A Conceptual Guide to Statistics Using SPSS
"This book helps students develop a conceptual understanding of a variety of statistical tests by linking the statistics with the computational steps and output from SPSS. Learning how statistical ideas map onto computation in SPSS will help students build a better understanding of both. For example, seeing exactly how the concept of variance is used in SPSS-how it is converted into a number based on real data, which other concepts it is associated with, and where it appears in various statistical tests-will not only help students understand how to use statistical tests in SPSS and how to interpret their output, but will also teach them about the concept of variance itself. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, pointing out wherever possible how the SPSS procedure and output connects back to the conceptual underpinnings of the test. Each of the steps is accompanied by annotated screen shots from SPSS, and relevant components of output are highlighted in both the text and in the figures. Sections explain the conceptual machinery underlying the statistical tests. In contrast to merely presenting the equations for computing the statistic, these sections describe the idea behind each test in plain language and help students make the connection between the ideas and SPSS procedures. These include extensive treatment of custom hypothesis testing in ANOVA, MANOVA, ANCOVA, and regression, and an entire chapter on the advanced matrix algebra functions available only through syntax in SPSS. The book will be appropriate for both advanced undergraduate and graduate level courses in statistics"--
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17App Berkman SPSS46610
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ANOVA table approach motivation assumption average bride’s button calculated cell chi-squared test citations click on Analyze clicking Analyze Closer Look co-workers coefficients compared comparison COMPUTE Conceptual Background covariance custom contrasts data file data set default degrees of freedom dependent measure dependent variable descriptive statistics design matrix display equality of variance equation error variance example F value factor analysis factor levels function graph group means homogeneity impact independent independent-samples t-test interaction intercept Interpreting linear model main effect marginal means matrix algebra mean square Mixed-Model ANOVA Multivariate nonparametric tests normally distributed observations one-sample one-way ANOVA options output paired paired-samples t-test parameters partial correlation predictors PRINT Q-Q plot R-square raters regression relation relationship researcher residuals sample significant slope specify sphericity SPSS standard error statistical tests strategy subjects sum-of-squares syntax univariate vector Wilcoxon Wilcoxon Signed-Rank Test window within-subjects ANOVA within-subjects factor zero