Kristin E. Voelkl

Social stigma: The affective consequences of attributional ambiguity

Two experiments investigated the hypothesis that the stigmatized can protect their self-esteem by attributing negative feedback to prejudice. Fifty-nine women participated in the 1st experiment. Women who received negative feedback from a prejudiced evaluator attributed the feedback to his prejudice and reported less depressed affect than women who received negative feedback from a nonprejudiced evaluator. In the 2nd experiment, 38 Black and 45 White students received interpersonal feedback from a White evaluator, who cither could see them or could not. Compared with Whites, Blacks were more likely to attribute negative feedback to prejudice than positive feedback and were more likely to attribute both types of feedback to prejudice when they could be seen by the other student. Being seen by the evaluator buffered the self-esteem of Blacks from negative feedback but hurt the self-esteem of Blacks who received positive feedback.

School Characteristics Related to Student Engagement.

It is well established that American students at risk often exhibit patterns of emotional and behavioral withdrawal or disengagement from school. These withdrawal behaviors may culminate in complete disengagement, that is, leaving school without graduating. This investigation examines aspects of the structural and regulatory environment of schools to identify features that are associated with higher levels of engagement among eighth-grade students at risk.

Measuring Students' Identification with School

This article describes the development of a student rating scale that assesses the degree to which students identify with or disidentify from school and whether there are gender or racial group differences in identification. The Identification with School Questionnaire was tested on a sample of 3,539 eighth-grade pupils. Confirmatory factor analyses of the data provided evidence that a unidimensional scale may better reflect the degree to which students identify with school than do separate measures of belonging and valuing. Analyses of variance of the scale indicated that female students exhibited higher degrees of identification with school than did their male peers, African American students were more identified with school than were White students, and White male youngsters had the lowest levels of identification with school.

Using SPSS for Windows: Data Analysis and Graphics

From the Publisher: This book provides a hands-on; step-by-step guide to data analysis using one of the most popular statistical computing packages, SPSS for Windows 8.0.

