San Diego State University School of Social Work 610 COMPUTER APPLICATIONS FOR SOCIAL WORK PRACTICE Statistical Package for the Social Sciences Office: Hepner Hall (HH) 100 Instructor: Mario D. Garrett, Ph.D. Email: mgarrett@mail.sdsu.edu Phone: 619-594-2818 Office: Hepner Hall (HH) 100 COURSE SYLLABUS OVERVIEW I. COURSE PURPOSE The purpose of the course is to provide graduate students with a practical knowledge of social science research based on a combination of a statistical computer application and an understanding of the methods used to undertake statistical analyses. This course provides students with basic graduate-level statistical techniques using SPSS that allow them to undertake social science research analyses using computer statistical software. This course covers topics related to learning how to use computer software to undertake social science research in practice. In academia, empirical research is the foundation for progress. II. LEARNING OUTCOMES Students will be able to: 1. Demonstrate a practical knowledge and application of research methods in social science practice. 2. Develop a working understanding of SPSS that will guide data analysis for survey and intervention research. 3. Will know how to collect, organize and interpret data Students will have specific skills that will allow them to: 1. Demonstrate an ability to apply principles of social science research methods into practice. 2. Demonstrate an ability to develop reliable and valid research questions. 3. Develop skills in identifying types of research design and data to be collected. 4. Demonstrate skills in analyzing data collected through both survey and intervention research.
5. Demonstrate skills in using data and information in evaluation of efficacy of intervention and effectiveness of program. 6. Undertake and interpret statistical analyses. III. TEXTBOOK READINGS Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 ISBN 97818478790734 All data can be found at their websites http://www.sagepub.com/field3e/spssdata.htm IV. COURSE ASSIGNMENTS There will be 3 Exams that cover all the course content. There will be no final exam and no final paper. The course is graded on the 3 exam (30% x 3) and attendance (10%). GRADING USE THE GRADE CALCULATOR HERE (EXCEL SPREADSHEET) Point Scores: A: 95 100 A : 90 94 B+: 87 89 B : 83 86 B : 80 82 C+: 77 79 C: 73 76 C : 70 72 D+: 60 69 F: Less than 59 Grade Guidelines as in the Student Graduate Handbook: Grades of A or A- are reserved for student work that not only demonstrates excellent mastery of content, but also shows that the student has (a) undertaken complex tasks, (b) applied critical thinking skills to the assignment, and/or (c) demonstrated creativity in her or his
approach to the assignment. The degree to which the student demonstrates these skills determines whether he/she receives an A or an A-. A grade of B+ is given to work that is judged to be very good. This grade denotes that a student has demonstrated a more-than-satisfactory understanding of the material being tested, and has exceeded expectations in the assignment. A grade of B is given to student work that meets the basic requirements of the assignment. It denotes that the student has done satisfactory work on the assignment and meets the expectations of the course. A grade of B- denotes that a student's performance was less than satisfactory on an assignment, reflecting only moderate grasp of content and is below expectations. A grade of C reflects a minimal grasp of the assignments, poor organization of ideas and/or several significant areas requiring improvement. Grades between C- and F denote a failure to meet minimum standards, reflecting serious deficiencies in a student's performance on the assignment. A grade of Credit in graduate level courses is equivalent to grades that earn 3.0 or more grade points (B and above). A grade of No Credit in graduate level course`s is equivalent to grades that earn less than 3.0 grade points (B- and below). A grade of RP (Report in Progress) is used in courses that extend more than one term (e.g., Field courses: SW 650 and 750). It indicates that work is in progress, has been evaluated, and is satisfactory to date. In assigning grades, the assumption will be that the student has completed the assignment at an average level of achievement (B for graduate students; C for undergraduate students). Students who demonstrate higher or lower performance levels will receive grades consistent with the guidelines provided, including plus and minus grades
