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Journal ArticleDOI

Effects of Classroom Social Climate on Individual Learning

01 Mar 1970-American Educational Research Journal (SAGE Publications)-Vol. 7, Iss: 2, pp 135-152
TL;DR: In this article, Anderson et al. investigated the relationship between individual pupil perceptions of their class and their individual learning in terms of interpersonal relationships among pupils, relationship between pupils and their teacher, relationships between pupils with both the subject studied and the method of learning and pupils' perceptions of the structural characteristics of the class.
Abstract: Teachers often suggest that classes have a distinctive personality or "climate" which influences the learning efficiency of their members. In some classes, the difficulties of one pupil become the concern of all. In other groups, each child works for personal rewards and the presence of others does little to aid or frustrate his individual learning. The properties of school classes that account for some of these differences have been termed the classroom social climate (Anderson, 1968). Derived from prior group research and from an intuitive analysis of the types of interactions that are present in typical school classes, these climate properties include interpersonal relationships among pupils, relationships between pupils and their teacher, relationships between pupils and both the subject studied and the method of learning, and finally, pupils' perceptions of the structural characteristics of the class. Previous research on classroom social climate has provided some insights into two aspects of the social psychology of the school class group. One study (Walberg and Anderson, 1968) considered the relationships between individual pupil perceptions of their class and their individual learning; a subsequent study (Anderson & Walberg, 1968) attempted to account for differential class performance in terms of
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Journal ArticleDOI
TL;DR: This article investigated the relationship among a variety of school-level climate variables and mean school achievement in a random, sample of Michigan elementary schools and found that SES, racial composition and climate were each highly related to mean school performance.
Abstract: The present study investigates the relationships among a variety of school-level climate variables and mean school achievement in a random, sample of Michigan elementary schools. School-level SES, racial composition and climate were each highly related to mean school achievement; only a small proportion of the between-school variance in achievement is explained by SES and racial composition after the effect of school climate is removed. The climate variable we have called Student Sense of Academic Futility had the largest correlation with achievement. An observational study of four schools with similar SES and racial composition but different achievement tended to support the more analytical findings and suggest the processes by which climate affects achievement.

556 citations


Cites methods from "Effects of Classroom Social Climate..."

  • ...Others have used measures of student personality or characteristics of school organization as proxies for school climate (Anderson, 1970; O'Reilly, 1975)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors present a review of attitudes to science in science education, focusing on the role of science education in the acceptance of science in the curriculum of public education.
Abstract: (1975). Attitudes to Science : A Review. Studies in Science Education: Vol. 2, No. 1, pp. 1-41.

468 citations

Journal ArticleDOI
TL;DR: In this article, the influence of students' background and perceptions on science attitude and achievement was investigated using the LISREL IV computer program, and two different models were tested: a model in which attitudes influence achievement and its converse (achievement influences attitudes).
Abstract: The purpose of the study was to investigate the influence of students' background and perceptions on science attitude and achievement. The data analysed came from Booklet 4 given to 17-year-olds during the 1976–1977 National Assessment of Educational Progress (NAEP) survey. Causal modeling procedures were used to analyze the data. In particular, the LISREL method which underlies the LISREL IV computer program, (Joreskog and Sorbom, 1978) was employed. The influence of five background variables (sex, race, home environment, amount of homework, and parents' education) on three dependent variables (student perception of science instruction, student attitudes, and student achievement) was examined. Sex, race, and the home environment were shown to have substantial influence on student achievement in science. Further, two different models were tested: a model in which attitudes influence achievement and its converse (achievement influences attitudes). The data supported the first model, that is, attitudes influence achievement.

259 citations

References
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Book
01 Jan 1966
TL;DR: In this paper, the main text for an upper-level undergraduate or graduate-level introductory statistics course in departments of psychology, educational psychology, education and related areas is presented. But the course is not designed for the general public.
Abstract: This is a main text for an upper-level undergraduate or graduate-level introductory statistics course in departments of psychology, educational psychology, education and related areas.

