1. SPSS Basics and Applications Workshop: Introduction to Statistics Using SPSS

2. 22 Workshop: Main Goal To ease your anxiety about using SPSS as the data analysis tool for your dissertation project

3. 33 Workshop: Objectives  SPSS overview  Open SPSS  Open existing data file, import external data files, create new data file, save data file  Screen and clean data (Sorting Method)  Basic procedures  Selecting specific cases  Creating and running syntax  Data Analysis  Descriptive statistics (quantitative and categorical variables)  Inferential statistics  Independent-Samples T-Test  Analysis of Variance (ANOVA)  Pearson Product Moment Correlation (r)  Summary/Conclusion  Questions  Evaluations

4. 44 SPSS Overview  Powerful data analysis package  Social sciences, natural science, business, etc.  Requires basic computer skills (Windows environment)  PC and MAC platforms  Basic edition and advanced edition (depending on design)  Quantitative methods  Comparing group means  Correlation  Univariate designs (single independent variable)  Factorial designs (multiple independent variables)  Multivariate designs (multiple dependent variables)  Sole source for completing your dissertation

5. 55 Open SPSS Clicking on the SPSS icon on your desktop will open SPSS. The applicable version of SPSS will open. The example above is for SPSS v.17.0 (MAC).

6. 66 The First View This is the first view you will see. You will have several options to select from : –“Type in data” will create a new data set –“Open an existing data source” will allow you to work with an existing data file –Open another type of file” will allow you to open a file such as Excel, Access, or other usable file The other options are advanced procedures; thus beyond the scope of this workshop.

7. 77 Open Existing Data File Select “Open an existing data source” (Arrow 1) Click OK (Arrow 2) Note: You will need to know where the data file is located. I suggest you create a folder for your dissertation project. For some, it may be best to keep the file on your desktop. Regardless, it is your file and you need to know where it is located. For this exercise, click on the file “SPSS Workshop Data File.sav” that is located on your desktop. Two primary views: Data view and variable view 1 2

8. 88 Data View

9. 99 Variable View

10. 10 Import External Data (Excel) Select “File/Open/Data”

11. 11 Import External Data (Excel) 1. Map to the stored location of your file 2. Select Appropriate File Type 3. Select your file (External Data.xls) 4. Click “Open”

12. 12 Import External Data (Cont’d) 1.Ensure “Read variables names from the first row of data” is checked (Arrow 1) 2.Click “OK” (Arrow 2) 1 2

13. 13 Import External Data (Cont’d)

14. 14 Create New Data Set 1.Open SPSS 2.Select “Type in data” (Arrow 1) 3.Click “OK” (Arrow 2) 4. Blank data editor screen will appear 1 2

15. 15 Screen and Cleaning Data PERHAPS THE MOST IMPORTANT STEP IN YOUR ANALYSIS GOGO – Garbage In Garbage Out Data must be screened to ensure it has been entered correctly. Sever Consequences – Threats to Statistical Conclusion Validity Example in Correlation Analysis –Correlation analysis is extremely sensitive to outliers –Entering 55 instead of 5 can seriously distort a correlation analysis –You report there is a significant difference when there really is not (Type II Error) –You report there is no significant relationship when there is (Type I error) Sort Case Method* * There are a host of other ways to evaluate the accuracy of data

16. Screen Data 16 Screen and Cleaning Data Bad Data

17. 17 Cleaning Data - Sorting Method Click “Data” Click “Sort Cases” Insert Variable(s) in Sort by box (Arrow 1) Select “Sort Order” (Arrow 2) Click “OK” (Arrow 3) Data will be sorted Look for bad data 1 2 3

18. Sorted Cases 18 Sorted Data Range of FoS data is 0-10 Bad data identified from (Arrow 1) down Identify case # in ID column (Arrow 2) Retrieve Survey; make correction 2 1

19. 19 Understanding Syntax Files Syntax refers to computer code SPSS can save the created code that you can use later; eliminates you having to repeat steps over and over. We’ll review later

20. 20 Variables Nominal –Categorical (No Value) –Gender (Male/Female –Politics (Democrat/Republican) Ordinal –Rank Order –1= 1 st Grade, = 2 nd Grade, etc. Interval –Quantitative –No true zero value –Temperature Ratio –Quantitative –True zero value –Height, weight, age

21. 21 Descriptive Statistics Procedures used to summarize, organize, and simplify data Does not address research questions/hypotheses Makes data more manageable Usually placed in tables in chapter 4 (Results) Quantitative examples (Ordinal, Interval, Ratio) –Mean –Standard Deviation –Range Categorical examples (Nominal Data) –Percentages

22. 22 Descriptive Statistics – Quantitative Variables 1.Select “Analyze” (Arrow 1) 2.Select “Descriptive Statistics” (Arrow 2) 3.Select “Descriptives” (Arrow 3) 2 1 3