Participation and attendance policy will be followed as adopted by the SSW. V. EXPECTATIONS FROM STUDENTS You will need to get a gmail account to access all course details posted on google sites. VI. COURSE TOPIC AND OUTLINES For podcasts of all the Andy Fields lectures, please go to: http://www.sagepub.com/field3e/spssstudentmovies.htm This course starts with a session on the use of the computer and Internet technology in various social science practices. Thus, in the first part of the course, we will explore and learn about capabilities and potentials the Internet provides for social science practice and research. More specifically, it involves discussion on the use of the computer technology in gathering information and assessing effectiveness of intervention related to social science practice. The second part of the course involves data analysis using the Statistical Package for Social Sciences (SPSS) program. You will learn how to analyze data from two major sources such as survey research and intervention research data. After data analysis, you will also learn how to appropriately report your findings and results. Finally, this course will discuss the use of qualitative research methods in analyzing non numerical types of data. Each class will be split into two sections. The first section includes a didactic format with a presentation on the topic for that class. The second section allows for a hands-on demonstration of the techniques discussed. Each class is independent of previous or subsequent classes. Schedule Class 1: 28 January Class 2: 4 February Class 3: Class 4: Class 5: Class 6: Class 7: Class 8: Class 9: 11 February 18 February Exam 25 February 4 March 11 march 18 March Exam 25 March 1 April SPRING BREAK Class 10: Class 11: Class 12: 8 April 15 April 22 April Exam
Class 13: Class 14: 29 April Tutorial 6 May Class 1: INTRODUCTION TO THE COURSE Introduction to the course. Expectations, grading, exam papers. Chapter 1. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 1.1 Class 2: OBTAINING DATA:SECONDARY DATA SOURCES Chapter 2. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 1.2 Z Score video The output from SPSS (in blue) and the arithmetic, with the degrees of freedom always (n-1). Age Mean Variance Squared Square root 23 30-7 44 7 24 30-6 32 6 25 30-5 22 5 26 30-4 13 4 27 30-3 7 3 34 30 4 19 4 35 30 5 28 5 36 30 6 40 6 37 30 7 54 7 TOTAL 0 260 47 sqrt of the total 260 = 16 260/(9-1)= 32.5 47/(9-1)= 5.8 Mean Std. Deviation Variance Statistic Std. Error Statistic Statistic 29.67 1.9 5.701 32.5
CLASS 3: CREATING A DATABASE http://dataferrett.census.gov/ PowerPoint Presentation Class 2.13 Chapter 3. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 4 DATA FILE download SYNTAX FILE download Class 5: Creating a new data file, translating, & data entry. Chapter 3.4. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 Practical Class Excel data to import CLASS 4: MANIPULATING DATA Class 6: Cleaning a data file. Chapter 3.5,3.6,3.7,3.8,3.9. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 Practical Class AGE DATA SPPS SYNTAX FINAL DATA BASE EXAM 1 If you look at last week's database we had a mistake in the AGE variable. We need to rectify this mistake and today's class we will go through two possible ways of doing this.you will need to combine two datasets...the new one above (AGE.DATA) with the old database created last week. TEXT File here CLASS 5: ANALYZING DATA Class 7: Using SPSS to summarize and describe sample; Graphs
Chapter 4. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 7 MASTER DATAFILE Exam Anxiety.sav CLASS 6: DESCRIPTIVE ANALYSIS Descriptive statistics vs. inferential statistics. Exploring Assumptions Chapter 5.1, 5.2, 5.3, 5.4, 5.5. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 8 CLASS 7: EXPLORING ASSUMPTIONS Central tendency and dispersion statistics. Exploring Assumptions Chapter 5. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 9 CLASS 8: DATA MANIPULATION COMPUTE, RECODE, & SELECT DATA FILE FOR LESSON PowerPoint Presentation Class 10 Men Like Dogs file EXAM 2 CLASS 9: NON PARAMETRIC TESTS 1 Class 11: Exploring relationship among variables: Non parametric Mann-Whitney Chapter 15.1, 15.2, 15.3. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 CLASS FILE FOR LESSON PowerPoint Presentation Class 11 Class 12: Exploring relationship among variables: Non parametric Wilcoxon
Chapter 15.4. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 12 CLASS 10: NON PARAMETRIC TESTS 2 Class 13: Exploring relationship among variables: Non parametric Kruskal-Wallis test Chapter 15.5. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 13 Class 14: Exploring relationship among variables: Non parametric Chi-square Chapter 18.1,18.2, 18.3, 18.4, 18.5. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 14 CLASS EXERCISE Hill et al.sav Williams.sav Exam Anxiety.sav Calculating and Interpreting Expected and Chi-Square Table I will work through a 3x3 contingency table. The method will be directly applicable to any similar problem. The following table refers to the performance of a baseball team on various pitches classified as good, medium and bad. The null hypothesis is that the state of the pitch does not affect the performance of the team. Wins Draws Losses Total --------- -------------------------- ---------- Good 11 6 4 21 Medium 12 7 7 26 Bad 7 7 14 28