2,314 citations

BookDOI
TL;DR: The Meaning and Strategic Use of Factor Analysis and its Role and Relationships among Statistical Methods are explained.
Abstract: I Multivariate Method and Theory Construction.- 1 Psychological Theory and Scientific Method.- 1. The Role of Methodology in Science.- 2. Design of This Book.- 3. Some Major Historical Springs of Methodological Tradition.- 4. What Is and What Might Be in Present-Day Research Method Concepts.- 5. The Nature of the Inductive-Hypothetico-Deductive (IHD) Method in Science.- 6. Summary.- References.- 2 The Principles of Experimental Design and Analysis in Relation to Theory Building.- 1. The Six Basic Parameters of Experimental Design.- 2. The Logically Possible and Practically Viable Types of Experimental Design.- 3. The Main Methods of Mathematico-Statistical Treatment.- 4. Definition of Theory, Law, Postulate, Hypothesis, and Reversibility-Irreversibility.- 5. Social and Psychological Influences in the Natural History of Scientific Theory.- 6. The Total Plan: Advantages and Disadvantages Guiding the Choice among Various Research Procedures.- 7. Creative Scientific Thinking in Relation to Multivariate and Bivariate Procedures.- 8. Summary.- References.- 3 The Data Box: Its Ordering of Total Resources in Terms of Possible Relational Systems.- 1. Relational System, Hypothesis, Design, and Method as the Four Panels of the Investigatory Plan.- 2. The Purpose of Developing the Covariation Chart into the BDRM or Data Box.- 3. Two Protoconstructs: Pattern Entity (Vector) and Attribute Scale (Scalar).- 4. The Ten Coordinates of the Hyperspace BDRM.- 5. The Nature and Definition of a BDRM Facet.- 6. Principles Governing "Entries": Aspects and Shifts.- 7. The Numbers and Varieties of Facets, and Associated Techniques.- 8. The Numbers and Varieties of Faces, Frames, and Grids.- 9. The Totality of Possible Direct and Derived Relational Analyses and Techniques.- 10. Sources of Variance and Covariance in the Data Box: Observable and Inherent (Ideal, Conceptual) Sources Contrasted.- 11. Scales and Standardizations: Normative, Ipsative, Abative.- 12. Superordinate Relational and Interactional Analysis Techniques: Including Superset and Interset Factor Analysis.- 13. Summary, Glossary, and Notation.- References.- 4 The Meaning and Strategic Use of Factor Analysis.- 1. Its Role and Relationships among Statistical Methods.- 2. The Basic Mathematical Propositions and Formulations.- 3. Alternative Models: Components and Factors.- 4. Properties and Formulas for the Full Factor Model.- 5. Unique Resolution and the Tests of Its Attainment.- 6. Factor Invariance, Identification, and Interpretation.- 7. Deciding the Number of Factors.- 8. The Reticular and Strata Models for Higher-Order Factors.- 9. Some Modifications, Developments, and Conditions of the Main Factor Model.- 10. Strategies in the Practical Use of Factor Analysis.- 11. Questions of Statistical Significance and Use of Computer Procedures.- 12. Summary (and Rationale of Notation).- References.- II Multivariate Modeling and Data Analysis.- 5 Analysis of Covariance Structures.- 1. Introduction.- 2. Some Types of Covariance Structures.- 3. General Approaches to Analysis of Covariance Structures.- 4. Analysis of the Examples.- 5. Generalizations.- References.- 6 Exploratory Factor Analysis.- 1. Introduction.- 2. Decision Points in Factoring.- 3. Implications: Some Designs for Exploratory Factor Analysis.- References.- 7 Confirmatory Factor Analysis.- 1. Philosophical Contrasts between Exploratory and Confirmatory Factor Analysis.- 2. The Fundamentals of Confirmatory Factor Analysis.- 3. Applications for Confirmatory Factor Analysis.- 4. Conclusion.- References.- 8 Multimode Factor Analysis.- 1. Multimode Experimental Design.- 2. Factor-Analytic Developments.