SPSS Training

• Understanding the research process and be clear in which type of data you must collect and how to measure it
• Differentiating among different statistical models and types of results and errors
• Familiar with SPSS environment
• Know how to use SPPS to work with graphs
• Exploring groups of data and making assumptions to work on a problem
• Use SPSS forvarious statistical procedures such as Correlation, Regression, Dependent and Independent T-test, Pearson Chi-Square
• Compare Means,and conduct post hoc test
• Run several tests for statistical significance and interpreted the results

• Researchers
• Data architects
• Data scientist
• Data analyst
• Decision makers

1:  Research methods

• Statistics?
• The Research Process
  •  Initial Observation
  •  Generate Theory
  •  Generate Hypotheses
  •  Data collection to Test Theory
    • •  What to measure
      •  How to Measure
  • 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