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Course Content
1. Research Methods
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Statistics The Research Methodology First Impression Create Theory Create Hypotheses Data gathering to put Theory to the Test What should be measured How to Calculate Data analysis Overview of Descriptive Statistics Tendency Central Variation measurement Variation Coefficient Statistical Model Fitting Conclusion.
2. Statistics
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Developing statistical models Statistical model types Samples and populations Basic statistical models As a model, consider the mean. The standard deviation and variance Theorem of the Central Limit The standard deviation Intervals of Confidence Statistics from tests Non-significant and significant outcomes: Tests with one and two tails Type I and Type II mistakes Sizes of Effects Statistical significance.
3. SPSS Environment
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SPSS Accessing SPSS key windows exploration Editor of data The audience The syntax checker Variables: How to Make Them Enter data and modify the variables’ attributes. How to Load and Save Files Excel Files Cannot Be Opened Variable Recoding Delete/Insert a Case or Column Case Selection SPSS Assistance.
4. Exploring Data With Graphs
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The art of data presentation SPSS Chart Creator Histograms are an excellent tool for identifying evident issues. Boxplots (box–whisker diagrams) Graphing entails the use of bar charts and error bars. Simple bar graphs for unrelated means Independent means clustered bar charts Simple bar graphs for similar meanings Bar graphs with clusters with related means Bar charts with clusters for’mixed’ designs Line graphs Relationship graphing: the scatterplot A simple scatterplot scatterplot with groups -D scatterplots, both simple and grouped Scatterplot Matrix Dot plot or density plot? Graph with a drop-line Graph editing.
5. Exploring Assumptions
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What exactly are assumptions? Parametric data assumptions The normalcy assumption Using statistics to quantify normalcy Investigating data sets Determining whether or not a distribution is normal.
SPSS Kolmogorov-Smirnov test 1. The results of the investigate process 2. K-S test reporting
Testing for variance homogeneity 1. The Levene test 2. Reporting Levene’s test results
Correction of data errors 1. Handling outliers 2. Confronting non-normality and uneven variances 3. Using SPSS to transform the data.
6. Correlation
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Examining connections How do we quantify relationships? 1. Covariance 2. The correlation coefficient and standardization The correlation coefficient’s importance r confidence intervals.
SPSS Correlation
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1. Correlation between two variables 2. Pearson’s coefficient of correlation 3. Spearman’s coefficient of correlation 4. Kendall’s remark (non-parametric) 5. Point-biserial and biserial correlations 6. Correlation in part 7. The theory behind partial and part correlation 8. SPSS for partial correlation 9. Partial (or semi-partial) correlations Correlation analysis Using independent rs to compare reliant rs calculating the size of the influence How should correlation coefficients be reported?
7. Regression
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An overview of regression Some crucial straight line facts The least squares approach Sums of squares, R, and R2 are used to evaluate the quality of fit.
Using SPSS to perform basic regression
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1. Explaining a basic regression 2. Model’s overall fit 3. Model settings 4. Applying the model.
The Fundamentals of Multiple Regression
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1. Explaining a basic regression 2. Model’s overall fit 3. Model settings 4. Applying the model The Fundamentals of Multiple Regression 1. An illustration of a multiple regression model 2. Square sums, R and R2 3. Regression methods 4. How good is my regression model? 5. Regression model evaluation, I: diagnostics 6. Regression model assessment II: generalization.
• How to do multiple regression using SPSS
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1. Things to consider before conducting the analysis 2. Primary alternatives 3. Statistics 4. Regression graphs 5. Regression diagnostics saving 6. Multiple regression interpretation.
• Descriptive
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1. Model synopsis 2. Parameters of the model 3. Variables omitted 4. Evaluation of the premise of no multicollinearity 5. Diagnosis by case.
• Checking assumptions
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1. What if I break one of the assumptions? 2. To document multiple regression.
8. Categorical Predictor in Multiple Regression
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Bogus coding and Dummy variable SPSS output
9. Logistic Regression
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Introduction to logistic regression What are the underlying concepts of logistic regression? The log-likelihood statistic is used to evaluate the model R and R2 are used to evaluate the model. 1. Wald’s statistic 2. The probability ratio: Exp (B)
• Methods of logistic regression
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1. Assumptions 2. Inadequate information from forecasters 3. Otal separation 4. Overdispersion 5. Binomial logistic regression 6. The major examination 7. Regression method 8. Classification predictors 9. Determining residuals.
• Interpreting logistic regression
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1. The preliminary model 2. Action: involvement 3. Predictions of probability 4. Interpretation of residuals 5. Determining the extent of the influence.
• Testing assumptions
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1. Checking the logit for linearity 2. Multicollinearity testing.
• Running multinomial logistic regression in SPSS
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1. Statistics 2. Other possibilities 3. Interpreting the results of multinomial logistic regression 4. Results reporting.
10. Comparing two means (t-test)
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Examining distinctions A difficulty with repeated-measures error bar graphs 1. Determine the mean for each participant. 2. Step 2: compute the grand mean 3. Step: Determine the correction factor 4. Generate corrected values for each variable.
• Rationale for the t-test 1. The t-test assumptions 2. The t-test for dependent variables 3. The standard error and sampling distributions 4. Explaining the dependent t-test equation 5. The dependent t-test and the normality assumption 6. SPSS dependent t-tests 7. Results of the dependent t-test 8. Determining the extent of the influence
• Reporting the dependent t-test 1. The t-test for independence 2. Explaining the independent t-test equation 3. The SPSS independent t-test 4. Results of the independent t-test 5. Determining the extent of the influence.
11. Comparing Several Means: ANOVA (GLM)
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• The theory behind ANOVA • Inflated error rates • Interpreting f-test • ANOVA as regression 1.The f-Logic ratio’s 2.Total square sum (SST) 3.Sum of squares model (SSM) 4.Square sum of residuals (SSR) 5th. Mean squares.
Sixth, 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 1.Output from a one-way valve ANOVA 2.Results of the primary analysis 3.Planned comparison output 4.Results of post-hoc tests 5.Determining the extent of the influence 6.Reporting one-way independent ANOVA findings 7.Assumption violations in one-way independent ANOVA.
12.Chi-Square
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• Analyzing categorical data • Theory of analyzing categorical data 1.The Pearson chi-square test 2.The Fisher exact test • The likelihood ratio • Yates’ correction • Assumptions of the chi-square test • Doing chi-square on SPSS 1.Start the analysis 2.Results of the chi-square test 3.Deconstructing a significant chi-square test using standardized residuals 4.Determining the extent of an influence • Reporting the results of chi-square.
SPSS Training
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