23. 23 Descriptive Statistics – Quantitative Variables APA Output

24. 24 Descriptive Statistics – Categorical Variables SPSS Output

25. 25 Inferential Statistics Hypotheses p-value and Alpha Level Statistical Significance (p <.05) Independent Samples T-Test Analysis of Variance (ANOVA) Pearson Correlation (r)

26. 26 Inferential Statistics Techniques that allow us to study samples and make generalizations about the population from which they were selected Addresses research questions/hypotheses Presented in chapter 4 (Results) Examples –T-Test –ANOVA –Correlation –Chi-Square

27. 27 Hypotheses (Null and Alternative) Null Hypothesis –First and most important of the two; the one you hope to reject –States the treatment has no effect –There is no change, difference or relationship –Hence the name “Null” –Statistically represented as H 0 Alternative Hypothesis –States the treatment has an effect –There is a change, difference, or relationship –Statistically represented as H 1 Example –Null Hypothesis (H 0 ): Gender has no impact on IQ –Alternative Hypothesis (H 1 ): Gender has an impact on IQ

28. 28 P-value and Alpha Level P-value –The P-value (probability) is a measure of how confident we can be that what we observe in the sample is also true for the population. –The P-value is important in inference. We infer from what we see in the sample to the population. Alpha Level –The alpha level is the P-value that we as researchers decide to accept before we will be confident enough to release a finding. –This is our predetermined acceptance level. –The alpha level is not calculated, it is chosen by the researcher(s) –In the social sciences, an alpha level of.05 is generally considered "acceptable.“ –A result, in SPSS, less than.05 (p <.05) is “significant”

29. 29 Statistical Significance (p <.05) Means the results did not happen by chance; something is going on; assuming the null hypothesis is true. We can reject the null hypothesis (there is no effect, difference, or relationship) We conclude there is an effect, difference, or relationship –Thus, we accept the alternative hypothesis Example –Null Hypothesis (H 0 ): Gender has no impact on IQ –Alternative Hypothesis (H 1 ): Gender has an impact on IQ –SPSS reports a result where p <.05 We reject the null hypothesis and conclude gender does have an impact IQ.

30. Independent-Samples T Test Comparing the Mean Difference Between Two Groups

31. 31 Independent Samples T-Test Used to compare the means of two independent groups. – Experimental studies using a control group and experimental group (random assignment) – Non-experimental studies comparing two groups (e.g. Gender) Example Question Is there a statistically significant gender mean difference in fear of statistics, measured by the Dr. Taylor Fear of Statistics Scale? Independent variable: Gender, with two levels (Male/Female) Dependent variable: Quantitative score on FoS scale Example Hypotheses: Null Hypothesis (H 0 ): There is not a statistically significant gender mean difference in fear of statistics, measured by the Dr. Taylor Fear of Statistics Scale. Alternative Hypothesis: There is a statistically significant gender mean difference in fear of statistics, measured by the Dr. Taylor Fear of Statistics Scale. ***Let’s analyze and prepare the APA statistical results write-up!!!***

32. 32 Independent Samples T-Test Click “Analyze” (Arrow 1) Click Compare Means (Arrow 2) Click “Independent-Samples T Test” (Arrow 3) 1 2 3

33. 33 Independent Samples T-Test Move dependent variable (FoS) into “Test Variable” box (Arrow 1) Move Independent variable into “Grouping Variable box (Arrow 2) Click “Define Groups” (Arrow 3) Enter “1” for Group 1 = Males (Arrow 4) Enter “2” for Group 2 = Females (Arrow 5) Click “Continue” (Arrow 6) 1 2 3 4 5 6

34. 34 Independent Samples T-Test SPSS Output

35. 35 Independent Samples T-Test APA Write-Up An independent samples t-test, alpha =.05, two-tailed, was conducted to determine if there is a statistically significant gender mean difference in Fear of Statistics, measured by the Dr. Taylor Fear of Statistics (FoS) scale. The independent variable was gender, with two levels, male and female. The dependent variable was the score on the FoS scale. The null hypothesis stated gender will not impact fear of statistics. The alternative hypothesis stated gender will impact fear of statistics. The Levene’s test for equality of variance indicated this assumption was met. The results were significant, t(38) = 3.185, p =.003. The results indicated males (M=8.00, SD=1.690) reported higher fear of statistics than females (M=6.39, SD=1.461). The effect size, measured by r 2 was.21, indicating 21% of the variance in FoS scores are accounted for by gender. The 95% CI was.587 – 2.635.

