Basic statistics for psychologists
著者
書誌事項
Basic statistics for psychologists
Palgrave Macmillan, 2011
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注記
Includes index
内容説明・目次
内容説明
The emphasis upon methods of statistical research in psychology is often overlooked by beginning undergraduate students and subsequently, many find difficulty in approaching this unexpected yet so often integral topic of any given psychology degree. Subsequently this clearly written and comprehensive textbook offers itself as a guide to those students looking for a clear introduction on how to use statistics in psychological research. Crucially, students will be equipped with the key methods of statistical inference and learn how to interpret the results of various statistical tests. Expect to learn how to summarise data using the frequency distribution, measures of central tendency, variability as well how to employ the t-test and non-parametric tests for various types of groups and samples.
This core adoptable textbook covers all areas of undergraduate statistics, with good formulas and explanations for calculations and will aid students with the knowledge and tools necessary to developing their ability to conduct reliable and methodical research using statistics. This is an incredibly helpful and informative read for undergraduate students taking research methods and statistics courses in psychology.
目次
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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