1. Analyzing Surveys

2. The Goal Once research is collected Analyze to find patterns Analyze to find connections (Correlations) Find the cause of these correlations (causality)

3. Finding patterns Create a list of each respondents answers Database Use Excel Number off possible choices for each question Categorize open ended questions Categorize observed responses

4. Compare the answers in different categories using “countif” statements or putting the data filter on One variable analysis can be used to find averages or percents or totals. Compare the correlation between different categories (you will need questions that have numerical value) Two or more variable analysis can be done using the chart wizard icon and the skills learned previously in the course. Remember to use pg. 166 in the text as a guide for entering data and creating a scatter plot.

5. What is correlation? indicates the strength and direction of a linear relationship between two random variables Do the two variables appear to have a relationship or an effect on each other? Eg. As one variable increases does the other also increase or decrease? Eg. Grade vs. Average total spent on lunch

6. Correlation vs. Causality Correlation does NOT mean causality Causality is the cause of an event Just because B happened after A does not mean A caused B to happen

7. Example of false causality Example Hot weather may cause both crime and ice-cream purchases. Therefore crime is correlated with ice- cream purchases. But crime does not cause ice-cream purchases ice-cream purchases do not cause crime

8. How do we find causality? More advanced statistical analysis Well designed and probing research Secondary research Multiple primary studies Educated guesses – can be wrong a lot

9. Pitfalls and Traps in Stats Errors in method Sample too small Sample too large Lack of replication

10. Problems with interpretation Accuracy/precision Universality Assumptions Interpreters bias False causation Lack of causation

11. Graphing Visual representation of the results Pie graphs – one variable Shows percentages that make up a total – one variable Bar graphs – one variable Line graph – one variable Scatter plots – two variable

12. To try… Super quick survey 1. Are you ___ male ___ female? 2. How tall are you? ________ 3. Do you enjoy watching soap operas? ___ Yes ___ No 4. What religion are you? ____________________ 5. Do you think violence in sports is appropriate? ___ Always___ Sometimes___ Rarely___ Never 6. What would you rather do on a Saturday night? ___ movie ___ club___ house party ___ stay at home with friend(s) 7. How many fruits and/or vegetables do you eat in a day usually? ___0 ___1 ___2 ___3 ___4 ___5 ___6 ___7 ___8 ___9 E-mail your answers to your teacher!

13. Using Excel Open the file titled “Excel Demonstration” to find a sample list of answers to the quick survey.

14. Using a “Countif” statement To find a total of a category from your data, type “countif(range)” put the range of data you are looking at and specify what you are looking for. In this example I am looking at how many males took the survey so I put in: countif(A3:A22,“m”) and pressed enter. The output was 9.

16. Using the data filter Highlight your table of data and click on the data menu at the top of the screen. Click on “filter” then “autofilter” and arrows should appear beside the question numbers.

17. Now you can sort the data based on category using your arrows. Lets select all of the students who said violence in sports is rarely appropriate. That was question 5, so click on the arrow beside question 5 and select r.

18. You will see the table reduced to 4 entries, two male and two female. Try it on your own! Select all from the pull down menu beside question 5 to reset. Now find how many catholics go to the movies!

19. For Q4 select c and for Q6 select m The answer is 4! 2 males and 2 females!

20. Consolidation On the sample survey, identify all of the biased questions and rewrite them to eliminate any bias. Identify two questions that could be used to see if there is a correlation and predict what the correlation might be.