Search Results - "Lockwood, J R"

Matching and Weighting With Functions of Error-Prone Covariates for Causal Inference by Lockwood, J. R., McCaffrey, Daniel F.

“…Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching…”
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A Comparative Study of Item Response Theory Models for Mixed Discrete-Continuous Responses by Zopluoglu, Cengiz, Lockwood, J R

“…Language proficiency assessments are pivotal in educational and professional decision-making. With the integration of AI-driven technologies, these assessments…”
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Trends in Classroom Observation Scores by Casabianca, Jodi M., Lockwood, J. R., McCaffrey, Daniel F.

“…Observations and ratings of classroom teaching and interactions collected over time are susceptible to trends in both the quality of instruction and rater…”
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Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects by Lockwood, J. R., McCaffrey, Daniel F.

“…A common strategy for estimating treatment effects in observational studies using individual student-level data is analysis of covariance (ANCOVA) or…”
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Improving Accuracy and Stability of Aggregate Student Growth Measures Using Empirical Best Linear Prediction by Lockwood, J. R., Castellano, Katherine E., McCaffrey, Daniel F.

“…Many states and school districts in the United States use standardized test scores to compute annual measures of student achievement progress and then use…”
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An Exploration of an Improved Aggregate Student Growth Measure Using Data from Two States by Castellano, Katherine E., McCaffrey, Daniel F., Lockwood, J. R.

“…The simple average of student growth scores is often used in accountability systems, but it can be problematic for decision making. When computed using a…”
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Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models by Lockwood, J. R., Castellano, Katherine E.

“…Student Growth Percentiles (SGPs) increasingly are being used in the United States for inferences about student achievement growth and educator effectiveness…”
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The Intertemporal Variability of Teacher Effect Estimates by McCaffrey, Daniel F., Sass, Tim R., Lockwood, J. R., Mihaly, Kata

“…The utility of value-added estimates of teachers' effects on student test scores depends on whether they can distinguish between high- and low-productivity…”
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Flexible Bayesian Models for Inferences From Coarsened, Group-Level Achievement Data by Lockwood, J. R., Castellano, Katherine E., Shear, Benjamin R.

“…This article proposes a flexible extension of the Fay-Herriot model for making inferences from coarsened, group-level achievement data, for example,…”
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Models for Value-Added Modeling of Teacher Effects by McCaffrey, Daniel F., Lockwood, J. R., Koretz, Daniel, Louis, Thomas A., Hamilton, Laura

“…The use of complex value-added models that attempt to isolate the contributions of teachers or schools to student development is increasing. Several variations…”
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Where You Come From or Where You Go? Distinguishing Between School Quality and the Effectiveness of Teacher Preparation Program Graduates by Mihaly, Kata, McCaffrey, Daniel, Sass, Tim R., Lockwood, J. R.

“…We consider the challenges and implications of controlling for school contextual bias when modeling teacher preparation program effects. Because teachers are…”
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Uncovering Multivariate Structure in Classroom Observations in the Presence of Rater Errors by McCaffrey, Daniel F., Yuan, Kun, Savitsky, Terrance D., Lockwood, J. R., Edelen, Maria O.

“…We examine the factor structure of scores from the CLASS‐S protocol obtained from observations of middle school classroom teaching. Factor analysis has been…”
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Linking Reading Coaches and Student Achievement: Evidence From Florida Middle Schools by Lockwood, J. R., McCombs, Jennifer Sloan, Marsh, Julie

“…Many policymakers suggest that school-based reading coaches can improve teachers ' practice, which consequently will improve students 'literacy skills…”
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The Impact of Measurement Error on the Accuracy of Individual and Aggregate SGP by McCaffrey, Daniel F., Castellano, Katherine E., Lockwood, J. R.

“…Student growth percentiles (SGPs) express students' current observed scores as percentile ranks in the distribution of scores among students with the same…”
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Estimating the Standard Error of the Impact Estimator in Individually Randomized Trials With Clustering by Weiss, Michael J., Lockwood, J. R., McCaffrey, Daniel F.

“…In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but…”
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The Sensitivity of Value-Added Teacher Effect Estimates to Different Mathematics Achievement Measures by Lockwood, J. R., McCaffrey, Daniel F., Hamilton, Laura S., Stecher, Brian, Le, Vi-Nhuan, Martinez, José Felipe

“…Using longitudinal data from a cohort of middle school students from a large school district, we estimate separate "value-added" teacher effects for two…”
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Estimating Test-Retest Reliability in the Presence of Self-Selection Bias and Learning/Practice Effects by Belzak, William C. M., Lockwood, J. R.

“…Test-retest reliability is often estimated using naturally occurring data from test repeaters. In settings such as admissions testing, test takers choose if…”
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Exploring Student-Teacher Interactions in Longitudinal Achievement Data by Lockwood, J. R., McCaffrey, Daniel F.

“…This article develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different…”
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A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical Scaling by Mariano, Louis T., McCaffrey, Daniel F., Lockwood, J. R.

“…There is an increasing interest in using longitudinal measures of student achievement to estimate individual teacher effects. Current multivariate models…”
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Using hidden information and performance level boundaries to study student–teacher assignments: implications for estimating teacher causal effects by Lockwood, J. R., McCaffrey, D.

“…Summary A common problem in educational evaluation is estimating causal effects of interventions from non‐experimental data on students. Scores from…”
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