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Journal ArticleDOI

Establishing a Causal Relationship between Interventions to Promote Self-Determination and Enhanced Student Self-Determination.

TL;DR: A randomized trial placebo control group study of 371 high school students receiving special education services under the categorical areas of mental retardation or learning disabilities showed that although all students in the study showed improved self-determination over the 3 years of the study,Students in the intervention group showed significantly greater growth, though specific intraindividual variables affected this growth.
Abstract: Promoting the self-determination of adolescents with disabilities has become best practice in secondary education and transition services, but to date there have been no studies establishing a causal relationship between efforts to promote self-determination and enhancement of the self-determination of youth with disabilities. This article reports a randomized trial, placebo control group study of 371 high school students receiving special education services under the categorical areas of mental retardation or learning disabilities. Students were randomly assigned to an intervention or control group (by high school campus), with students in the intervention condition receiving multiple instructional components to promote self-determination. Latent growth curve analysis showed that although all students in the study showed improved self-determination over the three years of the study, students in the intervention group showed significantly greater growth, though specific intra-individual variables impacted this growth. Implications for research and intervention are discussed.

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Citations
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Journal ArticleDOI
TL;DR: In this article, a follow-up analysis of 779 students with disabilities who participated in group-randomized, control group studies designed to examine the efficacy of self-determination interventions in secondary school was conducted.
Abstract: This article reports the results of a follow-up analysis of 779 students with disabilities who participated in group-randomized, control group studies designed to examine the efficacy of self-determination interventions in secondary school to examine the relationship between self-determination status when exiting high school and adult outcomes 1 and 2 years post-high school. Findings suggest that self-determination status upon exiting high school predicts positive outcomes in the domains of achieving employment and community access 1 year post-school, and that exposure to self-determination interventions in secondary school may lead to more stability in student outcomes over time. The complexity of the relationship between self-determination intervention and outcomes is discussed, as are recommendations for future research and practice.

289 citations


Cites background or methods or result from "Establishing a Causal Relationship ..."

  • ...This finding is consistent with Wehmeyer et al. (2011) but not Wehmeyer et al. (2013)....

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  • ...The characteristics of the interventions are described more fully in Wehmeyer et al. (2013)....

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  • ...Wehmeyer et al. (2013) and Wehmeyer et al. (2011) conducted group-randomized, 1University of Illinois at Urbana–Champaign, USA 2University of Kansas, Lawrence, USA Corresponding Author: Karrie A. Shogren, Department of Special Education, University of Illinois, 1310 S. Sixth St, Champaign, IL…...

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  • ...Participants were involved in a large-scale project examining the impact of self-determination curricula described previously (Wehmeyer et al., 2011; Wehmeyer et al., 2013)....

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  • ...As further described in Wehmeyer et al. (2011) and Wehmeyer et al. (2013), each campus that agreed to participate was assigned to be a “treatment” or “control” campus....

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Journal Article
TL;DR: Causal agency theory as discussed by the authors is an extension of the functional model of self-determination that provides a theoretical framework for developing and enhancing supports to enable youth to engage in agentic action through instruction in goal setting and attainment strategies, to influence selfdetermination, causal agency, and overall well-being across diverse social contextual contexts.
Abstract: This paper introduces Causal Agency Theory, an extension of the functional model of self-determination. Causal Agency Theory addresses the need for interventions and assessments pertaining to self-determination for all students and incorporates the significant advances in understanding of disability and in the field of positive psychology since the introduction of the functional model of self-determination. Causal Agency Theory provides a theoretical framework for developing and enhancing supports to enable youth to engage in agentic action through instruction in goal setting and attainment strategies, to influence self-determination, causal agency, and overall well-being across diverse social-contextual contexts.

232 citations


Cites methods from "Establishing a Causal Relationship ..."

  • ..., 1996); operationalized by the development of an assessment linked to the theory (Wehmeyer & Kelchner, 1995); served as the foundation for intervention development, particularly with regard to the development of the Self-Determined Learning Model of Instruction and related efforts (Shogren, Palmer, Wehmeyer, Williams-Diehm, & Little, 2012; Wehmeyer, Palmer, Agran, Mithaug, & Martin, 2000; Wehmeyer et al., 2012); and provided impetus for a variety of research activities (see Wehmeyer et al....

