Statistical Package for Social Science TRAINING - RADOKI..pptx

Notes on statistical package for social science students aids in data analysis and research

1. RADOKI STATISTICS SOLUTIONS SPSS TRAINING – FOR BEGINNERS Email: [email protected] Phone: [phone]

2. SPSS T raining C ourse O utline 1. Introduction to SPSS Interface and Functionalities Overview of SPSS and its applications Navigating the SPSS interface Data View and Variable View Menus and toolbars Importing and exporting data Importing data from Excel, CSV, and other formats Exporting data to various formats

3. SPSS T raining C ourse O utline Managing data Defining variables and their properties Labeling and coding variables Data entry and cleaning Handling missing values

4. SPSS T raining C ourse O utline 2. Descriptive Statistics and Exploratory Data Analysis Descriptive statistics Measures of central tendency (mean, median, mode)Measures of dispersion (range, variance, standard deviation) Frequency distributions Cross-tabulations and contingency tables Data visualization Creating and interpreting histograms, bar charts, pie charts, and box plots Customizing graphs and charts

5. SPSS T raining C ourse O utline 3. Advanced Statistical Procedures Analysis of Variance (ANOVA) One-way ANOVA Two-way ANOVA

6. SPSS T raining C ourse O utline 4. Interpretation and Presentation of Results Interpreting SPSS output Understanding tables and charts Making sense of statistical significance and confidence intervals Reporting results

7. Introduction to SPSS What is SPSS? SPSS: is a popular program for statistical analysis. It is used often in the behavioral sciences, like psychology, but it works well for lots of other fields. So, why has SPSS become so popular? The biggest strength of SPSS is it’s user interface. Now, I might argue that it’s biggest strength is actually with data handling, but it’s popularity is not doubt because of that user interface, which lies on drop-down menus, making it very easy to teach and learn.

8. Introduction to SPSS The common criticism of SPSS is because of it’s user interface. Because it makes statistical analysis simple, it also risks making analysis simplistic. It allows people to run tests without really knowing what they are doing, and it encourages mindless analysis. So, in this training I am showing you how to use SPSS a bit mindlessly. But I can teach you tests and interpretations in another series of training. So for now, you get comfortable with SPSS software, and later we are going to learn about theory and research design.

9. What does “SPSS “ stand for? The letter’s SPSS used to stand for S tatistical P ackage for the S ocial S ciences. SPSS was created for the social sciences like psychology, sociology, Health and Human Services, way back in 1968. In 2009, SPSS was purchased by IBM and because it is used in a variety of fields other than Social Sciences, SPSS is an acronym like BP, KFC, AOL, BNFS, AT&T, or IBM. The letters no longer stand for anything and its proper name is now IBM SPSS Statistics.

10. What does “SPSS “ stand for? Start by opening the SPSS software on your PC or computer. When the splash page pops up, just dismiss it and then we can get to work. Lets start with basics. There are two view modes to SPSS: 1. Data View 2. Variable View

11. SPSS I nterface o verview Data View: Displays the dataset in a spreadsheet format, where each row represents a case (e.g., a survey respondent), and each column represents a variable (e.g., age, gender, income). Variable View: Allows users to define the properties of each variable. Each row corresponds to a variable and columns include:

12. SPSS I nterface o verview Name: The variable's name. Type: The data type (e.g., numeric, string). Width: The number of characters allowed for the variable. Decimals: The number of decimal places. Label: A descriptive label for the variable. Values: Labels for categorical data (e.g., 1 = Male, 2 = Female). Missing: Codes for missing values.

13. SPSS I nterface o verview Columns: The column width in Data View. Align: Alignment of data (left, right, center). Measure: The level of measurement (nominal, ordinal, scale). Role: The role of the variable (input, target, etc.).

14. Basic Functionalities Opening and Saving Files: SPSS can open various file formats including .sav, .csv, . xls , and save files in its native .sav format. Path to open files: File > Open > Data Path to save files: File > Save As

15. Basic Functionalities Data Entry and Editing: Data can be entered manually into the Data View or imported from external files. Editing data involves modifying existing values, adding new cases, or deleting cases/variables. Variable Definition: Define variables in Variable View by specifying their properties such as name, type, and labels.

16. Basic Functionalities Data Transformation: Transform data using various tools: Compute Variable: Create new variables based on calculations. Path: Transform > Compute Variable Recode into Same Variables/Different Variables: Change the values of a variable. Path: Transform > Recode into Same Variables or Recode into Different Variables

17. Basic Functionalities Count Values within Cases: Count occurrences of certain values within cases. Path: Transform > Count Values within Cases

18. Descriptive Statistics and Exploratory Data Analysis 1. Descriptive Statistics Frequencies: Analyze the frequency distribution of categorical variables. Outputs include frequency tables and bar charts. Path: Analyze > Descriptive Statistics > Frequencies Descriptive: Obtain summary statistics (mean, standard deviation, minimum, maximum) for continuous variables. Path: Analyze > Descriptive Statistics > Descriptive

19. Descriptive Statistics and Exploratory Data Analysis Explore: Perform exploratory data analysis, providing measures of central tendency, dispersion, and normality tests, along with plots such as boxplots. Path: Analyze > Descriptive Statistics > Explore Crosstabs: Examine the relationship between two categorical variables, including chi-square tests and contingency tables. Path: Analyze > Descriptive Statistics > Crosstabs

20. Descriptive Statistics and Exploratory Data Analysis 2. Data Visualization Charts and Graphs: SPSS provides a variety of charting options to visualize data. Bar Charts: Display the frequency of categories. Path: Graphs > Chart Builder Histograms: Show the distribution of continuous data. Path: Graphs > Chart Builder Pie Charts: Display proportions of categories. Path: Graphs > Chart Builder

21. Descriptive Statistics and Exploratory Data Analysis Scatterplots: Show relationships between two continuous variables. Path: Graphs > Chart Builder

22. Advanced Statistical Procedures 1. ANOVA (Analysis of Variance) One-Way ANOVA: Compare the means of three or more groups to determine if there are statistically significant differences among them. Path: Analyze > Compare Means > One-Way ANOVA Output includes an ANOVA table showing F-statistic, degrees of freedom, and significance level.

23. Advanced Statistical Procedures Two-Way ANOVA: Explore the interaction effects between two independent variables on a dependent variable. Path: Analyze > General Linear Model > Univariate Output includes main effects, interaction effects, and post hoc tests if specified.

24. Advanced Statistical Procedures 2. Presentation of Results Exporting Output: Export SPSS output to formats like Word, Excel, or PDF for inclusion in reports and presentations. Path: File > Export Creating APA-Style Tables: Format tables according to APA guidelines for academic papers. Include clear labels, appropriate decimal places, and significance indicators.

25. Advanced Statistical Procedures Reporting Findings: Write clear interpretations of results: Context: Provide background and rationale for the analysis. Methodology: Describe the data, variables, and statistical methods used. Results: Present the findings with relevant tables and charts. Conclusions: Summarize the implications of the findings and suggest possible actions or further research.

26. RADOKI STATISTICS SOLUTIONS THANK YOU FOR YOUR ATTENTION