36. Analysis of Variance (ANOVA) Comparing the Mean Difference Between More Than Two Groups

37. 37 Analysis of Variance (ANOVA) Purpose –Used to compare the means of more than two independent groups. Experimental studies using a control group and experimental group (random assignment) Non-experimental studies comparing two or more groups (e.g.) Example Question: Is there a statistically significant milestone mean difference in dissertation depression, measured by the Dr. Taylor Dissertation Depression (DD) Scale? Independent variable: Milestone, with four levels (M1, M2, M3, M4) Dependent variable: Quantitative score on DD scale Example Hypotheses: Null Hypothesis (H 0 ): There is not a statistically significant milestone mean difference in dissertation depression, measured by the Dr. Taylor Dissertation Scale. Alternative Hypothesis: There is a statistically significant milestone mean difference in dissertation depression, measured by the Dr. Taylor Dissertation Scale.

38. 38 Analysis of Variance (ANOVA) Click “Analyze” (Arrow 1) Click Compare Means (Arrow 2) Click “One-way ANOVA” (Arrow 3) 1 2 3

39. 39 Analysis of Variance (ANOVA) 1. Place Dependent Variable in “Dependent List” (Arrow 1) 2. Place independent Variable in “Factor” Dialogue Box (Arrow 2) 3. Click “Post Hoc” Button (Arrow 3) 4. Click “OK” (Arrow 4) 1 2 3 4

40. 40 Analysis of Variance (ANOVA) 1.Select “LSD” (Arrow 1) 2.Click “Continue” (Arrow 2) 1 2

41. 41 Analysis of Variance (ANOVA) SPSS Output

42. 42 Analysis of Variance (ANOVA) Post Hoc Results (Where exactly are the differences?)

43. 43 Analysis of Variance (ANOVA) APA Write-Up A one-way ANOVA was conducted to evaluate group mean differences of dissertation depression scores, measured by the Dr. Taylor Dissertation Depression (DD) Scale. The independent variable was milestone, with 4 levels (Milestone 1, Milestone II, Milestone III, Milestone IV). The dependent variable was the score on the Dr. Taylor DD scale. The null hypothesis stated there is not a group mean difference in dissertation depression. The alternative hypothesis stated there is a group mean difference in dissertation depression. The assumptions of normality, independence, and equal variances were met. The results were significant, F(3,36) = 128.5, p <.01, indicating there is a significant group mean difference in dissertation depression. A post hoc test, using Tukey’s LSD method, identified the Milestone III students (M = 8.4, SD =.516) as having statistically significant higher dissertation depression than the Milestone I (M = 2.0, SD =.756), Milestone II (M = 2.14, SD =.167) and Milestone IV (M = 2.0, SD =.756) students.

44. Pearson Product Moment Correlation (r) Comparing the Relationship Between Two Quantitative Variables

45. 45 Pearson Correlation Used to examine the relationship between two quantitative variables –Example Question: Is there a statistically significant relationship between GPA and fear of Statistics, measured by the Dr. Taylor Fear of Statistics Scale? Doesn’t conceptualize independent and dependent variables –Example Hypotheses: Null Hypothesis (H 0 ): There is not a statistically significant relationship between GPA and fear of Statistics, measured by the Dr. Taylor Fear of Statistics Scale? Alternative Hypothesis: There is not a statistically significant relationship between GPA and fear of Statistics, measured by the Dr. Taylor Fear of Statistics Scale? ***Let’s analyze and prepare the APA statistical results write-up!!!***

46. 46 Pearson Correlation 1. Click “Analyze” (Arrow 1) 2. Click “Correlate” (Arrow 2) 3. Click “Bivariate” (Arrow 3) 1 23

47. 47 Pearson Correlation 1. Insert Variables in “Variable Box” (Arrow 1) 2. Select “Pearson” (Arrow 2) 3. Select “Two-tailed” (Arrow 3) 4. Select “Flag Significant Correlations” (Arrow 4) 5. Click “OK” (Arrow 5) 1 3 2 4 5

48. 48 Pearson Correlation SPSS Output and APA Write-Up Pearson’s correlation test, alpha =.05, two=tailed, examined the relationship between Walden students overall GPA and fear of statistics, measured by the Dr. Taylor Fear of Statistics scale. The null hypothesis stated there is no relationship between GPA and fear of statistics. The alternative hypothesis stated there is a relationship between GPA and fear of statistics. The assumptions of normality and linearity were met. The results were insignificant, r = -.234, p =.146.

49. 49 Workshop: Objectives  SPSS overview  Open SPSS  Open existing data file, import external data files, create new data file, save data file  Screen and clean data (Sorting Method)  Basic procedures  Selecting specific cases  Creating and running syntax  Data Analysis  Descriptive statistics (quantitative and categorical variables)  Inferential statistics  Independent-Samples T-Test  Analysis of Variance (ANOVA)  Pearson Product Moment Correlation (r)  Summary/Conclusion  Questions  Evaluations

50. 50 Questions ???