Stephen G. West

Arizona State University, Phoenix, Arizona, United States

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Publications (117)275.11 Total impact

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    ABSTRACT: Aging is associated with progressive changes in learning and memory. A potential approach to attenuate age-related cognitive decline is cognitive training. In this study, adult male and female rats were given either repeated exposure to a T-maze, or no exposure to any maze, and then tested on a final battery of cognitive tasks. Two groups of each sex were tested from 6-18 months old on the same T-maze; one group received a version testing spatial reference memory, and the other group received only the procedural testing components with minimal cognitive demand. Groups three and four of each sex had no maze exposure until the final battery, and were comprised of aged or young rats. The final maze battery included the practiced T-maze and two novel tasks, one with a similar, and one with a different, memory type to the practice task. The fifth group of each sex was not maze tested, serving as an aged control for the effects of maze testing on neurotrophin protein levels in cognitive brain regions. Results showed that adult intermittent cognitive training enhanced performance on the practice task when aged in both sexes, that cognitive training benefits transferred to novel tasks and cognitive domains only in females, and that cognitive demand was necessary for these effects since rats receiving only the procedural testing components showed no improvement on the final maze battery. Further, for both sexes, rats that showed faster learning when young demonstrated better memory when aged. Age-related increases in neurotrophin concentrations in several brain regions were revealed, which was related to performance on the training task only in females. This longitudinal study supports the tenet that cognitive training can help one remember later in life, with broader enhancements and associations with neurotrophins only in females.
    Neurobiology of Aging 06/2014; In Press. · 6.17 Impact Factor
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    ABSTRACT: A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
    Journal of Consulting and Clinical Psychology 04/2014; · 4.85 Impact Factor
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    ABSTRACT: The Adolescent Motivation for Educational Attainment Questionnaire is a 32-item questionnaire (we drew 20 items from 3 subscales of the Educational Motivation Questionnaire; Murdock, 1999) that was developed to measure multiple potential dimensions of adolescents' motivation to complete high school and enroll in post-secondary education, including competence and effort beliefs; perceived value of education; and peer, teacher, and parent support for educational attainment. We assessed a multiethnic sample (N = 569) of low-achieving students who started 1st grade together in 1 urban and 2 small city school districts. Participants were assessed over 2 consecutive years (Grades 8 and 9 given prior grade retention, or Grades 9 and 10 if not retained). Exploratory factor analyses identified 4 correlated dimensions underlying the questionnaire responses. Subsequent confirmatory factor analyses provided support for a bifactor model, which includes a general factor of students' basic educational motivation, and specific factors of (a) teacher educational expectations, (b) peer aspirations, and (c) value of education. Measurement invariance of the bifactor model was established across students' gender and ethnicity (Caucasian, African American, and Hispanic) and year of testing. Criterion-related validity of the general and specific factors with students' school belonging, student-teacher warmth and conflict, disciplinary practices, letter grade, conduct problems, and behavioral engagement was examined. Practical implications of the measure are discussed. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
    Psychological Assessment 03/2014; · 2.99 Impact Factor
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    ABSTRACT: This study demonstrates how a variant of growth curve modeling known as longitudinal parallel-process modeling can yield an understanding of the effect of symptoms on quality of life (QOL). A two-level hierarchical linear model with random intercepts and slopes was implemented within a structural equation modeling approach. The data (N = 367) comes from a large database of persons with HIV-associated illness. Twenty-three symptoms based on the Sign and Symptom Checklist for Persons with HIV disease and items measuring QOL from the general health status scales were used. Each respondent completed from 1 to 11 questionnaires. The number of reported symptoms had a significant association with patient QOL over time. These findings suggest that appropriate symptom management has the potential to improve patient QOL. This study demonstrates how a state-of-the-art longitudinal modeling technique evaluates the relationship between concurrent rates of change in measurements of two relevant variables.