--------- -------------------------- ----------- Total 30 20 25 75 To find the expected frequencies, we assume independence of the rows and columns. To get the expected frequency corresponding to the 11 at top left, we look at row total (21) and column total (30), multiply them, and then divide by the overall total (75). So the expected frequency is: 21*30 ------- = 8.4 75 So to complete the expected table, draw up another table similar to that above and having the same row and column totals. For each entry in this table, we simply calculate (row total*column total)/75. The completed table is: Wins Draws Losses Total ----------- -------------------------- ----------- Good 8.4 5.6 7.0 21 Medium 10.4 6.9 8.7 26 Bad 11.2 7.5 9.3 28 ----------- -------------------------- ------------ Total 30 20 25 75 The number of degrees of freedom is calculated for an m-by-n table as (m-1)(n-1), so in this case (3-1)(3-1) = 2*2 = 4. To calculate X^2, we then have a further table:
O E O-E O-E ^2/E ---------------------------------------- 11 8.4 2.6 0.805 6 5.6 0.4 0.0286 4 7 3 1.2857 12 10.4 1.6 0.246 7 6.9 0.1 0.0014 7 8.7 1.7 0.332 7 11.2 4.2 1.575 7 7.5 0.5 0.033 14 9.3 4.7 2.375 ------------------------------------------- Total = 6.70 = X^2 The tabular 95% value of X^2 (degrees of freedom = 4) is 9.49, so the value of X^2 that we obtained (6.70) is not significant at the 5% level. We conclude that the state of the pitch does not affect the performance of the team. CLASS 11: RELATIONSHIPS AMONG VARIABLES Class 15: Exploring relationship among variables: Correlation coefficient Chapter 6. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 15 Exam Anxiety.sav CLASS 12: EXAMINING DIFFERENCES Class 16: Examining differences in SPSS for Windows: Paired Sample and Dependent Samples t-tests Chapter 9.3, 9.4. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009
PowerPoint Presentation Class 16 SAMPLE DATA FOR INDEPENDENT GROUP DESIGN SAMPLE DATA FOR DEPENDENT GROUP DESIGN t-test computation Video Class 17: Examining differences in SPSS for Windows: Independent Samples t-tests Chapter 9.5, 9.6, 9.7, 9.8. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 17 DEGREES OF FREEDOM VIDEO CLASS 13: MULTIVARIATE ANALYSIS Class 18: Examining differences in SPSS for Windows: One way Analysis of Variance (ANOVA) Chapter 10. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 18 VIAGRA DATASET FOR ANOVA Class 19: Examining differences in SPSS for Windows: Two way Analysis of Variance (ANCOVA) Chapter 11. Andy Field Discovering Statistics Using SPSS. Sage. Third Edition 2009 PowerPoint Presentation Class 19 VIAGRA COVARIATE DATABASE EXAM 3 DATA FILE download SYNTAX FILE download What percentage has: Has poor health in general Heart failure Arthritis
Hay fever Cold or flu What percentage of Males has: Has poor health in general Heart failure Arthritis Hay fever Cold or flu How many that have Hay fever also reported having Arthritis. Is this a significant difference? For people with poor health that is the % of Heart failure, Arthritis, Hay fever and Cold or flu? PRACTICAL100 DATA SPSS SAV FILE WORKSHEET CLASS 14: WRITE UP Class 20: How To Report Results in MSWord: Tables and Write-up. PowerPoint Presentation Class 20 TRIAL DATA FOR TESTING Cheat Sheet on Reporting Statistics for Psychologists Three or four things to report You will be reporting three or four things, depending on whether you find a significant result for your 1- Way Betwee Subjects ANOVA
1. Test type and use You want to tell your reader what type of analysis you conducted. This will help your reader make sense of your results. You also want to tell your reader why this particular analysis was used. What did your analysis test for? Example You can report data from your own experiments by using the template below. A one-way between subjects ANOVA was conducted to compare the effect of (IV) on (DV) in,, and conditions. If we were reporting data for our example, we might write a sentence like this. A one-way between subjects ANOVA was conducted to compare the effect of sugar on memory for words in sugar, a little sugar and no sugar conditions. 2. Significant differences between conditions You want to tell your reader whether or not there was a significant difference between condition means. You can report data from your own experiments by using the template below. There was a significant (not a significant) effect of IV on DV at the p<.05 level for the three conditions [F(, ) =, p = ]. Just fill in the blanks by using the SPSS output Let s fill in the values. You are reporting the degrees of freedom (df), the F value (F) and the Sig. value (often referred to as the p value).
