Understanding statistics in psychology /
Howitt, Dennis,
Understanding statistics in psychology / Dennis Howitt and Duncan Cramer. - Ninth edition. - xxvi, 653 pages ; 27 cm.
"First published 1997"--Title page verso.
Includes bibliographical references and index.
Why statistics? -- Part 1. Descriptive statistics -- Some basics: variability and measurement -- Describing variables: tables and diagrams -- Describing variables numerically: averages, variation and spread -- Shapes of distributions of scores -- Standard deviation and z-scores: standard unit of measurement in statistics -- Relationships between two or more variables: diagrams and tables -- Correlation coefficients: Pearson's correlation and Spearman's rho -- Regression: prediction with precision -- Part 2. Significance testing -- Samples from populations -- Statistical significance for the correlation coefficient: a practical introduction to statistical inference -- Standard error: standard deviation of the means of samples -- Related or paired-samples t-test: comparing two samples of related/correlated/paired scores -- Unrelated or independent-samples t-test: comparing two samples of unrelated/uncorrelated/independent scores -- What you need to write about your statistical analysis -- Confidence intervals -- Effect size in statistical analysis: do my findings matter? -- Chi-square: differences between samples of frequency data -- Probability -- One- versus two-tailed (or -sided) significance testing -- Ranking tests: nonparametric statistics -- Part 3. Introduction to analysis of variance -- Variance ratio test: F-ratio to compare two variances -- Analysis of variance (ANOVA): one-way unrelated or uncorrelated ANOVA -- ANOVA for correlated scores or repeated measures -- Two-way or factorial ANOVA for unrelated/uncorrelated scores: two studies for the price of one? -- Multiple comparisons within ANOVA: a priori and post hoc tests -- Mixed-design ANOVA: related and unrelated variables together -- Analysis of covariance (ANCOVA): controlling for additional variables -- Multivariate analysis of variance (MANOVA) -- Discriminant (function) analysis - especially in MANOVA -- Statistics and analysis of experiments -- Part 4. More advanced correlational statistics -- Partial correlation: spurious correlation, third or confounding variables, suppressor variables -- Factor analysis: simplifying complex data -- Multiple regression and multiple correlation -- Path analysis -- Analysis of a questionnaire/survey project -- Part 5. Assorted advanced techniques -- Meta-analysis: combining and exploring statistical findings from previous research -- Reliability in scales and measurement: consistency and agreement -- Influence of moderator variables on relationships between two variables -- Statistical power analysis: getting the sample size right -- Part 6. Advanced qualitative or nominal techniques -- Log-linear methods: analysis of complex contingency tables -- Multinomial logistic regression: distinguishing between several different categories or groups -- Binomial logistic regression -- Part 7. Bringing things together -- Data mining and big data -- Towards a masterplan: demonstrating statistical competence.
"This is the ninth edition of Understanding Statistics in Psychology. Hopefully, this is even more helpful to the student learning experience. One thing has not changed which sets this book apart from others aimed at students: it continues to provide an accessible introduction to the wide range of statistics that professional researchers employ. Students using earlier editions of the book will often be well into teaching and research careers of their own. We hope that this edition will be permanently on their desks while they instruct their students how to do statistics properly"--
English.
9781292465180
2024040042
Psychometrics.
BF39 / .H74 2024
150.15195 / H842u
Understanding statistics in psychology / Dennis Howitt and Duncan Cramer. - Ninth edition. - xxvi, 653 pages ; 27 cm.
"First published 1997"--Title page verso.
Includes bibliographical references and index.
Why statistics? -- Part 1. Descriptive statistics -- Some basics: variability and measurement -- Describing variables: tables and diagrams -- Describing variables numerically: averages, variation and spread -- Shapes of distributions of scores -- Standard deviation and z-scores: standard unit of measurement in statistics -- Relationships between two or more variables: diagrams and tables -- Correlation coefficients: Pearson's correlation and Spearman's rho -- Regression: prediction with precision -- Part 2. Significance testing -- Samples from populations -- Statistical significance for the correlation coefficient: a practical introduction to statistical inference -- Standard error: standard deviation of the means of samples -- Related or paired-samples t-test: comparing two samples of related/correlated/paired scores -- Unrelated or independent-samples t-test: comparing two samples of unrelated/uncorrelated/independent scores -- What you need to write about your statistical analysis -- Confidence intervals -- Effect size in statistical analysis: do my findings matter? -- Chi-square: differences between samples of frequency data -- Probability -- One- versus two-tailed (or -sided) significance testing -- Ranking tests: nonparametric statistics -- Part 3. Introduction to analysis of variance -- Variance ratio test: F-ratio to compare two variances -- Analysis of variance (ANOVA): one-way unrelated or uncorrelated ANOVA -- ANOVA for correlated scores or repeated measures -- Two-way or factorial ANOVA for unrelated/uncorrelated scores: two studies for the price of one? -- Multiple comparisons within ANOVA: a priori and post hoc tests -- Mixed-design ANOVA: related and unrelated variables together -- Analysis of covariance (ANCOVA): controlling for additional variables -- Multivariate analysis of variance (MANOVA) -- Discriminant (function) analysis - especially in MANOVA -- Statistics and analysis of experiments -- Part 4. More advanced correlational statistics -- Partial correlation: spurious correlation, third or confounding variables, suppressor variables -- Factor analysis: simplifying complex data -- Multiple regression and multiple correlation -- Path analysis -- Analysis of a questionnaire/survey project -- Part 5. Assorted advanced techniques -- Meta-analysis: combining and exploring statistical findings from previous research -- Reliability in scales and measurement: consistency and agreement -- Influence of moderator variables on relationships between two variables -- Statistical power analysis: getting the sample size right -- Part 6. Advanced qualitative or nominal techniques -- Log-linear methods: analysis of complex contingency tables -- Multinomial logistic regression: distinguishing between several different categories or groups -- Binomial logistic regression -- Part 7. Bringing things together -- Data mining and big data -- Towards a masterplan: demonstrating statistical competence.
"This is the ninth edition of Understanding Statistics in Psychology. Hopefully, this is even more helpful to the student learning experience. One thing has not changed which sets this book apart from others aimed at students: it continues to provide an accessible introduction to the wide range of statistics that professional researchers employ. Students using earlier editions of the book will often be well into teaching and research careers of their own. We hope that this edition will be permanently on their desks while they instruct their students how to do statistics properly"--
English.
9781292465180
2024040042
Psychometrics.
BF39 / .H74 2024
150.15195 / H842u