1. Review-1 SPSS Training Naveen Shrestha

2. Epidemiologic Study Designs A.Descriptive studies 1.Populations (ecological studies) 2.Individuals a.Case reports and case series b.Cross-sectional surveys

3. B.Analytic studies 1.Observational studies a.Case-control studies (always retrospective) b.Cohort studies (retrospective or prospective) 2.Intervention studies a.randomized controlled trials b.field trials c.community trials

4. Relative Risk Results of a cohort study that followed 100 non- diabetic nurses for 15 years. At the end of the 15 years their smoking behavior was related to their diabetic status. Smoking Not smoking Total Diabetic 50 20 70 Not Diabetic 25 5 30 Total 75 25 100 RR = = 0.666667/0.8 = 0.833375 50/75 20/25

5. we can calculate the Odds Ratio for all the suspected risk factors: Gastroenteritis No Gastroenteritis Eaten Not Eaten Not Odds Ratio Lunch 22/1 6 31 9 48 6/31 / 9/48 = 1.0 Lunch 23/1 18 19 14 4318/19 / 14/43= 2.9 Salad 12 24 5 5212/24 / 5/52 = 5.2 Sandwich 16 21 14 4416/21 / 14/44= 2.4 Chicken 4 33 4 54 4/33 / 4/54 = 1.6 If the disease is rare <10% the RR and OR would be similar

6. P value and confidence interval P value reflects both the magnitude of association and sample size Confidence interval (CI) is more informative –Wide CI = variability

7. Sample size n = z 2 pq/l 2 n = the desired sample size z = the standard normal deviate, usually set at 1.96 (or more simply 2.0), which corresponds to the 95 percent confidence level. p = the proportion in the target population estimated to have a particular characteristics. If there is no reasonable estimate, then use 50% q = 1-p l = degree of accuracy desired (0.05) n = (2)2 (0.5) (0.5)/ (0.05)2 = 400

8. What is SPSS Like a spreadsheet: stores and manipulates numbers. Does calculations. Like a database: Stores records about individuals. It is: a statistics package: others include Statistica, Systat, BMDP, SAS.

9. Introduction to SPSS SPSS is a software package used for conducting statistical analyses, manipulating data, and generating tables and graphs that summarize data. Statistical analyses range from basic descriptive statistics, such as averages and frequencies, to advanced inferential statistics, such as regression models, analysis of variance, and factor analysis. SPSS also contains several tools for manipulating data, including functions for recoding data and computing new variables as well as merging and aggregating datasets. SPSS also has a number of ways to summarize and display data in the form of tables and graphs.

10. Data editor

11. Output viewer

12. Syntax editor