Once the blanks are full You have a sentence that looks very scientific but was actually very simple to produce. There was a significant effect of amount of sugar on words remembered at the p<.05 level for the three conditions [F(2, 12) = 4.94, p = 0.027]. 3. Only if result of test was significant, report results of post hoc tests In the previous chapter on interpretation, you learned that the significance value generated in a 1-Way Between Subjects ANOVA doesn t tell you everything. If you find a significant effect using this type of test, you can conclude that there is a significant difference between some of the conditions in your experiment. However, you will not know where this effect exists. The significant difference could be between any or all of the conditions in your experiment. In the previous chapter, you learned that to determine where significance exists you need to conduct a post hoc test to compare each condition with all other conditions. If you have an IV with 3 levels, like the one in this example, you would need to conduct and report the results of a post hoc test to report which conditions are significantly different from which other conditions. Example Because we have found a statistically significant result in this example, we needed to compute a post hoc test. We selected the Tukey post hoc test. This test is designed to compare each of our conditions to every other conditions. This test will compare the Sugar and No Sugar conditions. It will also compare the A little sugar and No Sugar conditions. It will also compare the A Little Sugar and Sugar conditions. The results of the Tukey post hoc must be reported if you find a significant effect for your overall ANOVA. You can use the following template to report the results of your Tukey post hoc test. Just fill in the means and standard deviation values for each condition. They are located in your Descriptives box. If you used this template with our example, you would end up with a sentence that looks something like this. Post hoc comparisons using the Tukey HSD test indicated that the mean score for the sugar condition (M = 4.20, SD = 1.30) was significantly different than the no sugar condition (M = 2.20, SD = 0.84). However, the a little sugar condition (M = 3.60, SD = 0.89) did not significantly differ from the sugar and no sugar conditions.
4. Report your results in words that people can understand Since it might be hard for someone to figure out what that sentence means or how it relates to your experiment, you want to briefly recap in words that people can understand. Try to imagine trying to explain your results to someone who is not familiar with science. In one sentence, explain your results in easy to understand language. Example You might write something like this for our example. Taken together, these results suggest that high levels of sugar really do have an effect on memory for words. Specifically, our results suggest that when humans consume high levels of sugar, they remember more words. However, it should be noted that sugar level must be high in order to see an effect. Medium sugar levels do not appear to significantly increase word memory. This sentence is so much easier to understand than the scientific one with all of the numbers in it. Let s see how this looks all together When you put the three main components together, results look something like this. A one-way between subjects ANOVA was conducted to compare the effect of sugar on memory for words in sugar, a little sugar and no sugar conditions. There was a significant effect of amount of sugar on words remembered at the p<.05 level for the three conditions [F(2, 12) = 4.94, p = 0.027]. Post hoc comparisons using the Tukey HSD test indicated that the mean score for the sugar condition (M = 4.20, SD = 1.30) was significantly different than the no sugar condition (M = 2.20, SD = 0.84). However, the a little sugar condition (M = 3.60, SD = 0.89) did not significantly differ from the sugar and no sugar conditions. Taken together, these results suggest that high levels of sugar really do have an effect on memory for words. Specifically, our results suggest that when humans consume high levels of sugar, they remember more words. However, it should be noted that sugar level must be high in order to see an effect. Medium sugar levels do not appear to significantly increase word memory. Looks pretty complicated but it is simple when you know how to write each part. PRACTICAL EXERCISE DATA PRACTICAL EXERCISE QUESTIONS
CLASS 15: REVIEW OF THE COURSE Cheat Sheet of the Course Exercises: Pre-Exam Data ASCII File (DATA) Pre-Exam Syntax File (SPSS)