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Journal ArticleDOI
TL;DR: In this paper, the authors present the results of a group-randomized, modified equivalent control group design study of the efficacy of the Self-Determined Learning Model of Instruction (SDLMI, Wehmeyer, Palmer, Agran, Mithaug, & Martin, 2000) to promote self-determination.
Abstract: Promoting self-determination has become a best practice in special education. There remains, however, a paucity of causal evidence for interventions to promote self-determination. This article presents the results of a group-randomized, modified equivalent control group design study of the efficacy of the Self-Determined Learning Model of Instruction (SDLMI, Wehmeyer, Palmer, Agran, Mithaug, & Martin, 2000) to promote self-determination. The authors used data on self-determination using multiple measures collected with 312 high school students with cognitive disabilities in both a control and a treatment group to examine the relationship between the SDLMI and self-determination. After determining strong measurement invariance for each latent construct, they found significant differences in latent means across measurement occasions and differential effects attributable to the SDLMI. This was true across disability category, though there was variance across disability populations.

207 citations

Journal ArticleDOI
TL;DR: Findings support the efficacy of the Self-Determined Learning Model of Instruction for both goal attainment and access to the general education curriculum, though students varied in the patterns of goal attainment as a function of type of disability.
Abstract: Promoting self-determination has been identified as best practice in special education and transition services and as a means to promote goal attainment and access to the general education curriculum for students with disabilities. There have been, however, limited evaluations of the effects of interventions to promote self-determination on outcomes related to access to the general education curriculum. This article reports findings from a cluster or group-randomized trial control group study examining the impact of intervention using the Self-Determined Learning Model of Instruction on students’ academic and transition goal attainment and on access to the general education curriculum for students with intellectual disability and learning disabilities. Findings support the efficacy of the model for both goal attainment and access to the general education curriculum, though students varied in the patterns of goal attainment as a function of type of disability.

192 citations

Journal ArticleDOI
TL;DR: Self-determination was confirmed as a partial mediator of enhanced quality of life and coaches for youth in the intervention group completed high school, were employed, and carried out independent living activities at notably higher rates than the comparison group.

164 citations

References
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Book
01 Jan 1999
TL;DR: In this paper, the authors proposed a multilevel regression model to estimate within-and between-group correlations using a combination of within-group correlation and cross-group evidence.
Abstract: Preface second edition Preface to first edition Introduction Multilevel analysis Probability models This book Prerequisites Notation Multilevel Theories, Multi-Stage Sampling and Multilevel Models Dependence as a nuisance Dependence as an interesting phenomenon Macro-level, micro-level, and cross-level relations Glommary Statistical Treatment of Clustered Data Aggregation Disaggregation The intraclass correlation Within-group and between group variance Testing for group differences Design effects in two-stage samples Reliability of aggregated variables Within-and between group relations Regressions Correlations Estimation of within-and between-group correlations Combination of within-group evidence Glommary The Random Intercept Model Terminology and notation A regression model: fixed effects only Variable intercepts: fixed or random parameters? When to use random coefficient models Definition of the random intercept model More explanatory variables Within-and between-group regressions Parameter estimation 'Estimating' random group effects: posterior means Posterior confidence intervals Three-level random intercept models Glommary The Hierarchical Linear Model Random slopes Heteroscedasticity Do not force ?01 to be 0! Interpretation of random slope variances Explanation of random intercepts and slopes Cross-level interaction effects A general formulation of fixed and random parts Specification of random slope models Centering variables with random slopes? Estimation Three or more levels Glommary Testing and Model Specification Tests for fixed parameters Multiparameter tests for fixed effects Deviance tests More powerful tests for variance parameters Other tests for parameters in the random part Confidence intervals for parameters in the random part Model specification Working upward from level one Joint consideration of level-one and level-two variables Concluding remarks on model specification Glommary How Much Does the Model Explain? Explained variance Negative values of R2? Definition of the proportion of explained variance in two-level models Explained variance in three-level models Explained variance in models with random slopes Components of variance Random intercept models Random slope models Glommary Heteroscedasticity Heteroscedasticity at level one Linear variance functions Quadratic variance functions Heteroscedasticity at level two Glommary Missing Data General issues for missing data Implications for design Missing values of the dependent variable Full maximum likelihood Imputation The imputation method Putting together the multiple results Multiple imputations by chained equations Choice of the imputation model Glommary Assumptions of the Hierarchical Linear Model Assumptions of the hierarchical linear model Following the logic of the hierarchical linear model Include contextual effects Check whether variables have random effects Explained variance Specification of the fixed part Specification of the random part Testing for heteroscedasticity What to do in case of heteroscedasticity Inspection of level-one residuals Residuals at level two Influence of level-two units More general distributional assumptions Glommary Designing Multilevel Studies Some introductory notes on power Estimating a population mean Measurement of subjects Estimating association between variables Cross-level interaction effects Allocating treatment to groups or individuals Exploring the variance structure The intraclass correlation Variance parameters Glommary Other Methods and Models Bayesian inference Sandwich estimators for standard errors Latent class models Glommary Imperfect Hierarchies A two-level model with a crossed random factor Crossed random effects in three-level models Multiple membership models Multiple membership multiple classification models Glommary Survey Weights Model-based and design-based inference Descriptive and analytic use of surveys Two kinds of weights Choosing between model-based and design-based analysis Inclusion probabilities and two-level weights Exploring the informativeness of the sampling design Example: Metacognitive strategies as measured in the PISA study Sampling design Model-based analysis of data divided into parts Inclusion of weights in the model How to assign weights in multilevel models Appendix. Matrix expressions for the single-level estimators Glommary Longitudinal Data Fixed occasions The compound symmetry models Random slopes The fully multivariate model Multivariate regression analysis Explained variance Variable occasion designs Populations of curves Random functions Explaining the functions 27415.2.4 Changing covariates Autocorrelated residuals Glommary Multivariate Multilevel Models Why analyze multiple dependent variables simultaneously? The multivariate random intercept model Multivariate random slope models Glommary Discrete Dependent Variables Hierarchical generalized linear models Introduction to multilevel logistic regression Heterogeneous proportions The logit function: Log-odds The empty model The random intercept model Estimation Aggregation Further topics on multilevel logistic regression Random slope model Representation as a threshold model Residual intraclass correlation coefficient Explained variance Consequences of adding effects to the model Ordered categorical variables Multilevel event history analysis Multilevel Poisson regression Glommary Software Special software for multilevel modeling HLM MLwiN The MIXOR suite and SuperMix Modules in general-purpose software packages SAS procedures VARCOMP, MIXED, GLIMMIX, and NLMIXED R Stata SPSS, commands VARCOMP and MIXED Other multilevel software PinT Optimal Design MLPowSim Mplus Latent Gold REALCOM WinBUGS References Index

