Analysis Of Educational Research Research Articles

The latent curve ARMA (p, q) panel model: longitudinal data analysis in educational research and evaluation

Autocorrelated residuals in longitudinal data are widely reported as common to longitudinal data. Yet few, if any, researchers modeling growth processes evaluate a priori whether their data have this feature. Sivo, Fan, and Witta (2005) found that not modeling autocorrelated residuals present in longitudinal data severely biases latent curve parameter estimates. The purpose of this article is to explain how educational researchers and evaluators analyzing longitudinal data might approach longitudinal data in which change is hypothesized to occur over time. Specification of the latent curve ARMA model (i.e., the growth curve ARMA model) is introduced as an approach to filtering out the effects of autocorrelation on latent curve parameter estimates by modeling this nuisance condition so that the estimates of primary interest are more accurate.

PITFALLS AND PROMISES: THE USE OF SECONDARY DATA ANALYSIS IN EDUCATIONAL RESEARCH

ABSTRACT: This paper considers the use of secondary data analysis in educational research. It addresses some of the promises and potential pitfalls that influence its use and explores a possible role for the secondary analysis of numeric data in the ‘new’ political arithmetic tradition of social research. Secondary data analysis is a relatively under-used technique in Education and in the social sciences more widely, and it is an approach that is not without its critics. Here we consider two main objections to the use of secondary data: that it is full of errors and that because of the socially constructed nature of social data, simply reducing it to a numeric form cannot fully encapsulate its complexity. However, secondary data also offers numerous methodological, theoretical and pedagogical benefits. Indeed by treating secondary data analysis with appropriate scepticism and respect for its limitations, by demanding that tacit assumptions about the unreliability of secondary data are applied equally to other research methods, and crucially by combining secondary data analysis with small-scale in-depth work, this paper argues for a return to prominence of secondary data analysis in its own right as well as becoming a central component of the new political arithmetic tradition of social research.

Metaphors as a Bridge to Understanding Educational and Social Contexts

Educational researchers and practitioners are frequently asking questions about how better to understand educational theory and practice. Through the years, they have employed a variety of both quantitative and qualitative methods to elucidate the world of education. In this article, the author explores the epistemological legitimacy of metaphor analysis as a viable means for qualitative educational inquiry. In so doing, he explores the concepts of the theory of abduction, educational research and social constructivism, categories of metaphors, and metaphorical analysis in educational research. In addition, a review of the literature on educational research that uses metaphor analysis as the primary methodology revealed five major themes.

Pitfalls in Pathways: Some Perspectives on Competing Risks Event History Analysis in Education Research

A set of discrete-time methods for competing risks event history analysis is presented. The approach used is accessible to the practitioner and the article describes the strengths, weaknesses, and interpretation of both exploratory and model-based tools. These techniques are applied to the impact of “nontraditional” enrollment features (working, stopout, and delayed enrollment) on competing outcomes of sub-baccalaureate enrollment (degree attainment, transfer, and dropout). In this setting, model selection, estimation, and comparative inference are discussed and more general guidelines for each stage of analysis are provided. Substantively, the study finds that nontraditional enrollment features are associated with dramatic changes in expected outcomes.

Analysis of educational research as a component of pre-service education

Analysis of educational research as a component of pre-service education

Some Problems and Purposes of Narrative Analysis in Educational Research

The significance of narrative analysis for education research is discussed by reviewing general work on the nature of narrative and examining some of the ways in which narratives vary, especially between cultural groups, by reviewing some of the uses of narratives in classroom activities and classroom-related research, and, finally, by reposing the question of discontinuities between spoken and written forms of narrative. Two narrative studies are discussed which were conducted by the author in public schools in California and Chicago. These were concerned with the relation between oral discourse form, as assessed through narrative analysis, and access to literacy activities in typical urban classrooms. The paper draws on this research to suggest further contributions that narrative analysis can make to educational research by clarifying (a) the nature of cultural difference seen as language difference, (b) the role of syntactic, lexical, and intonational options in the structuring of narrative forms, and (c) how cultural (here, narrative) differences do and do not affect aspects of classroom learning.

Cluster Analysis in Educational Research

AbstractThis paper examines the use of cluster analysis in educational research. Its first conclusion is that the important issues are not technical but interpretative. While we must, of course, reject clusters which fail to meet minimum technical requirements, it does not follow that those which are technically admissable are therefore meaningful explanatory constructs. In general, I will argue, they are not.The principal difficulty with cluster analysis is that it introduces a concept of ‘type’ which is unscientific. As a result, we become embroiled with character‐sketches and pen‐pictures more appropriate to literature than to research. This point has been touched on by critics who find cluster analysis to be ‘gross' in certain respects: in its “failure to pinpoint significant teaching variables”, in its tendency to “intensify” what are described as “unproductive polarizations” (Elliott, 1978, p. 77), in its propagation of “emotionally laden catch‐all terms” (Wragg, 1976, p. 285). The second conclusion of the paper is that when the ‘grossness' of cluster analysis is understood and accepted, the technique can nonetheless serve an important heuristic function, provided certain conditions are met. The paper refers to the Lancaster and ORACLE studies to illustrate the problems and possibilities for cluster analysis in educational research. It ends with some recommendations.