- 3. Application: Spectrum of Affect.- 4. Comparisons and Contemplations.- References.- 9 Causal Modeling via Structural Equation Systems.- 1. Introduction.- 2. Structural Equations.- 3. Path Diagrams.- 4. Representation Systems.- 5. Estimation Systems.- 6. Examples.- 7. Future Directions.- References.- 10 Multivariate Analysis of Discrete Data.- 1. Introduction.- 2. One-Way Tables.- 3. Bivariate Data: Two-Way Tables.- 4. Multiway Tables.- 5. Latent Structure Models.- 6. Conclusion.- References.- 11 Some Multivariate Developments in Nonparametric Statistics.- 1. A Characterization of Nonparametric Statistics.- 2. Multivariate Perspective.- 3. Exploratory Nonparametric Analysis of All Analytical Units.- 4. Exploratory Nonparametric Analysis of Subsets of Analytical Units.- 5. Confirmatory Nonparametric Analysis.- 6. Discussion and Summary.- References.- 12 Multivariate Analysis of Variance.- 1. Classical Approach.- 2. General Linear Model Approach.- 3. Significance Tests.- 4. Discriminant Analysis.- References.- 13 Multidimensional Scaling.- 1. Introduction.- 2. Models and Methods.- 3. Important Findings.- 4. Classic Problems in MDS.- 5. Current Issues and Future Directions.- References.- 14 The Methods and Problems of Cluster Analysis.- 1. Introduction to Cluster Analysis.- 2. Cluster Analysis Methods.- 3. Similarity.- 4. Unresolved Problems of Cluster Analysis.- 5. Final Remarks.- References.- 15 Human Behavior Genetics.- 1. Introduction.- 2. The Development of Multivariate Human Behavior Genetic Analysis.- 3. Multivariate Generalization of Path Analysis.- 4. Application of Multivariate Path Analysis: Nuclear Family and Twin Design.- 5. Application of Multivariate Path Analysis: Full Adoption Design.- 6. Current Status of Multivariate Human Behavior Genetics.- 7. Multivariate Behavior Genetic Models of Development.- 8. Future Directions: Intergenerational Equilibrium?.- 9. Summary.- References.- 16 Multivariate Reliability Theory: Principles of Symmetry and Successful Validation Strategies.- 1. Introduction.- 2. Basic Concepts of Reliability Theory.- 3. Multivariate Extensions of Reliability Concepts.- 4. Foundations of a General Measurement and Research Strategy Synthesizing the Experimental and the Psychometric Traditions in Psychology.- 5. Paradoxes Revisited.- 6. Relationships to Other Approaches, Implications, and Conclusions.- References.- 17 Dynamic but Structural Equation Modeling of Repeated Measures Data.- 1. Introduction.- 2. Basic Features of a Latent Growth Curve Model.- 3. Dynamic Modeling with Latent Growth Curves.- 4. The Curve-of-Factors Model of Multivariate Growth.- 5. The Factor-of-Curves Model as a Multivariate Alternative.- 6. Discussion of Further Issues.- 7. Appendix: Assorted Technical Issues for LGM Programming.- References.- 18 N-Way Factor Analysis for Obtaining Personality, Situation, and Test Form Contributions to a Psychological Response: Illustrated by a Three-Way Plasmode.- 1. Three Existing Approaches and Two Possible Models for Representing Environment in the Behavioral Equation.- 2. The Utility of Alternative Breakdowns into Contributing Factor Systems.- 3. The Problem of "Side Effects" in Analysis by Faces.- 4. Numerical, Plasmode Illustration.- 5. Summary.- 6. Appendix: Boundary Values.- References.- III Multivariate Research and Theory.- 19 Thinking about Human Abilities.- 1. The Many and Few of Human Abilities: Common and Specific.- 2. A Hierarchy of Human Abilities.- 3. Developmental Evidence.- 4. Genetic Evidence and Early Development.- 5. To Come to a Close.- References.- 20 Personality: Multivariate Systems Theory and Research.- 1. Introduction.- 2. Traits, States, and Situations: An Overview.- 3. Multivariate Personality Research: Some Basic Issues.- 4. Classification of Traits.- 5. Primary Source Traits in L- and Q-Data.