Applied Multivariate Statistics for the Social Sciences: by Keenan A. Pituch, James P. Stevens

By Keenan A. Pituch, James P. Stevens

Now in its 6th version, the authoritative textbook Applied Multivariate facts for the Social Sciences, maintains to supply complex scholars with a pragmatic and conceptual realizing of statistical strategies via examples and data-sets from real learn reviews. With the extra services of co-author Keenan Pituch (University of Texas-Austin), this 6th version keeps many key positive factors of the former variations, together with its breadth and intensity of insurance, a evaluation bankruptcy on matrix algebra, utilized insurance of MANOVA, and emphasis on statistical energy. during this new version, the authors proceed to supply sensible guidance for checking the knowledge, assessing assumptions, reading, and reporting the consequences to assist scholars examine info from their very own study optimistically and professionally.

Features new to this variation include:

    • NEW bankruptcy on Logistic Regression (Ch. eleven) that is helping readers comprehend and use this very versatile and usual process
    • NEW bankruptcy on Multivariate Multilevel Modeling (Ch. 14) that is helping readers comprehend some great benefits of this "newer" strategy and the way it may be utilized in traditional and multilevel settings
    • NEW instance effects part write-ups that illustrate how effects might be awarded in examine papers and magazine articles
    • NEW assurance of lacking info (Ch. 1) to aid scholars comprehend and deal with difficulties linked to incomplete data
    • Completely re-written chapters on Exploratory issue research (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. sixteen) with elevated specialize in realizing types and examining results
    • NEW research summaries, inclusion of extra syntax reasons, and aid within the variety of SPSS/SAS discussion packing containers to lead scholars via info research in a extra streamlined and direct technique
    • Updated syntax to mirror most modern types of IBM SPSS (21) /SAS (9.3)
    • A unfastened on-line assets website at with information units and syntax from the textual content, extra information units, and instructor’s assets (including PowerPoint lecture slides for decide on chapters, a conversion consultant for fifth variation adopters, and solutions to exercises).

Ideal for complicated graduate-level classes in schooling, psychology, and different social sciences during which multivariate facts, complicated statistics, or quantitative suggestions classes are taught, this publication additionally appeals to training researchers as a worthwhile reference. Pre-requisites comprise a direction on factorial ANOVA and covariance; notwithstanding, a operating wisdom of matrix algebra isn't really assumed.

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Extra resources for Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS, Sixth Edition

Sample text

25. So, if seven significant results are found, the Bonferroni procedure suggests that most (or all) of the results could be spurious. 05, and this conveys a very different impression. Now, the conclusion is that perhaps a few of the significant results are spurious. 4 Statistical Significance Versus Practical Importance You have probably been exposed to the statistical significance versus practical importance issue in a previous course in statistics, but it is sufficiently important to have us review it here.

Notice from the table that as the α level decreases, power also decreases (given that effect and sample size are held constant). 1 should help to make clear why this happens. The power of a statistical test is dependent on three factors: The α level set by the experimenter Sample size Effect size—How much of a difference the treatments make, or the extent to which the groups differ in the population on the dependent variable(s). 1 has already demonstrated that power is directly dependent on the α level.

With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally.

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