APPROXIMATE SAMPLE SIZE AND POWER DETERMINATION FOR SCHEFFÈ CONTRASTS

Journal of Educational MeasurementVolume 18, Issue 1 p. 55-57 APPROXIMATE SAMPLE SIZE AND POWER DETERMINATION FOR SCHEFFÈ CONTRASTS JOEL R. LEVIN, Corresponding Author JOEL R. LEVIN University of Wisconsin LEV1N, JOEL R. Address: Department of Educational Psychology, 1025 W. Johnson St., University of Wisconsin, Madison, Wisconsin 53706. Title: Professor of Educational Psychology. Degrees: B.A. University of California, Berkeley, M.A. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Children's learning, Design and analysis of educational research. SERLIN, RONALD C. Address: Department of Educational Psychology, University of Wisconsin, Madison, Wisconsin 53706. Title: Assistant Professor, Educational Psychology. Degrees: B.S. University of Michigan, M.S. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Nonparametric Statistics, Multivariate analysis.Search for more papers by this authorRONALD C. SERLIN, Corresponding Author RONALD C. SERLIN University of Wisconsin LEV1N, JOEL R. Address: Department of Educational Psychology, 1025 W. Johnson St., University of Wisconsin, Madison, Wisconsin 53706. Title: Professor of Educational Psychology. Degrees: B.A. University of California, Berkeley, M.A. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Children's learning, Design and analysis of educational research. SERLIN, RONALD C. Address: Department of Educational Psychology, University of Wisconsin, Madison, Wisconsin 53706. Title: Assistant Professor, Educational Psychology. Degrees: B.S. University of Michigan, M.S. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Nonparametric Statistics, Multivariate analysis.Search for more papers by this author JOEL R. LEVIN, Corresponding Author JOEL R. LEVIN University of Wisconsin LEV1N, JOEL R. Address: Department of Educational Psychology, 1025 W. Johnson St., University of Wisconsin, Madison, Wisconsin 53706. Title: Professor of Educational Psychology. Degrees: B.A. University of California, Berkeley, M.A. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Children's learning, Design and analysis of educational research. SERLIN, RONALD C. Address: Department of Educational Psychology, University of Wisconsin, Madison, Wisconsin 53706. Title: Assistant Professor, Educational Psychology. Degrees: B.S. University of Michigan, M.S. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Nonparametric Statistics, Multivariate analysis.Search for more papers by this authorRONALD C. SERLIN, Corresponding Author RONALD C. SERLIN University of Wisconsin LEV1N, JOEL R. Address: Department of Educational Psychology, 1025 W. Johnson St., University of Wisconsin, Madison, Wisconsin 53706. Title: Professor of Educational Psychology. Degrees: B.A. University of California, Berkeley, M.A. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Children's learning, Design and analysis of educational research. SERLIN, RONALD C. Address: Department of Educational Psychology, University of Wisconsin, Madison, Wisconsin 53706. Title: Assistant Professor, Educational Psychology. Degrees: B.S. University of Michigan, M.S. University of California, Berkeley, Ph.D. University of California, Berkeley. Specialization: Nonparametric Statistics, Multivariate analysis.Search for more papers by this author First published: March 1981 https://doi.org/10.1111/j.1745-3984.1981.tb00842.xCitations: 3 The authors are grateful to Beth Brown for typing the manuscript. Requests for reprints should be sent to Joel R. Levin, Department of Educational Psychology, 1025 W. Johnson Street, University of Wisconsin, Madison, Wl 53706. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume18, Issue1March 1981Pages 55-57 RelatedInformation

Multivariate Analysis in Educational Research: Applications of the Multivariance Program.

Multivariate Analysis in Educational Research: Applications of the Multivariance Program.

Multivariate Analysis in Educational Research: Applications of the Multivariance Program

Multivariate Analysis in Educational Research: Applications of the Multivariance Program

Book Reviews: Finn, J. D. and Mattsson, I. Multivariate Analysis in Educational Research: Applications of the Multivariance Program

Book Reviews: Finn, J. D. and Mattsson, I. <i>Multivariate Analysis in Educational Research: Applications of the Multivariance Program</i>

Applications of Cluster Analysis in Educational Research: looking for a needle in a haystack[1]

[1] A version of this paper was presented at the Annual Meeting of the American Educational Research Association, New York, April 1977. I wish to thank Leigh Burstein, Robert Linn, Cathy Stasz and Linda Powell for their comments on a draft of this paper. I would also like to thank Carol Moore for providing copies of her Chernoff scaling solutions.

10: Factor Analysis in Educational Research

Most educational researchers will admit to some kind of knowledge of factor analysis. The terminology is widely used, and the technique appears to be almost too easy to apply. It has been said that power corrupts; the technique of factor analysis is very powerful and robust for many sets of data, and, accordingly, the responsibility of the factor analyst to his colleagues is large. There are, however, reasonable guidelines for using factoranalytic techniques which permit the clarification of much data obtained in educational research and which at the same time restrain the practitioner from wasteful exercises at the computer.

