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Journal ArticleDOI

Why Students Choose STEM Majors Motivation, High School Learning, and Postsecondary Context of Support

01 Oct 2013-American Educational Research Journal (SAGE Publications)-Vol. 50, Iss: 5, pp 1081-1121
TL;DR: In this article, a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions was proposed.
Abstract: This study draws upon social cognitive career theory and higher education literature to test a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions. Results suggest that choosing a STEM major is directly influenced by intent to major in STEM, high school math achievement, and initial postsecondary experiences, such as academic interaction and financial aid receipt. Exerting the largest impact on STEM entrance, intent to major in STEM is directly affected by 12th-grade math achievement, exposure to math and science courses, and math self-efficacy beliefs—all three subject to the influence of early achievement in and attitudes toward math. Multiple-group structural equation modeling analyses indicated heterogeneous effects of math achievement and exposure to math and science across racial groups, with their positive impact on STEM intent accruing most to White students and least ...

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Journal Article
TL;DR: Arum and Roksa as mentioned in this paper argue that students gain surprisingly little from their college experience, that there is "persistent and growing inequality" in the students' learning, and that "there is notable variation both within and across institutions" so far as "measurable differences in students' educational experiences" is concerned.
Abstract: Academically Adrift: Limited Learning on College Campuses Richard Arum and Josipa Roksa University of Chicago Press, 2011 This book has much to say that is perceptive about today's undergraduate higher education in the United States. It will be valuable to review the authors' insights. At the same time, it will be as instructive to note the book's weaknesses, and especially what is omitted from the discussion. It is a discussion that is truncated intellectually by the authors' close adherence to the selective awareness that so greatly typifies the mindscape of the contemporary American "establishment" in academia and throughout the commanding heights of American society. That mindscape allows a recognition of many things, but not of others. The authors are both faculty members at major American universities. Richard Arum is a sociology professor at New York University with a tie to the university's school of education. He is the author of several books on education and director of the Education Research Program sponsored by the Social Science Research Council. His co-author, Josipa Roksa, is an assistant professor of sociology at the University of Virginia. That the book is published by the University of Chicago Press attests to its presumptive merit. Academically Adrift furnishes an example of something that has long been common in social science writing: a rather thin empirical study serving as the work's own contribution, combined with considerable additional material coming out of the literature on whatever subject is being explored. The function of the authors' own research is thus often to serve more or less as scientistic windowdressing. The reason we say the empiricism for this book is "thin" is that the "longitudinal data of 2,322 students," while seemingly ample, involves students spread over "a diverse range of campuses," including "liberal arts colleges and large research institutions, as well as a number of historically black colleges and universities and Hispanic-serving institutions," all "dispersed nationally across all four regions of the country." This must necessarily mean that the "sample" from any given institution or program was quite small. We are told that the authors didn't concern themselves with the appropriateness of each sample, but left the recruitment and retention of the sample's students to each of the respective institutions. The authors acknowledge that the study included fewer men than women, and more good students than those of "lower scholastic ability." So far as this book is concerned, however, the thinness doesn't particularly hurt the content, since so much of what is said doesn't especially depend upon anything unique found by the authors' own research. A brief summary is provided when the authors say that "we will highlight four core 'important lessons' from our research." These are that the institutions and students are "academically adrift" (which is the basis for the book's title), that students gain surprisingly little from their college experience, that there is "persistent and growing inequality" in the students' learning, and that "there is notable variation both within and across institutions" so far as "measurable differences in students' educational experiences" is concerned. Following the lead of former president Derek Bok of Harvard and of the Council for Aid to Education, the authors' ideal for higher education is that it will enhance students' "capacity for critical thinking, complex reasoning, and writing." These are the three ingredients measured by the Collegiate Learning Assessment (CLA), which the authors value most among the various assessment tools. The CLA results, they say, show that "growing numbers of students are sent to college at increasingly higher costs, but for a large proportion of them the gains in critical thinking, complex reasoning and written communication are either exceedingly small or empirically nonexistent. …

663 citations

Journal ArticleDOI
TL;DR: This study shows that participation in course-based undergraduate research experiences improves students’ likelihood of graduating with a STEM degree and graduating within 6 years.
Abstract: This study shows that participation in course-based undergraduate research experiences (CUREs) improves students’ likelihood of graduating with a STEM degree and graduating within 6 years. These re...