    Western Journal of Nursing Research 11/2013; · 1.22 Impact Factor
  • Lucia Ciciolla, Keith A Crnic, Stephen G West
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    ABSTRACT: Maternal sensitivity is a fundamental parenting construct and a determinant of positive child outcomes and healthy parent-child relationships. Few longitudinal studies have investigated determinants of sensitive parenting, particularly in a population of children at risk for developmental delay. This study modeled trajectories of maternal sensitivity observed in two independent parenting contexts at child ages 3-, 4-, and 5-years. The sample included N = 247 mother-child dyads, with n = 110 children classified as at risk for developmental delays. Predictors included maternal distress, child anger proneness, and developmental risk status. Maternal sensitivity changed during more demanding parenting tasks over the 3-year period but not during a low-demand task. Mothers of children with developmental risk, relative to mothers of typically developing children, and mothers of boys relative to mothers of girls, showed less sensitivity during more demanding parenting tasks. Early developmental risk and child gender contribute to the nature of maternal sensitivity over time, but their contributions depend on the situational demands of the interaction. This contextualized view of sensitivity provides further evidence in support of parenting as a dynamic developmental process.
    Parenting 07/2013; 13(3):178-195. · 1.13 Impact Factor
  • Stefany Coxe, Leona S. Aiken, Stephen G. West
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    ABSTRACT: How many days per week do you exercise for 30 minutes or more? Never? Once or twice? Every other day? Most days? Every day? Coarsely grouped counts such as this are commonly used in the behavioral sciences. When these variables are used as outcome variables, they often violate the assumptions of both linear regression and models designed for categorical outcomes; there is no model designed specifically for grouped count outcomes. Many analysis approaches also ignore the unequal spacing between categories; using the mean of the count range for each category captures the unequal spacing common in grouped count outcomes. The purpose of this study was to compare the statistical performance of three common regression models (linear regression, Poisson regression, and ordinal logistic regression) that can be used when the outcome is a grouped count. METHOD A simulation study was used to determine the power, type I error, and confidence interval (CI) coverage for these models. Mean structure, variance structure, effect size, predictor type, and sample size were included in the factorial design. Mean structure reflected either a linear or an exponential relationship between the predictor and the outcome. Since the distribution of the underlying count is unobserved, several variance options were evaluated, including homoscedastic, monotonically increasing, and increasing then decreasing variance. Zero, small, medium, and large effect sizes and sample sizes of 100, 200, 500, and 1000 were examined. A single predictor (either continuous or binary) was used to predict the grouped count outcome. RESULTS All regression models produced unbiased estimates of the regression coefficient. Ordinal logistic regression produced type I error, power, and confidence interval (CI) coverage rates that were consistently within acceptable limits. Linear regression produced type I error and power that were within acceptable limits, but CI coverage was too low in conditions with an exponential mean structure, particularly with a large effect size and/or monotonically increasing variance structure. Poisson regression displayed inflated type I error, low power, and low CI coverage rates for nearly all conditions. CONCLUSIONS Based on the statistical performance of the three models, ordinal logistic regression is the preferred method for analyzing grouped count outcomes. Linear regression also performed well, but CI coverage was too low for several conditions with an exponential mean structure; these specific conditions are of particular interest because they reflect conditions commonly observed for counts and frequencies. Comparisons of model fit and tests of model assumptions (e.g., the proportional odds assumption for ordinal logistic regression) are in progress.
    Society for Prevention Research 21nd Annual Meeting 2015; 05/2013
  • Stephanie E Moser, Stephen G West, Jan N Hughes
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    ABSTRACT: This study investigated the effects of retention or promotion in first grade on growth trajectories in mathematics and reading achievement over the elementary school years (grades 1-5). From a large multiethnic sample (n = 784) of children who were below the median in literacy at school entrance, 363 children who were either promoted (n = 251) or retained (n = 112) in first grade could be successfully matched on 72 background variables. Achievement was measured annually using Woodcock-Johnson W scores; scores of retained children were shifted back one year to permit same-grade comparisons. Using longitudinal growth curve analysis, trajectories of math and reading scores for promoted and retained children were compared. Retained children received a one year boost in achievement; this boost fully dissipated by the end of elementary school. The pattern of subsequent retention in grades 2, 3 and 4 and placement in special education of the sample during the elementary school years is also described and their effects are explored. Policy implications for interventions for low achieving children are considered.
