Susan B. Gerber

The Enduring Effects of Small Classes.

The purpose of this investigation was to extend our knowledge of the effects of small classes in the primary grades on pupils’ academic achievement. Three questions were addressed that have not been answered in previous research: (1) How large are the effects of small classes relative to the number of years students participate in those classes? (2) How much does any participation in small classes in K–3 affect performance in later grades when all classes are full-size? (3) How much does the duration of participation in small classes in K–3 affect the magnitude of the benefits in later grades (4, 6, and 8)? Rationales for expecting the continuing impacts of small classes were derived in the context of other educational interventions (for example, Head Start, Perry Preschool Project). The questions were answered using data from Tennessee’s Project STAR, a statewide controlled experiment in which pupils were assigned at random to small classes, full-size classes, or classes with a full-time teaching assistant. Hierarchical linear models (HLMs) were employed because of the multilevel nature of the data; the magnitude of the small-class effect was expressed on several scales including “months of schooling.” The results for question (1) indicate that both the year in which a student first enters a small class and the number of years (s)he participates in a small class are important mediators of the benefits gained. The results for questions (2) and (3) indicate that starting early and continuing in small classes for at least three years are necessary to assure long-term carryover effects. Few immediate effects of participation in a class with a full-time teacher aide, and no long-term benefits, were found. The results are discussed in terms of implications for class-size reduction initiatives and further research questions.

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.

Teacher Aides and Students’ Academic Achievement

Despite more than 600,000 teacher aides in American schools today, research provides little information about their classroom activities, their qualifications for carrying out their duties, or their impact on student achievement and behavior. This investigation asked whether the presence of a teacher aide in the classroom has any noticeable impact on pupils’ learning. Three primary questions were addressed: (1) In Grades K through 3, does the presence of a full-time teacher aide in the classroom affect students’ academic achievement? (2) If teacher aides have a positive effect on students’ performance, does the effect depend on the number of years the student attends classes with a teacher aide? (3) Do some functions of aides (i.e., clerical tasks, instructional tasks, noninstructional tasks) have a greater impact on student achievement than others? This investigation showed that the teacher aide movement in the United States has created a state of affairs that requires many aides to perform tasks for which they are ill-prepared. In addition, teacher aide data were analyzed from Tennessee’s Project STAR, a longitudinal experiment in which students were assigned at random to small classes, regular-size classes without an aide, or regular-size classes with a full-time teacher aide. The analyses reported here extend previous investigations, examining the functions and effects of teacher aides in depth. The results showed that teacher aides have little, if any, positive effect on students’ academic achievement. The only positive effect was an improvement in reading scores for students who attended a class with a teacher aide for 2 or 3 years. These results were the only exceptions to a plethora of negative findings. The study also showed that the types of duties aides performed had no bearing on student achievement. Because teacher aides are called upon increasingly to provide instruction to pupils, policies and research must help us select and prepare aides to perform effectively.

Instructor influence on reasoned argument in discussion boards

In this study, we explore the extent to which two instructional techniques promote critical discourse in an online class on educational standards and curriculum: instructor stance (challenging/nonchallenging) and topic level (higher order/lower order). Posts from 25 students, across four modules, were analyzed. These four modules constituted approximately one third of the course, and were selected because the professor was the sole facilitator for them. Results indicate that, regardless of topic level, a challenging stance by the professor had a positive effect on the percentage of student posts that referenced readings and theory. There was an interaction between level and stance on student use of reasoned argument. Lower order challenging forums were associated with a greater percentage of reasoned posts. This may be due to the abstractness of the professors probes in higher order forums. Implications for future research include empirical investigations incorporating contextual variables and qualitative studies to ascertain how students engage with bulletin boards.

Gamers and gaming context: Relationships to critical thinking

Gaming is purported to hold promise for education, in part, because it is thought to develop 21st century skills such as critical thinking. To date, there has been a dearth of generalisable research investigating the relationship between gaming and critical thinking. Results of a survey of 121 adults found that gamers and non-gamers do not differ significantly on critical thinking dispositions. However, gamers who play strategy games scored higher on actively open-minded thinking than did other types of gamers. In addition, low compared with high involvement in the gaming community was associated with higher open-minded thinking. Implications for educators and for further research are discussed. [ABSTRACT FROM AUTHOR]

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.

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.)