The SPSS Guide to the New Statistical Analysis of Data

I: Introduction.- 1 The Nature of SPSS.- 1.1 Getting Started with SPSS for Windows.- 1.2 Managing Data and Files.- 1.3 Transforming Variables and Data Files.- 1.4 Examining and Printing Output.- 1.5 Missing Values.- Exercises.- II: Descriptive Statistics.- 2 Organization of Data.- 2.2 Organization of Categorical Data.- 2.3 Organization of Numerical Data.- Exercises.- Appendix II: SPSS Syntax for Organization of Data.- 3 Measures of Location.- 3.1 The Mode.- 3.2 The Median and Other Percentiles.- 3.3 The Mean.- Exercises.- Appendix III: SPSS Syntax for Measures of Location.- 4 Measures of Variability.- 4.1 Ranges.- 4.2 The Mean Deviation.- 4.3 The Standard Deviation.- 4.5 Some Uses of Location and Dispersion Measures Together.- Exercises.- Appendix IV: SPSS Syntax for Measures of Variability.- 5 Summarizing Multivariate Data: Association Between Numerical Variables.- 5.1 Association of Two Numerical Variables.- 5.2 More than Two Variables.- Exercises.- Appendix V: SPSS Syntax for Summarizing Multivariate Data: Association Between Numerical Variables.- 6 Summarizing Multivariate Data: Association Between Categorical Variables.- 6.1 Two-by-Two Frequency Tables.- 6.2 Larger Two-Way Frequency Tables.- 6.3 Three Categorical Variables.- 6.4 Effects of a Third Variable.- Exercises.- Appendix VI: SPSS Syntax for Summarizing Multivariate Data: Association Between Categorical Variables.- III: Probability.- 7 Basic Ideas of Probability.- 7.3 Probability in Terms of Equally Likely Cases.- 7.8 Random Sampling Random Numbers.- Exercises.- Appendix VII: SPSS Syntax for Basic Ideas of Probability.- 8 Probability Distributions.- 8.5 Family of Standard Normal Distributions.- Exercises.- Appendix VIII: SPSS Syntax for Probability Distributions.- 9 Sampling Distributions.- 9.1 Sampling from a Population.- 9.2 Sampling Distribution of a Sum and of a Mean.- 9.5 The Normal Distribution of Sample Means.- Exercises.- IV: Statistical Inference.- 10 Using a Sample to Estimate Characteristics of One Population.- 10.1 Estimation of a Mean by a Single Number.- 10.2 Estimation of Variance and Standard Deviation.- 10.3 An Interval of Plausible Values for a Mean.- 10.4 Estimation of a Proportion.- 10.5 Estimation of a Median.- 10.6 Paired Measurements.- Exercises.- Appendix X: SPSS Syntax for Using a Sample to Estimate Characteristics of One Population.- 11 Answering Questions About Population Characteristics.- 11.1 Testing a Hypothesis About a Mean.- 11.3 Testing Hypotheses About a Mean when the Standard Deviation is Unknown.- 11.4 P Values: Another Way to Report Tests of Significance.- 11.5 Testing Hypotheses About a Proportion.- 11.6 Testing Hypotheses About a Median: The Sign Test.- 11.7 Paired Measurements.- Exercises.- Appendix XI: SPSS Syntax for Answering Questions About Population Characteristics.- 12 Differences Between Two Populations.- 12.1 Comparison of Two Independent Sample Means when the Population Standard Deviations are Known.- 12.2 Comparison of Two Independent Sample Means when the Population Standard Deviations are Unknown but Treated as Equal.- 12.3 Comparison of Two Independent Sample Means when the Population Standard Deviations are Unknown and not Treated as Equal.- 12.4 Comparison of Two Independent Sample Proportions.- 12.5 The Sign Test for a Difference in Locations.- Exercises.- Appendix XII: SPSS Syntax for Differences Between Two Populations.- 13 Variability in One Population and in Two Populations.- 13.1 Variability in One Population.- 13.2 Variability in Two Populations.- Exercises.- Appendix XIII: SPSS Syntax for Variability in One Population and in Two Populations.- V: Statistical Methods for Other Problems.- 14 Inference on Categorical Data.- 14.1 Tests of Goodness of Fit.- 14.2 Chi-Square Tests of Independence.- 14.3 Measures of Association.- Exercises.- Appendix XIV: SPSS Syntax for Inference on Categorical Data.- 15 Simple Regression Analysis.- 15.1 The Scatter Plot and Correlation Coefficient.- 15.2 SPSS for Simple Regression Analysis.- 15.3 Another Example: Inverse Association of x and y.- Exercises.- Appendix XV: SPSS Syntax for Simple Regression Analysis.- 16 Comparisons of Several Populations.- 16.1 One-Way Analysis of Variance.- 16.2 Which Groups Differ from Which, and by How Much?.- 16.3 Analysis of Variance of Ranks.- Exercises.- Appendix XVI: SPSS Syntax for Comparisons of Several Populations.- Appendix Data Files.

Recall After Relearning by Fast and Slow Learners

Abstract Rates of forgetting among fast and slow learners have been shown to be equivalent if learners are equated for amount of original learning on a memorization task. This finding is quite robust across a variety of learning tasks, materials, and experimental procedures. The authors of the present study replicated these findings with fourth and fifth graders, showing that, once they learned a poem to the same criterion level (75–90% correct), the fast and slow learners recalled approximately the same amount of the poem after 7 days. The experiment was extended by having the students relearn the poem to the same criterion level and testing their retention. After students were re-equated on relearning the poem, however, fast learners recalled significantly more than slow learners at both 14-day and 28-day intervals. Thus, the forgetting curves for fast and slow learners were the same after original learning, but different after relearning. Possible causes of this difference are discussed in terms of the...

Comparisons of Several Populations

Throughout this book we have stressed the basic statistical concept of variability. When some measurement, such as height or aptitude for a particular job, is made on several individuals, the values vary from person to person. The variability of a quantitative scale is measured by its variance. If the set of individuals is stratified into more homogeneous groups, the variance of the measurements within the more homogeneous groups will be less than that of the measurements in the entire group; that is what “more homogeneous” means. For example, the variance of the heights of pupils in an elementary school is usually greater than the variance of heights of pupils in just the first grade, the variance in the second grade, and the variance in each of the other grades. At the same time, the average height of pupils also varies from grade to grade.

Differences Between Two Populations

This chapter focuses on the comparison of two independent populations. SPSS does not have a procedure for testing hypotheses about means when the population standard deviations are known, or any procedure for testing hypotheses about two proportions; these tests must be computed by hand. There are procedures in SPSS for comparing means when the standard deviations are estimated from the sample, however.

The Nature of SPSS

SPSS for Windows is a versatile computer package that will perform a wide variety of statistical procedures. To start SPSS for Windows, begin with the main windows screen and open SPSS using the Start menu. (Alternatively, if you have an SPSS icon on your desktop, you may double click on it to start the program.)