    Journal of Educational Psychology 08/2012; 104(3):603-621. · 3.08 Impact Factor
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    ABSTRACT: Multilevel mediation analysis examines the indirect effect of an independent variable on an outcome achieved by targeting and changing an intervening variable in clustered data. We study analytically and through simulation the effects of an omitted variable at level 2 on a 1-1-1 mediation model for a randomized experiment conducted within clusters in which the treatment, mediator, and outcome are all measured at level 1. When the residuals in the equations for the mediator and the outcome variables are fully orthogonal, the two methods of calculating the indirect effect (ab, c - c') are equivalent at the between- and within-cluster levels. Omitting a variable at level 2 changes the interpretation of the indirect effect and will induce correlations between the random intercepts or random slopes. The equality of within-cluster ab and c - c' no longer holds. Correlation between random slopes implies that the within-cluster indirect effect is conditional, interpretable at the grand mean level of the omitted variable.
    British Journal of Mathematical and Statistical Psychology 05/2012; · 1.26 Impact Factor
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    ABSTRACT: Registered nurses and nurse researchers often use questionnaires to measure patient outcomes. When questionnaires or other multiple-item instruments have been developed using a relatively homogeneous sample, the suitability of even a psychometrically well-developed instrument for the new population comes into question. Bias or lack of equivalence can be introduced into instruments through differences in perceptions of the meaning of the measured items, constructs, or both in the two groups. To explain measurement invariance and illustrate how it can be tested using the English and Spanish versions of the Paediatric Asthma Quality of Life Questionnaire (PAQLQ). A sample of 607 children from the Phoenix Children's Hospital Breathmobile was selected for this analysis. The children were of ages 6-18 years; 61.2% completed the PAQLQ in Spanish. Testing measurement invariance using multiple-group confirmatory factor analysis, a series of hierarchical nested models, is demonstrated. In assessing the adequacy of the fit of each model at each stage, both χ2 tests and goodness-of-fit indexes were used. The test of measurement invariance for the one-factor model showed that the English and Spanish versions of the scale met the criteria for measurement invariance. The level of strict invariance (equal factor loadings, intercepts, and residual variances between groups) was achieved. Confirmatory factor analysis is used to evaluate the structural integrity of a measurement instrument; multiple confirmatory factor analyses are used to assess measurement invariance across different groups and to stamp the data as valid or invalid. The PAQLQ, a widely used instrument having evidence to support reliability and validity was used separately in English- and Spanish-speaking groups. Traditional methods for evaluating measurement instruments have been less than thorough, and this article demonstrates a well-developed approach, allowing for confident comparisons between populations.
    Nursing research 05/2012; 61(3):171-80. · 1.80 Impact Factor
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    ABSTRACT: Behavioral science researchers routinely use scale scores that sum or average a set of questionnaire items to address their substantive questions. A researcher applying multiple imputation to incomplete questionnaire data can either impute the incomplete items prior to computing scale scores or impute the scale scores directly from other scale scores. This study used a Monte Carlo simulation to assess the impact of imputation method on the bias and efficiency of scale-level parameter estimates, including scale score means, between-scale correlations, and regression coefficients. Although the choice of imputation approach had no influence on the bias of scale-level parameter estimates, it had a substantial impact on efficiency, such that item-level imputation consistently produced a meaningful power advantage. The simulation results clearly supported the use of item-level imputation. To illustrate the differences between item- and scale-level imputation, we examined predictors of 7th-grade academic self-efficacy in a sample of 595 low-income Mexican Origin adolescents in a planned missingness design. The results of the empirical data analysis were consistent with those of the simulation and also suggested that researchers should be cautious when implementing planned missing data designs that necessitate scale-level imputation.
    Multivariate Behavioral Research 02/2012; 47(1):1-25. · 1.66 Impact Factor
  • Ehri Ryu, Stephen G West, Karen H Sousa
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    ABSTRACT: Many health measures (e.g., blood pressure, quality of life) have meaningful fluctuation over time around a relatively stable mean level for each person. This didactic paper describes two closely related statistical models for examining between-person and within-person relationships between two or more sets of measures collected over time: the latent intercept model with correlated residuals (LI) in structural equation modeling framework and the multivariate multilevel model (MVML) in multilevel modeling framework. We illustrated that the basic LI model and the MVML model are equivalent. We presented an illustrative example using a national arthritis data resource to examine between-person and within-person relationships of symptom status, functional health, and quality of life in arthritis patients. Additional design and modeling issues for the treatment of missing data are considered. We discuss contexts in which one of the two models may be preferred. Mplus and SAS syntax are available.
