SPSS Training Course

  Lecture -1 Research methods

·     Statistics?

·     The Research Process

·     Initial Observation

·     Generate Theory

·     Generate Hypotheses

·     Data collection to Test Theory

·     Analyze data

·     Descriptive Statistics: Overview

·     Central Tendency

·     Measure of variation

·     Coefficient of Variation

·     Fitting Statistical Models

·     Conclusion

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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 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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -8 Categorical predictor in multiple regression
·     Dummy coding

·     SPSS output for dummy variables

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -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

·     Practical Exercise

  Lecture -12 Chi-square

·     Analyzing categorical data

·     Theory of analyzing 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

·     Practical Exercise

  Case Studies