242 citations


Cites background or methods from "Why Students Choose STEM Majors Mot..."

  • ...…in FRI and students’ choice to major in STEM: SAT total score or ACT equivalent as a measure of prior academic achievement, number of high school science credits earned as a measure of science preparation, and number of high school math credits earned as a measure of math preparation (Wang, 2013)....

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  • ...We also included variables that have been shown to be associated with enrollment in FRI and students’ choice to major in STEM: SAT total score or ACT equivalent as a measure of prior academic achievement, number of high school science credits earned as a measure of science preparation, and number of high school math credits earned as a measure of math preparation (Wang, 2013)....

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Journal ArticleDOI
TL;DR: In this paper, the authors developed and tested a model of factors contributing to science, technology, engineering, and mathematics (STEM) learning and career orientation, examining the complex paths and relationships among social, motivational, and instructional factors underlying these outcomes for middle school youth.
Abstract: The purpose of this research was to develop and test a model of factors contributing to science, technology, engineering, and mathematics (STEM) learning and career orientation, examining the complex paths and relationships among social, motivational, and instructional factors underlying these outcomes for middle school youth. Social cognitive career theory provided the foundation for the research because of its emphasis on explaining mechanisms which influence both career orientations and academic performance. Key constructs investigated were youth STEM interest, self-efficacy, and career outcome expectancy (consequences of particular actions). The study also investigated the effects of prior knowledge, use of problem-solving learning strategies, and the support and influence of informal educators, family members, and peers. A structural equation model was developed, and structural equation modeling procedures were used to test proposed relationships between these constructs. Results showed that educator...

228 citations


Cites background from "Why Students Choose STEM Majors Mot..."

  • ...Research has confirmed this proposition, showing that self-efficacy is a predictor of students’ college major, career choices, and career aspirations (Adedokun, Bessenbacher, Parker, Kirkham, & Burgess, 2013; Brown & Lent, 2006; Vedder-Weiss & Fortus, 2012; Wang, 2013)....

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  • ...Although exposure to mathematics and science courses has been shown to be related to student intent to major in STEM (Wang, 2013), in this study it was not as powerful an indicator as was youths’ direct indication of career interest....

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  • ...Path models examining these learning and motivational relationships have been developed for a university-age audience (Ferry, Fouad, & Smith, 2000; Mills, 2009) or high school audience (Papanastasiou & Zembylas, 2004; Pietsch, Walker, & Chapman, 2003; Wang, 2013)....

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  • ...Exposure to mathematics and (Maltese & Tai, 2011; Wang, 2013)....

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Journal ArticleDOI
TL;DR: The results suggest that both math self-concept and intrinsic value interact in predicting advanced math course selection, matriculation results, entrance into university, and STEM fields of study andGender differences in educational outcomes are mediated by gender differences in motivational beliefs and prior academic achievement.
Abstract: Drawing on the expectancy-value model, the present study explored individual and gender differences in university entry and selection of educational pathway (e.g., science, technology, engineering, and mathematics [STEM] course selection). In particular, we examined the multiplicative effects of expectancy and task values on educational outcomes during the transition into early adulthood. Participants were from a nationally representative longitudinal sample of 15-year-old Australian youths (N = 10,370). The results suggest that (a) both math self-concept and intrinsic value interact in predicting advanced math course selection, matriculation results, entrance into university, and STEM fields of study; (b) prior reading achievement has negative effects on advanced math course selection and STEM fields through math motivational beliefs; and (c) gender differences in educational outcomes are mediated by gender differences in motivational beliefs and prior academic achievement, while the processes underlying choice of educational pathway were similar for males and females.