    Annals of Behavioral Medicine 01/2012; 43(3):330-42. · 4.20 Impact Factor
  • Heining Cham, Stephen G West, Yue Ma, Leona S Aiken
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    ABSTRACT: A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance.
    Multivariate Behavioral Research 01/2012; 47(6):840-876. · 1.66 Impact Factor
  • The Handbook of Research Methods in Psychology: Vol. 3. Data Analysis and Research Publication, Edited by H. Cooper, P. Camic, D. Long, A. Panter, K. Sher, 01/2012; American Psychological Association.
  • Felix J. Thoemmes, Stephen G. West
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    ABSTRACT: In this article we propose several modeling choices to extend propensity score analysis to clustered data. We describe different possible model specifications for estimation of the propensity score: single-level model, fixed effects model, and two random effects models. We also consider both conditioning within clusters and conditioning across clusters. We examine the underlying assumptions of these modeling choices and the type of randomized experiment approximated by each approach. Using a simulation study, we compare the relative performance of these modeling and conditioning choices in reducing bias due to confounding variables at both the person and cluster levels. An applied example based on a study by Hughes, Chen, Thoemmes, and Kwok (2010) is provided in which the effect of retention in Grade 1 on passing an achievement test in Grade 3 is evaluated. We find that models that consider the clustered nature of the data both in estimation of the propensity score and conditioning on the propensity score performed best in our simulation study; however, other modeling choices also performed well. The applied example illustrates practical limitations of these models when cluster sizes are small.
    Multivariate Behavioral Research 05/2011; 46(3):514-543. · 1.66 Impact Factor
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    Jan N Hughes, Wei Wu, Stephen G West
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    ABSTRACT: We investigated growth trajectories for classroom performance goal practices and for student behavioral engagement across grades 2 to 5 for 497 academically at-risk elementary students. This study is the first longitudinal investigation of performance goal practices in the early elementary years. On average, teacher use of performance goal practices increased and students' behavioral engagement declined across the four years. Using autoregressive latent trajectory (ALT) models, we examined the synchronous relations between teacher-reported performance goal practices and teacher-reported student behavioral engagement. As expected, as students move into classrooms with a new teacher with less emphasis on performance goal practices, they become more behaviorally engaged in school. Gender did not moderate these results. Implications for teacher professional development are discussed.
    Journal of school psychology 02/2011; 49(1):1-23. · 2.31 Impact Factor
  • Stephen G West, Ehri Ryu, Oi-Man Kwok, Heining Cham
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    ABSTRACT: Traditional statistical analyses can be compromised when data are collected from groups or multiple observations are collected from individuals. We present an introduction to multilevel models designed to address dependency in data. We review current use of multilevel modeling in 3 personality journals showing use concentrated in the 2 areas of experience sampling and longitudinal growth. Using an empirical example, we illustrate specification and interpretation of the results of series of models as predictor variables are introduced at Levels 1 and 2. Attention is given to possible trends and cycles in longitudinal data and to different forms of centering. We consider issues that may arise in estimation, model comparison, model evaluation, and data evaluation (outliers), highlighting similarities to and differences from standard regression approaches. Finally, we consider newer developments, including 3-level models, cross-classified models, nonstandard (limited) dependent variables, multilevel structural equation modeling, and nonlinear growth. Multilevel approaches both address traditional problems of dependency in data and provide personality researchers with the opportunity to ask new questions of their data.