9,578 citations

Book
01 Jan 2011
TL;DR: In this paper, Glommary et al. proposed a multilevel regression model with a random intercept model to estimate within-and between-group regressions, which is based on a hierarchical linear model.
Abstract: Preface second edition Preface to first edition Introduction Multilevel analysis Probability models This book Prerequisites Notation Multilevel Theories, Multi-Stage Sampling and Multilevel Models Dependence as a nuisance Dependence as an interesting phenomenon Macro-level, micro-level, and cross-level relations Glommary Statistical Treatment of Clustered Data Aggregation Disaggregation The intraclass correlation Within-group and between group variance Testing for group differences Design effects in two-stage samples Reliability of aggregated variables Within-and between group relations Regressions Correlations Estimation of within-and between-group correlations Combination of within-group evidence Glommary The Random Intercept Model Terminology and notation A regression model: fixed effects only Variable intercepts: fixed or random parameters? When to use random coefficient models Definition of the random intercept model More explanatory variables Within-and between-group regressions Parameter estimation 'Estimating' random group effects: posterior means Posterior confidence intervals Three-level random intercept models Glommary The Hierarchical Linear Model Random slopes Heteroscedasticity Do not force ?01 to be 0! Interpretation of random slope variances Explanation of random intercepts and slopes Cross-level interaction effects A general formulation of fixed and random parts Specification of random slope models Centering variables with random slopes? Estimation Three or more levels Glommary Testing and Model Specification Tests for fixed parameters Multiparameter tests for fixed effects Deviance tests More powerful tests for variance parameters Other tests for parameters in the random part Confidence intervals for parameters in the random part Model specification Working upward from level one Joint consideration of level-one and level-two variables Concluding remarks on model specification Glommary How Much Does the Model Explain? Explained variance Negative values of R2? Definition of the proportion of explained variance in two-level models Explained variance in three-level models Explained variance in models with random slopes Components of variance Random intercept models Random slope models Glommary Heteroscedasticity Heteroscedasticity at level one Linear variance functions Quadratic variance functions Heteroscedasticity at level two Glommary Missing Data General issues for missing data Implications for design Missing values of the dependent variable Full maximum likelihood Imputation The imputation method Putting together the multiple results Multiple imputations by chained equations Choice of the imputation model Glommary Assumptions of the Hierarchical Linear Model Assumptions of the hierarchical linear model Following the logic of the hierarchical linear model Include contextual effects Check whether variables have random effects Explained variance Specification of the fixed part Specification of the random part Testing for heteroscedasticity What to do in case of heteroscedasticity Inspection of level-one residuals Residuals at level two Influence of level-two units More general distributional assumptions Glommary Designing Multilevel Studies Some introductory notes on power Estimating a population mean Measurement of subjects Estimating association between variables Cross-level interaction effects Allocating treatment to groups or individuals Exploring the variance structure The intraclass correlation Variance parameters Glommary Other Methods and Models Bayesian inference Sandwich estimators for standard errors Latent class models Glommary Imperfect Hierarchies A two-level model with a crossed random factor Crossed random effects in three-level models Multiple membership models Multiple membership multiple classification models Glommary Survey Weights Model-based and design-based inference Descriptive and analytic use of surveys Two kinds of weights Choosing between model-based and design-based analysis Inclusion probabilities and two-level weights Exploring the informativeness of the sampling design Example: Metacognitive strategies as measured in the PISA study Sampling design Model-based analysis of data divided into parts Inclusion of weights in the model How to assign weights in multilevel models Appendix Matrix expressions for the single-level estimators Glommary Longitudinal Data Fixed occasions The compound symmetry models Random slopes The fully multivariate model Multivariate regression analysis Explained variance Variable occasion designs Populations of curves Random functions Explaining the functions 2741524 Changing covariates Autocorrelated residuals Glommary Multivariate Multilevel Models Why analyze multiple dependent variables simultaneously? The multivariate random intercept model Multivariate random slope models Glommary Discrete Dependent Variables Hierarchical generalized linear models Introduction to multilevel logistic regression Heterogeneous proportions The logit function: Log-odds The empty model The random intercept model Estimation Aggregation Further topics on multilevel logistic regression Random slope model Representation as a threshold model Residual intraclass correlation coefficient Explained variance Consequences of adding effects to the model Ordered categorical variables Multilevel event history analysis Multilevel Poisson regression Glommary Software Special software for multilevel modeling HLM MLwiN The MIXOR suite and SuperMix Modules in general-purpose software packages SAS procedures VARCOMP, MIXED, GLIMMIX, and NLMIXED R Stata SPSS, commands VARCOMP and MIXED Other multilevel software PinT Optimal Design MLPowSim Mplus Latent Gold REALCOM WinBUGS References Index

