Steiner, 2011 .

On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores

William R. Shadish, Thomas D. Cook, Peter M. Steiner, 2011 .

On the Bias-Amplifying Effect of Near Instruments in Observational Studies.

Yongnam Kim, Peter M Steiner, 2014 .

S. Guo & M.W. Fraser (2010). Propensity Score Analysis: Statistical Methods and Applications.

Peter M Steiner, 2010 .

Causal Inference with Observational Multilevel Data: Investigating Selection and Outcome Heterogeneity

Jee-Seon Kim, Wen-Chiang Lim, Peter M. Steiner, 2016 .

Causal graphical views of fixed effects and random effects models.

Yongnam Kim, Peter M Steiner, 2019, The British journal of mathematical and statistical psychology.

Practice of causal inference with the propensity of being zero or one: assessing the effect of arbitrary cutoffs of propensity scores.

Peter M. Steiner, Joseph Kang, Mi‐Ok Kim, 2016, Communications for statistical applications and methods.

No Child Left Behind: An Interim Evaluation of Its Effects on Learning Using Two Interrupted Time Series Each With Its Own Non-Equivalent Comparison Series (WP-09-11)

T. Cook, Peter M Steiner, Manyee Wong, 2009 .