Introduction to SPSS in psychology - Info and Reading Options

"Introduction to SPSS in psychology" Table Of Contents:

• 1- Introduction to SPSS
• 2- Basics of SPSS data entry and statistical analysis
• 3- Descriptive statistics
• 4- Describing variables : tables and diagrams
• 5- Describing variables numerically : averages, variation and spread
• 6- Shapes of distributions of scores
• 7- Standard deviation : the standard unit of measurement in statistics
• 8- Relationships between two or more variables : diagrams and tables
• 9- Correlation coefficients : Pearson's correlation and Spearman's rho
• 10- Regression : prediction with precision
• 11- Significance testing and basic inferential tests
• 12- Standard error
• 13- The t-test : comparing two samples of correlated/related scores
• 14- The t-test : comparing two groups of unrelated/uncorrelated scores
• 15- Confidence intervals
• 16- Chi-square : differences between samples of frequency data
• 17- Ranking tests for two groups : non-parametric statistics
• 18- Ranking tests for three or more groups : non-parametric statistics
• 19- Analysis of variance
• 20- The variance ratio test : using the F-ratio to compare two variances
• 21- Analysis of variance (ANOVA) : introduction to the one-way unrelated or uncorrelated ANOVA
• 22- Analysis of variance for correlated scores or repeated measures
• 23- Two-way analysis of variance for unrelated/uncorrelated scores
• 24- Multiple comparisons in ANOVA
• 25- Two-way mixed analysis of variance (ANOVA)
• 26- Analysis of covariance (ANCOVA)
• 27- Multivariate analysis of variance (MANOVA)
• 28- Discriminant function analysis (for MANOVA)
• 29- More advanced correlational statistics
• 30- Partial correlation
• 31- Factor analysis
• 32- Item reliability and inter-rater agreement
• 33- Stepwise multiple regression
• 34- Hierarchical multiple regression
• 35- Advanced qualitative or nominal techniques
• 36- Log-linear analysis
• 37- Multinomial logistic regression
• 38- Binomial logistic regression
• 39- Data handling procedures
• 40- Reading ASCII or text files into the data editor
• 41- Missing values
• 42- Recoding values
• 43- Computing new variables with no values missing
• 44- Computing new variables with some values missing
• 45- Selecting cases
• 46- Samples and populations : generating a random sample
• 47- Inputting a correlation matrix
• 48- Checking the accuracy of data input.

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