Book Review: Design and analysis in educational research

Book Review: Design and analysis in educational research

Multivariate Analysis in Educational Research

Multivariate Analysis in Educational Research

9: Multivariate Analysis in Educational Research

There has been a sharp increase during the past decade in the use of multivariate analysis in research in the behavioral sciences and education. This is a healthy trend because educational research inherently involves many variables, both as predictors and criteria. We seldom seek to predict educational outcomes from a single variable, and we seldom assess them with single criterion variables.

BOOK REVIEWS

Book reviewed in this article:Realism and the Explanation of Behavior. By Merle B. Turner.Homo Psychologicus: Bridging the Arts and Sciences by a Study of Man as Worker, Player and Idler. By John Cohen.Patterning of Time. By Leonard W. Doob.Psycholinguistics: Biological and Social Factors. Edited by John Morton.Language Acquisition: Models and Methods. Edited by Renira Huxley and Elisabeth Ingram.Language and Thinking in Human Development. By D. G. Boyle.The Acquisition and Development of Language. By Paula Menyuk.Picture Language Machines. Edited by S. Kaneff.The Analysis of Intelligence. By J. P. Guilford and Ralph Hoepfner.Cognitive Psychology. Edited by A. Summerfield.The Study of Twins: By Peter Mittler.The Development of Behaviour. By W. Mary Woodward.Recent Developments in Psychogeriatrics. Edited by D. W. K. Kay and Alexander Walk.An Introduction to Abnormal Psychology. By J. E. Orme.Drugs, Brain and Behaviour: Proceedings of Symposium on Psychopharmacology in Durham, 1970. Edited by Daphné Joyce.Psychopathy: Theory and Research. By Robert Hare.Emotional Problems of the Student. Edited by Graham B. Blaine, Jr. and Charles C. McArthur.Psychology of Women: a Study of Bio‐Cultural Conflicts. By Judith M. Bardwick.The Psychology of Fear and Stress. By Jeffrey A. Gray.Measuring the Concepts of Personality. By Donald W. Fiske.The Growth of Sociability. By H. R. Schaffer.Self‐Disclosure: an Experimental Analysis of the Transparent Self. By Sidney M. Jourard.Black Americans: a Psychological Analysis. By E. Earl Baughman.Black and White Identity Formation: Studies in the Psychosocial Development of Lower Socioeconomic Class Adolescent Boys. By Stuart T. Hauser.The Psychological Consequences of Being a Black American: a Sourcebook of Research by Black Psychologists. Edited by Roger Wilcox.Psychological Modelling: Conflicting Theories. Edited by Albert Bandura.Introductory Statistics for the Behavioral Sciences. By Joan Welkowitz, Robert B. Ewen and Jacob Cohen.Practical Statistics for Non‐mathematical People. By Russell Langley.Statistical Methods for Behavioral Science Research. By Leonard A. Marascuilo.Reading and its Difficulties: a Psychological Study. By M. D. Vernon.Design and Analysis in Educational Research. By G. E. R. Burroughs.

Design and Analysis Methodology. An Overview

The intended focus of this chapter is on articles related to the design of experiments and on articles which either present practical data analysis methodology or develop theoretical ideas which have implication for data analysis in educational research. Thus, attention is drawn to a note on curve fitting with correlated observations and to a discursive article on experimental inference as well as to the use of balanced sets of latin squares. In addition, a number of important articles which do not fit nicely into the framework of other chapters in this issue have been included.

Factor Analysis in Educational Research

SHORTER treatments of factor methods and results have appeared passim in previous issues of the REVIEW in the past 12 years. In the cycle dealing with research methods in education, the following articles deserve mention: Vol. XII, December 1942, Chapter VII (Fattu); Vol XV, December 1945, Chapter VII (Lorge); Vol. XVIII, December 1948, Chapter VIII (Fattu). Much greater attention has been paid to factor analysis in the cycle dealing with educational and psychological tests; the following articles may be consulted: Vol. XI, February 1941, Chapters II (Stuit), V (Traxler), and VIII (Flanagan); Vol. XIV, February 1944, Chapters IV (Sells), V (Traxler), and X (Conrad); Vol. XVII, February 1947, Chapters II (Findley, Turnbull, and Conrad), III (Carter), IV (Ellis), and VIII (Travers); and Vol. XX, February 1950, Chapters I (Segel and Gerberich), II (Cornell and Gillette), III (Stuit), IV (Traxler and Jacobs), and IX (Ebel). Since this is the first full-scale treatment of factor analysis which has appeared in the REVIEW since the December 1939 issue it seems wise to consider as a whole the work that has been done since that time, with particular attention to more recent developments. Our purpose is to present a basis for the evaluation of factor analysis as a research technic, with proper concern for results and applications.

Chapter IV: Factor Analysis in Educational Research

Chapter IV: Factor Analysis in Educational Research