202 citations

Journal ArticleDOI
TL;DR: This article used story-based instruction to model how scientists achieve through failures and struggles, and found that participation in either of the struggle story conditions improved science learning post-intervention, relative to that of students in the control condition.
Abstract: Students’ beliefs that success in science depends on exceptional talent negatively impact their motivation to learn. For example, such beliefs have been shown to be a major factor steering students away from taking science and math courses in high school and college. In the present study, we tested a novel story-based instruction that models how scientists achieve through failures and struggles. We designed this instruction to challenge this belief, thereby improving science learning in classroom settings. A demographically diverse group of 402 9th and 10th grade students read 1 of 3 types of stories about eminent scientists that described how the scientists (a) struggled intellectually (e.g., made mistakes in investigating scientific problems, and overcame the mistakes through effort), (b) struggled in their personal life (e.g., suffered family poverty and lack of parental support but overcame it), or (c) made great discoveries (a control condition, similar to the instructional material that appears in many science textbooks, that did not describe any struggles). Results showed that participation in either of the struggle story conditions improved science learning postintervention, relative to that of students in the control condition. Additionally, the effect of our intervention was more pronounced for low-performing students. Moreover, far more students in either of the struggle story conditions felt connected to the stories and scientists than did students in the control condition. The use of struggle stories provides a promising and implementable instructional approach that can improve student motivation and academic performance in science and perhaps other subjects as well. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

190 citations


Cites background from "Why Students Choose STEM Majors Mot..."

  • ...Belief in the necessity of exceptional scientific talents has been shown to be one of the major factors steering students away from science and math courses in both high school and college (Blickenstaff, 2005; Singh, Granville, & Dika, 2002; Wang, 2013)....

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References
More filters
Book
27 May 1998
TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.

42,102 citations


"Why Students Choose STEM Majors Mot..." refers background or methods in this paper

  • ...Following the full-sample SEM analysis, multiple-group analyses were employed to examine whether the hypothesized model was equivalent across subgroups....

