An Introduction to Statistical Concepts, Second Edition

by Lomax; Richard G.

Edition:

2nd

ISBN13:

9780805857399

ISBN10:

0805857397

Format:

Hardcover

Pub. Date:

4/12/2007

Publisher(s):

Lawrence Erlbau

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Summary

Unlike many other statistics texts, this one is comprehensive and flexible enough for either a single or a two-semester course. Instructors can select only the topics that are most appropriate for their course.Its intuitive approach helps students more easily understand the concepts and interpret software results. Throughout the text, the author demonstrates how many statistical concepts relate to one another. Only the most crucial equations are included.The new edition features: SPSS sections throughout with input, output, and APA style write-ups using the book's dataset A CD with every example and problem dataset used in the text in SPSS format; More information on confidence intervals, effect size measures, power, and regression models A revised sequence of the regression and ANOVA chapters for enhanced conceptual flow De-emphasized computations to provide more discussion of concepts and software More end of chapter problems with more realistic data and a greater emphasis on interpretation Many more references An Instructor's Resource CD with all of the solutions to the problems and other teaching aids.The first five chapters cover basic descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution and other standard scores, and probability and sampling.The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance (ANOVA) and regression models. It contains a number of topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple regression models.Realistic examples from education and the behavioral sciences illustrate the concepts. Each example includes an examination of the various procedures and necessary assumptions, tips on developing an APA style write-up, and sample SPSS output. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter concludes with conceptual and computational problems, about a third of which are new to this edition. Answers to the odd-numbered problems are provided.Intended for a one- or a two-semester course in introductory statistics taught in education and/or behavioral science departments. Although used predominantly at the master's or doctoral level, the book is also used at the undergraduate level. Only a rudimentary knowledge of algebra is required.

Author Biography

Richard Lomax, Ph.D., is a Professor in the College of Education at The Ohio State University. He received his Ph.D. in Educational Research Methodology from the University of Pittsburgh.

Preface	p. ix
Introduction	p. 1
What is the value of statistics?	p. 3
Brief introduction to the history of statistics	p. 5
General statistical definitions	p. 5
Types of variables	p. 7
Scales of measurement	p. 8
Summary	p. 12
Data Representation	p. 16
Tabular display of distributions	p. 18
Graphical display of distributions	p. 23
Percentiles	p. 29
SPSS	p. 33
Summary	p. 34
Univariate Population Parameters and Sample Statistics	p. 39
Summation notation	p. 40
Measures of central tendency	p. 41
Measures of dispersion	p. 45
SPSS	p. 53
Summary	p. 55
The Normal Distribution and Standard Scores	p. 59
The normal distribution	p. 60
Standard scores	p. 65
Skewness and kurtosis statistics	p. 68
SPSS	p. 72
Summary	p. 73
Introduction to Probability and Sample Statistics	p. 77
Brief introduction to probability	p. 78
Sampling and estimation	p. 81
Summary	p. 87
Introduction to Hypothesis Testing: Inferences About a Single Mean	p. 92
Types of hypotheses	p. 93
Types of decision errors	p. 95
Level of significance [Alpha]	p. 98
Overview of steps in the decision-making process	p. 100
Inferences about [Mu] when [sigma] is known	p. 101
Type II error [Beta] and power [1 - Beta]	p. 105
Statistical versus practical significance	p. 108
Inferences about [Mu] when [sigma] is unknown	p. 109
SPSS	p. 113
Summary	p. 114
Inferences About the Difference Between Two Means	p. 119
New concepts	p. 120
Inferences about two independent means	p. 122
Inferences about two dependent means	p. 129
SPSS	p. 133
Summary	p. 134
Inferences About Proportions	p. 140
Inferences about proportions involving the normal distribution	p. 141
Inferences about proportions involving the chi-square distribution	p. 151
SPSS	p. 156
Summary	p. 158
Inferences About Variances	p. 162
New concepts	p. 163
Inferences about a single variance	p. 164
Inferences about two dependent variances	p. 166
Inferences about two or more independent variances (homogeneity of variance tests)	p. 168
SPSS	p. 172
Summary	p. 173
Bivariate Measures of Association	p. 176
Scatterplot	p. 177
Covariance	p. 179
Pearson product-moment correlation coefficient	p. 182
Inferences about the Pearson product-moment correlation coefficient	p. 183
Some issues regarding correlations	p. 186
Other measures of association	p. 188
SPSS	p. 191
Summary	p. 192
One-Factor Analysis of Variance-Fixed-Effects Model	p. 196
Characteristics of the one-factor ANOVA model	p. 198
The layout of the data	p. 200
ANOVA theory	p. 201
The ANOVA model	p. 206
Assumptions and violation of assumptions	p. 210
The unequal n's or unbalanced design	p. 213
Alternative ANOVA procedures	p. 213
SPSS	p. 215
Summary	p. 217
Multiple Comparison Procedures	p. 222
Concepts of multiple comparison procedures	p. 224
Selected multiple comparison procedures	p. 228
SPSS	p. 241
Summary	p. 242
Factorial Analysis of Variance-Fixed-Effects Model	p. 247
The two-factor ANOVA model	p. 249
Three-factor and higher-order ANOVA	p. 265
Factorial ANOVA with unequal n's	p. 267
SPSS	p. 268
Summary	p. 269
Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model With a Single Covariate	p. 277
Characteristics of the model	p. 278
The layout of the data	p. 281
The ANCOVA model	p. 281
The ANCOVA summary table	p. 282
Partitioning the sums of squares	p. 283
Adjusted means and related procedures	p. 283
Assumptions and violation of assumptions	p. 286
An example	p. 289
ANCOVA without randomization	p. 292
More complex ANCOVA models	p. 293
Nonparametric ANCOVA procedures	p. 293
SPSS	p. 293
Summary	p. 294
Random-and Mixed-Effects Analysis of Variance Models	p. 301
The one-factor random-effects model	p. 303
The two-factor random-effects model	p. 306
The two-factor mixed-effects model	p. 309
The one-factor repeated measures design	p. 313
The two-factor split-plot or mixed design	p. 319
SPSS	p. 325
Summary	p. 331
Hierarchical and Randomized Block Analysis of Variance Models	p. 335
The two-factor hierarchical model	p. 336
The two-factor randomized block design for n = 1	p. 343
The two-factor randomized block design for n > 1	p. 350
The Friedman test	p. 350
Comparison of various ANOVA models	p. 351
SPSS	p. 353
Summary	p. 357
Simple Linear Regression	p. 361
The concepts of simple linear regression	p. 362
The population simple linear regression model	p. 364
The sample simple linear regression model	p. 365
SPSS	p. 381
Summary	p. 383
Multiple Regression	p. 387
Partial and semipartial correlations	p. 388
Multiple linear regression	p. 390
Other regression models	p. 403
SPSS	p. 408
What's next?	p. 408
Summary	p. 410
References	p. 415
Appendix Tables	p. 425
Answers	p. 449
Index	p. 463
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