- 1: Research methods
- 2: Statistics
- 3: SPSS Environment
- 4: Exploring data with graphs
- 5: Exploring assumptions
- 6: Correlation
- 7: Regression
- 8: Categorical predictor in multiple regression
- 9: Logistic regression
- 10: Comparing two means (t-test)
- 11: Comparing several means: ANOVA (GLM)
- 12: Chi-square
1: Research methods
- Statistics?
- The Research Process
- Initial Observation
- Generate Theory
- Generate Hypotheses
- Data collection to Test Theory
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- What to measure
- How to Measure
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- Analyze data
- Descriptive Statistics: Overview
- Central Tendency
- Measure of variation
- Coefficient of Variation
- Fitting Statistical Models
- Conclusion
2: Statistics
- Building statistical models
- Types of statistical models
- Populations and samples
- Simple statistical models
- The mean as a model
- The variance and standard deviation
- Central Limit Theorem
- The standard error
- Confidence Intervals
- Test statistics
- Non-significant results and Significant results:
- One- and two-tailed tests
- Type I and Type II errors
- Effect Sizes
- Statistical power
3: SPSS Environment
- Accessing SPSS
- To explore the key windows in SPSS
- Data editor
- The viewer
- The syntax editor
- How to create variables
- Enter Data and adjust the properties of your variables
- How to Load Files and Save
- Opening Excel Files
- Recoding Variables
- Deleting/Inserting a Case or a Column
- Selecting Cases
- Using SPSS Help
4: Exploring data with graphs
- The art of presenting data
- The SPSS Chart Builder
- Histograms: a good way to spot obvious problems
- Boxplots (box–whisker diagrams)
- Graphing means: bar charts and error bars
- Simple bar charts for independent means
- Clustered bar charts for independent means
- Simple bar charts for related means
- Clustered bar charts for related means
- Clustered bar charts for ‘mixed’ designs
- Line charts
- Graphing relationships: the scatterplot
- Simple scatterplot
- Grouped scatterplot
- Simple and grouped -D scatterplots
- Matrix scatterplot
- Simple dot plot or density plot
- Drop-line graph
- Editing graphs
5: Exploring assumptions
- What are assumptions?
- Assumptions of parametric data
- The assumption of normality
- Quantifying normality with numbers
- Exploring groups of data
- Testing whether a distribution is normal
- Kolmogorov–Smirnov test on SPSS
- Output from the explore procedure
- Reporting the K–S test
- Testing for homogeneity of variance
- Levene’s test
- Reporting Levene’s test
- Correcting problems in the data
- Dealing with outliers
- Dealing with non-normality and unequal variances
- Transforming the data using SPSS
6: Correlation
- Looking at relationships
- How do we measure relationships?
- Covariance
- Standardization and the correlation coefficient
- The significance of the correlation coefficient
- Confidence intervals for r
- Correlation in SPSS
- Bivariate correlation
- Pearson’s correlation coefficient
- Spearman’s correlation coefficient
- Kendall’s tau (non-parametric)
- Biserial and point–biserial correlations
- Partial correlation
- The theory behind part and partial correlation
- Partial correlation using SPSS
- Semi-partial (or part) correlations
- Comparing correlations
- Comparing independent rs
- dependent rs
- Calculating the effect size
- How to report correlation coefficients
7: Regression
- An introduction to regression
- Some important information about straight lines
- The method of least squares
- Assessing the goodness of fit: sums of squares, R and R2
- Doing simple regression on SPSS
- Interpreting a simple regression
- Overall fit of the model
- Model parameters
- Using the model
- Multiple regression: the basics
- An example of a multiple regression model
- Sums of squares, R and R2
- Methods of regression
- How accurate is my regression model?
- Assessing the regression model I: diagnostics
- Assessing the regression model II: generalization
- How to do multiple regression using SPSS
- Some things to think about before the analysis
- Main options
- Statistics
- Regression plots
- Saving regression diagnostics
- Interpreting multiple regression
- Descriptive
- Summary of model
- Model parameters
- Excluded variables
- Assessing the assumption of no multicollinearity
- Casewise diagnostics
- Checking assumptions
- What if I violate an assumption?
- to report multiple regression
8: Categorical predictor in multiple regression
- Dummy coding
- SPSS output for dummy variables
9: Logistic regression
- Background to logistic regression
- What are the principles behind logistic regression?
- Assessing the model: the log-likelihood statistic
- Assessing the model: R and R2
- The Wald statistic
- The odds ratio: Exp (B)
- Methods of logistic regression
- Assumptions
- Incomplete information from the predictors
- Complete separation
- Overdispersion
- Binary logistic regression
- The main analysis
- Method of regression
- Categorical predictors
- Obtaining residuals
- Interpreting logistic regression
- The initial model
- Step: intervention
- Listing predicted probabilities
- Interpreting residuals
- Calculating the effect size
- How to report logistic regression
- Testing assumptions
- Testing for linearity of the logit
- Testing for multicollinearity
- Predicting several categories: multinomial logistic regression
- Running multinomial logistic regression in SPSS
- Statistics
- Other options
- Interpreting the multinomial logistic regression output
- Reporting the results
10: Comparing two means (t-test)
- Looking at differences
- A problem with error bar graphs of repeated-measures designs
- Step : calculate the mean for each participant
- Step : calculate the grand mean
- Step : calculate the adjustment factor
- : create adjusted values for each variable
- The t-test
- Rationale for the t-test
- Assumptions of the t-test
- The dependent t-test
- Sampling distributions and the standard error
- The dependent t-test equation explained
- The dependent t-test and the assumption of normality
- Dependent t-tests using SPSS
- Output from the dependent t-test
- Calculating the effect size
- Reporting the dependent t-test
- The independent t-test
- The independent t-test equation explained
- The independent t-test using SPSS
- Output from the independent t-test
- Calculating the effect size
- Reporting the independent t-test
- Between groups or repeated measures?
- The t-test as a general linear model
11: Comparing several means: ANOVA (GLM)
- The theory behind ANOVA
- Inflated error rates
- Interpreting f-test
- ANOVA as regression
- Logic of the f-ratio
- Total sum of squares (SST)
- Model sum of squares (SSM)
- Residual sum of squares (SSR)
- Mean squares
- The f-ratio
- Assumptions of ANOVA
- Planned contrasts
- Post hoc procedure
- Running one-way ANOVA on SPSS
- Planned comparisons using SPSS
- Post hoc tests in SPSS
- Output from one-way ANOVA
- Output for the main analysis
- Output for planned comparisons
- Output for post hoc tests
- Calculating the effect size
- Reporting results from one-way independent ANOVA
- Violations of assumptions in one-way independent ANOVA
12: Chi-square
- Analysing categorical data
- Theory of analysing categorical data
- Pearson’s chi-square test
- Fisher’s exact test
- The likelihood ratio
- Yates’ correction
- Assumptions of the chi-square test
- Doing chi-square on SPSS
- Running the analysis
- Output for the chi-square test
- Breaking down a significant chi-square test with standardized residuals
- Calculating an effect size
- Reporting the results of chi-square