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  • ...Prior to multiple-group analyses, the SEM model was analyzed based on the whole sample, and fit indices suggested excellent model-to-data fit (line Why Students Choose STEM Majors T a b le 2 D e s c ri p ti v e S ta ti s ti c s o f D e m o g ra p h ic C h a ra c te ri s ti c s o f th e S a m p le , U n w e ig h te d a n d W e ig h te d ST E M In te n t (2 0 0 4 ) ST E M E n tr an ce (2 0 0 6 ) T o ta l N In te n d e d to M aj o r in ST E M D id N o t In te n d D e cl ar e d a ST E M M aj o r D id N o t D e cl ar e a ST E M M aj o r N W td N N (% ) W td N (% ) N (% ) W td N (% ) N (% ) W td N (% ) N (% ) W td N (% ) T o ta l 6 ,3 0 0 1 ,5 6 0 ,0 5 0 1 ,2 2 0 (1 9 .3 ) 3 0 2 ,8 6 0 (1 9 .4 ) 5 ,0 9 0 (8 0 .7 ) 1 ,2 5 7 ,1 8 0 (8 0 .6 ) 9 7 0 (1 5 .4 ) 2 4 0 ,6 7 0 (1 5 .4 ) 5 ,3 3 0 (8 4 .6 ) 1 ,3 1 9 ,3 7 0 (8 4 .6 ) G e n d e r F e m al e 3 ,4 4 0 8 5 1 ,2 0 0 3 7 0 (1 0 .9 ) 9 2 ,6 3 0 (1 0 .9 ) 3 ,0 6 0 (8 9 .1 ) 7 5 8 ,5 8 0 (8 9 .1 ) 3 5 0 (1 0 .2 ) 8 7 ,3 2 0 (1 0 .3 ) 3 ,0 8 0 (8 9 .8 ) 7 6 3 ,8 8 0 (8 9 .7 ) M al e 2 ,8 7 0 7 0 8 ,8 4 0 8 4 0 (2 9 .3 ) 2 1 0 ,2 4 0 (2 9 .7 ) 2 ,0 3 0 (7 0 .7 ) 4 9 8 ,6 1 0 (7 0 .3 ) 6 2 0 (2 1 .6 ) 1 5 3 ,3 6 0 (2 1 .6 ) 2 ,2 5 0 (7 8 .5 ) 5 5 5 ,4 9 0 (7 8 .4 ) R ac e /e th n ic it y W h it e 4 ,0 5 0 9 9 6 ,6 6 0 7 2 0 (1 7 .7 ) 1 8 3 ,2 9 0 (1 8 .4 ) 3 ,3 4 0 (8 2 .3 ) 8 1 3 ,3 7 0 (8 1 .6 ) 5 6 0 (1 3 .9 ) 1 4 3 ,3 6 0 (1 4 .4 ) 3 ,4 9 0 (8 6 .1 ) 8 5 3 ,3 0 0 (8 5 .6 ) A si an 7 5 0 1 8 1 ,2 4 0 2 0 0 (2 6 .7 ) 4 4 ,3 0 0 (2 4 .4 ) 5 5 0 (7 3 .3 ) 1 3 6 ,9 4 0 (7 5 .6 ) 1 8 0 (2 4 .0 ) 4 2 ,2 2 0 (2 3 .3 ) 5 7 0 (7 6 .0 ) 1 3 9 ,0 2 0 (7 6 .7 ) U n d e rr e p re se n te d m in o ri ti e s B la ck 6 6 0 1 6 6 ,0 9 0 1 4 0 (2 0 .8 ) 3 5 ,1 7 0 (2 1 .2 ) 5 3 0 (7 9 .2 ) 1 3 0 ,9 2 0 (7 8 .8 ) 1 1 0 (1 7 .2 ) 3 0 ,8 4 0 (1 8 .6 ) 5 5 0 (8 2 .8 ) 1 3 5 ,2 5 0 (8 1 .4 ) H is p an ic 5 2 0 1 3 5 ,4 8 0 1 0 0 (1 8 .7 ) 2 5 ,8 6 0 (1 9 .1 ) 4 3 0 (8 1 .3 ) 1 0 9 ,6 2 0 (8 0 .9 ) 7 0 (1 2 .8 ) 1 4 ,4 7 0 (1 0 .7 ) 4 6 0 (8 7 .2 ) 1 2 1 ,0 0 0 (8 9 .3 ) A m e ri ca n In d ia n 3 0 6 ,9 2 0 1 0 (2 5 .0 ) 1 ,3 9 0 (2 0 .0 ) 2 0 (7 5 .0 ) 5 ,5 4 0 (8 0 .0 ) 0 (7 .1 ) 2 4 0 (3 .5 ) 3 0 (9 2 .9 ) 6 ,6 8 0 (9 6 .5 ) M u lt ir ac ia l 2 8 0 7 3 ,6 6 0 5 0 (1 8 .5 ) 1 2 ,8 7 0 (1 7 .5 ) 2 3 0 (8 1 .5 ) 6 0 ,8 0 0 (8 2 .5 ) 4 0 (1 4 .9 ) 9 ,5 4 0 (1 3 .0 ) 2 4 0 (8 5 .1 ) 6 4 ,1 2 0 (8 7 .0 ) So c io e c o n o m ic st at u s Lo w e st q u ar ti le 7 0 0 1 7 3 ,8 3 0 1 6 0 (2 2 .2 ) 3 7 ,2 5 0 (2 1 .4 ) 5 5 0 (7 7 .8 ) 1 3 6 ,5 8 0 (7 8 .6 ) 9 0 (1 2 .4 ) 2 0 ,1 5 0 (1 1 .6 ) 6 2 0 (8 7 .6 ) 1 5 3 ,6 8 0 (8 8 .4 ) Se co n d q u ar ti le 1 ,0 5 0 2 6 8 ,5 8 0 1 8 0 (1 7 .4 ) 4 7 ,9 4 0 (1 7 .9 ) 8 7 0 (8 2 .6 ) 2 2 0 ,6 4 0 (8 2 .1 ) 1 4 0 (1 3 .0 ) 3 4 ,6 1 0 (1 2 .9 ) 9 1 0 (7 8 .0 ) 2 3 3 ,9 7 0 (8 7 .1 ) T h ir d q u ar ti le 1 ,6 2 0 3 9 8 ,6 4 0 2 7 0 (1 6 .8 ) 6 9 ,0 8 0 (1 7 .3 ) 1 ,3 5 0 (8 3 .2 ) 3 2 9 ,5 6 0 (8 2 .7 ) 2 3 0 (1 4 .2 ) 5 6 ,2 4 0 (1 4 .1 ) 1 ,3 9 0 (8 5 .8 ) 3 4 2 ,4 0 0 (8 5 .9 ) H ig h e st q u ar ti le 2 ,9 3 0 7 1 9 ,0 0 0 6 0 0 (2 0 .6 ) 1 4 8 ,5 9 0 (2 0 .7 ) 2 ,3 2 0 (7 9 .4 ) 5 7 0 ,4 1 0 (7 9 .3 ) 5 2 0 (1 7 .6 ) 1 2 9 ,6 7 0 (1 8 .0 ) 2 ,4 1 0 (8 2 .4 ) 5 8 9 ,3 3 0 (8 2 .0 ) N o te ....