4,162 citations

01 Jan 2005
TL;DR: The authors call for applied research to better understand service delivery processes and contextual factors to improve the efficiency and effectiveness of program implementation at local state and national levels.
Abstract: In the past few years several major reports highlighted the gap between our knowledge of effective treatments and services currently being received by consumers. These reports agree that we know much about interventions that are effective but make little use of them to help achieve important behavioral health outcomes for children families and adults nationally. This theme is repeated in reports by the Surgeon General (United States Department of Health and Human Services 1999; 2001) the National Institute of Mental Health [NIMH] National Advisory Mental Health Council Workgroup on Child and Adolescent Mental Health Intervention Development and Deployment (2001) Bernfeld Farrington & Leschied (2001) Institute of Medicine (2001) and the Presidents New Freedom Commission on Mental Health (2003). The authors call for applied research to better understand service delivery processes and contextual factors to improve the efficiency and effectiveness of program implementation at local state and national levels. Our understanding of how to develop and evaluate evidence-based intervention programs has been furthered by on-going efforts to research and refine programs and practices to define "evidence bases" and to designate and catalogue "evidence-based programs or practices". However the factors involved in successful implementation of these programs are not as well understood. Current views of implementation are based on the scholarly foundations prepared by Pressman & Wildavskys (1973) study of policy implementation Havelock & Havelocks (1973) classic curriculum for training change agents and Rogers (1983; 1995) series of analyses of factors influencing decisions to choose a given innovation. These foundations were tested and further informed by the experience base generated by pioneering attempts to implement Fairweather Lodges and National Follow-Through education models among others. Petersilia (1990) concluded that "The ideas embodied in innovative social programs are not self-executing." Instead what is needed is an "implementation perspective on innovation--an approach that views postadoption events as crucial and focuses on the actions of those who convert it into practice as the key to success or failure". (excerpt)

3,603 citations

Journal ArticleDOI
TL;DR: This paper is written as a step-by-step tutorial that shows how to fit the two most common multilevel models: (a) school effects models, designed for data on individuals nested within naturally occurring hierarchies (e.g., students within classes); and (b) individual growth models,designed for exploring longitudinal data (on individuals) over time.
Abstract: SAS PROC MIXED is a flexible program suitable for fitting multilevel models, hierarchical linear models, and individual growth models. Its position as an integrated program within the SAS statistic...

2,903 citations

Book
03 Apr 2000
TL;DR: A First Course in Structural Equation Modeling as discussed by the authors is an excellent introductory book for structural equation modeling with examples from EQS, LISREL, and Mplus, which can be used to set up input files to fit the most commonly used types of structural equation models with these programs.
Abstract: In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner’s guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.Highlights of the Second Edition include:• Review of latent change (growth) analysis models at an introductory level• Coverage of the popular Mplus program• Updated examples of LISREL and EQS• Downloadable resources that contains all of the text’s LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

1,548 citations