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Basic statistics for psychologists Marc Brysbaert

By: Material type: TextTextPublication details: Basingstoke Palgrave Macmillan 2011Description: xiv, 413 pISBN:
  • 9780230275423
Subject(s): DDC classification:
  • 519.502415
Contents:
Preface -- Using statistics in psychology research -- Summarising data using the frequency distribution -- Summarising data using measures of central tendency -- Summarising data using measures of variability -- Standardised scores, normal distribution and probability -- Using the t-test to measure the difference between independent groups -- Interpreting the results of a statistical test -- Using non-parametric tests to measure the difference between independent groups -- Using the t-test to measure change in related samples -- Using non-parametric tests to measure change in related samples -- Improving predictions through the Pearson correlation coefficient -- Improving predictions through non-parametric tests: The Spearman rank correlation and the chi-square test for independence -- Using analysis of variance (ANOVA) to compare more than two conditions -- Post-hoc tests in ANOVA and multiple regression analysis.
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Holdings
Item type Current library Home library Shelving location Call number Copy number Status Date due Barcode Item holds
4 Week Loan St. Luke's General Hospital Kilkenny St. Luke's General Hospital Kilkenny Open Shelves 519.502415 (Browse shelf(Opens below)) 1 Available 032251
Total holds: 0

Includes bibliographical references and index

Preface -- Using statistics in psychology research -- Summarising data using the frequency distribution -- Summarising data using measures of central tendency -- Summarising data using measures of variability -- Standardised scores, normal distribution and probability -- Using the t-test to measure the difference between independent groups -- Interpreting the results of a statistical test -- Using non-parametric tests to measure the difference between independent groups -- Using the t-test to measure change in related samples -- Using non-parametric tests to measure change in related samples -- Improving predictions through the Pearson correlation coefficient -- Improving predictions through non-parametric tests: The Spearman rank correlation and the chi-square test for independence -- Using analysis of variance (ANOVA) to compare more than two conditions -- Post-hoc tests in ANOVA and multiple regression analysis.

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