- 6. Higher-Order Factors.- 7. Objective Test Data.- 8. The Universality of Source Traits.- 9. The Heritability of Personality.- 10. States and Processes.- 11. The Full Specification Equation.- 12. Conclusion.- 13. Summary.- References.- 21 Elucidation of Motivation Structure by Dynamic Calculus.- 1. Introduction.- 2. Criticisms of Cattell's Motivation Research.- 3. Exploratory Factor-Analytic Principles in Motivation Research.- 4. Objective Devices and the Measurement of Motivation Strength Components.- 5. Dynamic Structure of Ergs and Sentiments.- 6. Computation of Ergic Tension Arousal and Sentiment Activation.- 7. Measurement of Dynamic Motivation Structure.- 8. Dynamic Calculus of Conflict.- 9. Structured Learning Theory of Motivation...- 10. Systems Theory Approach to Motivation.- 11. Summary and Conclusions.- References.- 22 Multivariate Approaches to Human Learning.- 1. Introduction.- 2. Factor Analysis and Learning.- 3. Structured Learning Theory.- 4. Summary.- References.- 23 Clinical Psychology: A Multivariate Appraisal.- 1. Introduction.- 2. Some General Considerations: Developments and Shortcomings of Research in Clinical Psychology.- 3. Selected Topics.- 4. Future Directions.- References.- 24 Psychophysiological Processes.- 1. Introduction.- 2. Patterns of Activation.- 3. Personality Traits.- 4. Psychophysiological Research and Applied Areas.- 5. Essentials of Psychophysiological Assessment.- References.- 25 Organizational Climate.- 1. Introduction.- 2. Toward a Theory of Organizational Climate.- 3. Measurement Problems and Strategies.- 4. Dimensions of Organizational Climate.- 5. Homogeneity of Climate in Complex Organizations.- 6. Types of Organizational Climate.- 7. Prediction of Organizational Performance Criteria.- References.- 26 Multivariate Analyses of the Sociology of Intelligence.- 1. J. B. Mailer: The Sociology of Intelligence in New York, 1930.- 2. C. Burt: Educational Backwardness in London.- 3. E. L. Thorndike: Your City.- 4. K. S. Davenport and H. H. Remmers: Intelligence Differences between the American States.- 5. R. L. Thorndike: More American Cities.- 6. S. Wiseman: The Manchester Studies.- 7. O. D. Duncan: Path Models in Sociology.- 8. R. Lynn: A Path Model of the Sociology of Intelligence in the British Isles.- 9. R. Lynn: The Sociology of Intelligence in France.- 10. Conclusion.- References.

1,340 citations

Journal ArticleDOI
TL;DR: In this article, the authors show that the prediction of freshman engineering grades from appropriate scales of the Strong Interest Test is lower for groups thought to be compulsive than for groups considered to be non-compulsive, and that the degree or mode of predictability is thought to vary as a function of membership in one or another of designated groups, which are presumed to be distinct and homogeneous.
Abstract: different from that for non-veterans (3). The prediction of freshman engineering grades from appropriate scales of the Strong Interest Test is lower for groups thought to be compulsive than for groups thought to be non-compulsive (4). Analysis of covariance provides a statistical method for studying situations in which the degree or mode of predictability is thought to vary as a function of membership in one or another of designated groups, which are presumed to be distinct and homogeneous. On the other hand, the &dquo;moderated multiple regression&dquo; provides a simple generalization to the case in which the basic parameter is not membership in some group, but score on some continuous variable. The &dquo;compulsiveness&dquo; of the third example cited above ought to be one illustration of such a continuous variable. The amount of prediction obtainable from the Strong Engineer Scale Score should vary continuously with the score on compulsiveness, and should

475 citations