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  • ...Why Students Choose STEM Majors The potentially varying effects of the modeled factors were examined through conducting multiple-group SEM analyses based on race, gender, and SES....

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  • ...Figure 2 is a depiction of the structural part of the SEM diagram based on the theoretical model....

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  • ...1Following the suggestion made by one of the reviewers, two sets of high school variables were also analyzed as additional covariates in the structural equation modeling (SEM) model: (a) family background including first-generation status (1 = first-generation college student; 0 = continuing generation) and language background (1 = English is native language; 0 = English is not native language) and (b) high school context variables including percentage of the school’s students that qualify for free/reduced-price lunch, percentage of minority students in the school, student-teacher ratio of the school, high school type (dummy coded into Catholic, other private, and public as the referent group), and urbanicity of the school (dummy coded into suburban, rural, and urban as the referent group)....

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Book
21 Jul 2011
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Abstract: Psychology is a science that advances by leaps and bounds The impulse of new mathematical models along with the incorporation of computers to research has drawn a new reality with many methodological progresses that only a few people could imagine not too long ago Such progress has no doubt revolutionized the panorama of research in the behavioral sciences Structural Equation Models are a clear example of this Under this label are usually included a series of state-of-the-art multivariate statistical procedures that allow the researcher to test theoryguided hypotheses with clearly confi rmatory ends as well as to establish causal relations among variables Confi rmatory factor analysis, the study of measurement invariance, or the multitraitmultimethod models are some of the procedures that stem from this methodology In this sense, it would be diffi cult to fi nd a scientifi c journal that publishes empirical works in psychology that does not address some of these issues, so their current transcendence is undeniable The manual written by the Full Professor of the University of Ottawa, Barbara M Byrne, is a link in a series of books that address this topic Throughout her long academic trajectory, Professor Byrne developed interesting and popular work focused on bringing the researcher and the professional layman—and not so layman—closer to the diverse statistical programs available on the market for data analysis from the perspective of structural equation models (ie, LISREL, AMOS, EQS) (Byrne, 1998, 2001, 2006) Bearing this in mind, the main goal of this work is to introduce the reader to the basic concepts of this methodology, in a simple and entertaining way, avoiding mathematical technicisms and statistical jargon For this purpose, we used the statistical program Mplus 60 (Muthen & Muthen, 2007-2010), an extremely suggestive software that incorporates interesting applications The authoress provides a practical guide that leads the reader through illustrative examples of how to proceed step by step with the Mplus, from the initial specifi cations of the model to the interpretation of the output fi les On the one hand, we underline that the data used proceed from prior investigations and can be consulted in the Internet, offering the reader the possibility of practicing with them (http://wwwpsypresscom/sem-with-mplus/ datasets/); on the other hand, updating the information with novel and apt bibliographic references allows the reader to study in more depth the diverse topics that are presented in the manual, if he or she so desires The book consists of four sections, with a total of 12 chapters The fi rst section, Chapters 1 and 2, addresses introductory terms related to structural equation models and working with the Mplus program at a user-level The second unit focuses on data analysis with a single group In Chapter 3, the factor validity of the self-concept is tested by means of confi rmatory factor analysis In Chapter 4, the authoress performs a fi rst-order confi rmatory factor analysis, in which she examines the validity of the scores of the Maslach Burnout Inventory (MBI) in a sample of teachers In Chapter 5, the internal structure of the scores on the Beck Depression Inventory-II is analyzed by means of second-order confi rmatory factor analysis in a sample of Chinese adolescents In the next chapter, the complete model of structural equations is tested, and the authoress examines the causal relation established between diverse variables (ie, work climate, self-esteem, social support) and Burnout The third section of the manual is, in my opinion, the most interesting, not only because of the expansion of the study of measurement invariance in recent years but also because of the expansion it may possibly have in the future In this section, Professor Byrne goes into multigroup comparisons Specifi cally, in Chapter 7, she examines the factor equivalence of the MBI in two samples of teachers by means of the analysis of covariance structures In this chapter, she introduces relevant concepts, such as types of invariance (confi gural, metric, and strict) or the invariance of partial measurement In Chapter 8, she also analyzes measurement invariance, using for this purpose the analysis of mean and covariance structures This analysis, in comparison to the analysis of covariance structures, allows contrasting the latent means of two or more groups With this goal, she verifi es whether there is measurement invariance between the scores on the Self-description Questionnaire-I in Nigerian and Australian adolescents In Chapter 9, she proposes a complete model of structural equations in which she tests the causal structure through the procedure of cross validation Lastly, in the fourth section, she reveals three very interesting topics, that are also up-to-date and that, to some degree, go beyond the initial goal of the book, such as the multitrait-multimethod models, latent growth curves, and multilevel models Summing up, the work “Structural Equation Modeling with Mplus: Basic concepts, applications, and programming” is of enormous interest and utility for all professionals of psychology and related sciences who, without having exhaustive knowledge of the details of structural equation models, wish to test their hypothetical models by means of the Mplus program No doubt, this is a reference manual, a must-read that is accessible and that has a high degree of methodological rigor We hope that the readers

