Introduction 1. Woman's Place in Man's Life Cycle 2. Images of Relationship 3. Concepts of Self and Morality 4. Crisis and Transition 5. Women's Rights and Women's Judgment 6. Visions of Maturity References Index of Study Participants General Index

In a Different Voice. Psychological Theory and Women’s Development. Cambridge, MA (Harvard University Press) 1982.

Ulric Neisser (Chair) Gwyneth Boodoo Thomas J. Bouchard, Jr. A. Wade Boykin Nathan Brody Stephen J. Ceci Diane E Halpern John C. Loehlin Robert Perloff Robert J. Sternberg Susana Urbina Emory University Educational Testing Service, Princeton, New Jersey University of Minnesota, Minneapolis Howard University Wesleyan University Cornell University California State University, San Bernardino University of Texas, Austin University of Pittsburgh Yale University University of North Florida

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Intelligence: Knowns and unknowns.

Factor analysis is a multivariate statistical approach commonly used in psychology, education, and more recently in the health-related professions. This paper will attempt to provide novice researchers with a simplified approach to undertaking exploratory factor analysis (EFA). As the paramedic body of knowledge continues to grow, indeed into scale and instrument psychometrics, it is timely that an uncomplicated article such as this be offered to the paramedic readership both nationally and internationally. Factor analysis is an important tool that can be used in the development, refinement, and evaluation of tests, scales, and measures that can be used in education and clinical contexts by paramedics. The objective of the paper is to provide an exploratory factor analysis protocol, offering potential researchers with an empirically-supported systematic approach that simplifies the many guidelines and options associated with completing EFA.

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Exploratory factor analysis: A five-step guide for novices

The Bell Curve: Intelligence and Class Structure in American Life.

One of the most important steps in the qualitative research process is analysis of data. The purpose of this article is to provide elements for understanding multiple types of qualitative data analysis techniques available and the importance of utilizing more than one type of analysis, thus utilizing data analysis triangulation, in order to understand phenomenon more fully for school psychology research and beyond. The authors describe seven qualitative analysis tools: methods of constant comparison, keywords-in-context, word count, classical content analysis, domain analysis, taxonomic analysis, and componential analysis. Then, the authors outline when to use each type of analysis. In so doing, the authors use real qualitative data to help distinguish the various types of analyses. Furthermore, flowcharts and tables are provided to help delineate when to choose each type of analysis. Finally, the role of computer-assisted software in the qualitative data-analytic process is discussed. As such, use of the analyses outlined in this article should help to promote rigor in qualitative research.

An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation.

The relationship of test performance on the 11 WAIS-R subtests to five background variables was examined by MANOVAs and univariate ANOVAs on standardization data. For all four broad age groups (16-19, 20-34, 35-54, 55-74), significant differences were found for sex, race, and education main effects. Geographic region produced significant main effects for three of the age groups, but residence yielded significant results only for the oldest group. The pattern of the relationships of subtest scores to each background variable was analyzed, interpreted, and integrated with previous research.

Sex, race, residence, region, and education differences on the 11 WAIS-R subtests.

Compared the reliability m d validity of three Wechsler Intelligence Scale for Children-Third Edition (WZSC-ZIZ; Wechsler, 1991) short f o m , each a tetrad composed of two Verbal and two Perj4ormance subtests. The first tetrad was selected based on psychometric and clinical considerations, and the second was based primarily on practical considerations, namely its brevity in administration and scoring. The third reflects a blend of psychometric, clinical, and practical factors. WISC-III standardization data were used to select the short forms, to determine their psychometric properties, and to develop formulas for computing a child's estimated Full Scale Intelligence Quotient. All three short forms had acceptable average reliability coefficients above .90, but the validity of the brief practical short form was too low to represent a good alternative to either of the other two tetrads. Overall, the short form that represented a compromise between psychometric-clinical and practical variables (Similarities-...

Comparison of three WISC-III short forms: Weighing psychometric, clinical, and practical factors.

Demographic characteristics and IQ among adults: Analysis of the WAIS—R standardization sample as a function of the stratification variables.

The research methodology literature in recent years has included a full frontal assault on statistical significance testing.The purpose of this paper is to promote the position that, while significance testing as the sole basis for resultinterpretation is a fundamentally flawed practice, significance tests can be useful as one of several elements in acomprehensive interpretation of data. Specifically, statistical significance is but one of three criteria that must bedemonstrated to establish a position empirically. Statistical significance merely provides evidence that an event did nothappen by chance. However, it provides no information about the meaningfulness (practical significance) of an eventor if the result is replicable. Thus, we support other researchers who recommend that statistical sign ificance testing mustbe accompanied by judgments of the event’s practical significance and replicability.

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The Role of Statistical Significance Testing In Educational Research

Age and WAIS-R intelligence in a national sample of adults in the 20- to 74-year age range: A cross-sectional analysis with educational level controlled

James E. McLean

Papers

Sex, race, residence, region, and education differences on the 11 WAIS-R subtests.

Comparison of three WISC-III short forms: Weighing psychometric, clinical, and practical factors.

Demographic characteristics and IQ among adults: Analysis of the WAIS—R standardization sample as a function of the stratification variables.

The Role of Statistical Significance Testing In Educational Research

Age and WAIS-R intelligence in a national sample of adults in the 20- to 74-year age range: A cross-sectional analysis with educational level controlled