    Journal of Personality 02/2011; 79(1):2-50. · 2.44 Impact Factor
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    ABSTRACT: Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x 1 2, x 1 x 4) to serve as indicators of each nonlinear latent construct. These approaches require the use of complex nonlinear constraints and additional model specifications and do not directly address the nonnormal distribution of the product terms. In contrast, recently developed, easy-to-use distribution analytic approaches do not use product indicators, but rather directly model the nonlinear multivariate distribution of the measured indicators. This article outlines the theoretical properties of the distribution analytic Latent Moderated Structural Equations (LMS; Klein & Moosbrugger, 2000) and Quasi-Maximum Likelihood (QML; Klein & Muthén, 2007) estimators. It compares the properties of LMS and QML to those of the product indicator approaches. A small simulation study compares the two approaches and illustrates the advantages of the distribution analytic approaches as multicollinearity increases, particularly in complex models with multiple nonlinear terms. An empirical example from the field of work stress applies LMS and QML to a model with an interaction and 2 quadratic effects. Example syntax for the analyses with both approaches is provided.
    Structural Equation Modeling A Multidisciplinary Journal 01/2011; 18(3):465-491. · 4.24 Impact Factor
  • Stephen G. West, Ehri Ryu
    Measurement Interdisciplinary Research and Perspectives 06/2010; 5(4):259-263.
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    Stephen G West, Felix Thoemmes
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    ABSTRACT: Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on the identification of threats to validity and the inclusion of design features that may prevent those threats from occurring or render them implausible. Rubin's approach focuses on the precise specification of both the possible outcomes for each participant and assumptions that are mathematically sufficient to estimate the causal effect. In this article, the authors compare the perspectives provided by the 2 approaches on randomized experiments, broken randomized experiments in which treatment nonadherence or attrition occurs, and observational studies in which participants are assigned to treatments on an unknown basis. The authors highlight dimensions on which the 2 approaches have different emphases, including the roles of constructs versus operations, threats to validity versus assumptions, methods of addressing threats to internal validity and violations of assumptions, direction versus magnitude of causal effects, role of measurement, and causal generalization. The authors conclude that investigators can benefit from drawing on the strengths of both approaches in designing research.
    Psychological Methods 03/2010; 15(1):18-37. · 4.45 Impact Factor
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    Wei Wu, Stephen G West, Jan N Hughes
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    ABSTRACT: In a 4-year longitudinal study, the authors investigated effects of retention in first grade on children's externalizing and internalizing behaviors; social acceptance; and behavioral, cognitive, and affective engagement. From a large multiethnic sample (n = 784) of children below the median on literacy at school entrance, 124 retained children were matched with 251 promoted children on the basis of propensity scores (probability of being retained in first grade estimated from 72 baseline variables). Relative to promoted children, retained children were found to benefit from retention in both the short and longer terms with respect to decreased teacher-rated hyperactivity, decreased peer-rated sadness and withdrawal, and increased teacher-rated behavioral engagement. Retained children had a short-term increase in mean peer-rated liking and school belongingness relative to promoted children, but this advantage showed a substantial decrease in the longer term. Retention had a positive short-term effect on children's perceived school belonging and a positive longer term effect on perceived academic self-efficacy. Retention may bestow advantages in the short-term, but longer term detrimental effects on social acceptance may lead to the documented longer term negative effects of retention.
    Journal of Educational Psychology 02/2010; 102(1):135-152. · 3.08 Impact Factor

Publication Stats

16k Citations
275.11 Total Impact Points


  • 1987–2014
    • Arizona State University
      • • Department of Psychology
      • • College of Nursing and Health Innovation
      Phoenix, Arizona, United States
  • 2012
    • Chestnut Hill College
      Boston, Massachusetts, United States
  • 2011
    • Texas A&M University
      • Department of Educational Psychology
      College Station, TX, United States
  • 2010
    • University of Kansas
      • Department of Psychology
      Lawrence, KS, United States
  • 2009
    • University of Michigan
      • Institute for Social Research
      Ann Arbor, MI, United States
  • 2004
    • University of Wisconsin, Madison
      • Department of Psychology
      Madison, MS, United States
    • Florida State University
      • Department of Medical Humanities & Social Sciences
      Tallahassee, FL, United States
  • 2000
    • University of North Carolina at Chapel Hill
      • Department of Psychology
      Chapel Hill, NC, United States
    • University of Washington Seattle
      • Department of Psychology
      Seattle, WA, United States
  • 1996
    • Indiana State University
      Indiana, United States
  • 1990
    • University of Alabama at Birmingham
      Birmingham, Alabama, United States