16,616 citations


"Why Students Choose STEM Majors Mot..." refers background in this paper

  • ...On the contrary, a significant Dx2 statistic would suggest that the given parameter was not equivalent across gender groups; therefore, it would be freely estimated in the subsequent models for invariance tests (Byrne, 2010; Kline, 2011)....

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Book
01 Nov 2000
TL;DR: In this article, the EQS program is used to test the factorial verifiability of a theoretical construct and its invariance to a Causal Structure using the First-Order CFA model.
Abstract: Contents: Part I: Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part II: Single-Group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part III: Multiple-Group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-Order CFA Model). Application 8: Testing for Latent Mean Differences (Second-Order CFA Model). Part IV: Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model.

13,439 citations

Book
11 Oct 1985
TL;DR: In this paper, models of Human Nature and Casualty are used to model human nature and human health, and a set of self-regulatory mechanisms are proposed. But they do not consider the role of cognitive regulators.
Abstract: 1. Models of Human Nature and Casualty. 2. Observational Learning. 3. Enactive Learning. 4. Social Diffusion and Innovation. 5. Predictive Knowledge and Forethought. 6. Incentive Motivators. 7. Vicarious Motivators. 8. Self-Regulatory Mechanisms. 9. Self-Efficacy. 10. Cognitive Regulators. References. Index.

11,264 citations

Journal ArticleDOI
TL;DR: In this article, a framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented, where the value of confidence intervals for fit indices is emphasized.
Abstract: A framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the relationship of confidence intervals to a framework for hypothesis testing. The approach allows for testing null hypotheses of not-good fit, reversing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit. The approach also allows for direct estimation of power, where effect size is defined in terms of a null and alternative value of the root-mean-square error of approximation fit index proposed by J. H. Steiger and J. M. Lind (1980). It is also feasible to determine minimum sample size required to achieve a given level of power for any test of fit in this framework. Computer programs and examples are provided for power analyses and calculation of minimum sample sizes.

8,401 citations

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