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# Data Analysis Presentation

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### Data Analysis Presentation

1. 1. Data Analysis RWJF || GRC
2. 2. Available SoftwareQuantitative R SPSS STATAQualitative NVivo Atlas.ti
3. 3. The Big ProblemHow do you ask a question that a computer can answer?
4. 4. Asking the right questionAre you looking for descriptive statistics?Are you looking for confirmation/refutation of a hypothesis?
5. 5. Asking the right questionDescriptive statistics are perhaps theeasiest to structure. Quantitative: Central tendency, quartiles, data range Qualitative: Frequent words, reoccurring themes
6. 6. Asking the right questionHypothesis testing requiresadditional steps Formulate hypothesis prior to test, have a clear null and alternative established
7. 7. Asking the right questionH0: There is no difference/relationshipHa: There is a difference/relationship
8. 8. Asking the right questionQuantitative Regression Is there a relationship between variable X and Y?
9. 9. Asking the right questionQuantitative Difference Is there a difference between variable X and Y?
10. 10. Asking the right questionMany problems with any kind of data analysis software stem fromimpossible to answer questions.
11. 11. Simplify QuestionsBreak your question down to individual steps, as small as you cango.
12. 12. Simplify Questions“I need to test the GPA of students who scored below a 26 on theACT vs. those who scored above.”
13. 13. Simplify QuestionsTo answer this question, you need a software function that canorder the data, split it, and test the variables you need.It can be hard to find one program that does all that - but it can beeasier if you break the problem up into its components.
14. 14. Simplify Questions“I need to test the GPA of studentswho scored below a 26 on the ACTvs. those who scored above.”• Break the question down into individual steps: 1. Sort the data by lowest to highest ACT 2. Divide into ACT scores below 26 and scores above 3. Run a two-sample T-test on the GPA’s from each group.
15. 15. Simplify QuestionsData analysis is a lot easier when each step is made smaller.
16. 16. Choosing your SoftwareQuantitative R Pros: Most flexibility, free, customized software Cons: Very difficult to learn
17. 17. Choosing your SoftwareQuantitative SPSS Pros: User friendly, frequently used, allows for infinite cases, has drop-down commands. Cons: Expensive, can be difficult to interpret results.
18. 18. Choosing your SoftwareQuantitative STATA Pros: Allows for syntax-based do-files that create consistent change tracking Cons: Expensive, can be difficult use
19. 19. Choosing your SoftwareQuantitative Excel Pros: Ubiquitous, somewhat smaller learning curve Cons: Fundamental limitations in formulas
20. 20. Choosing your SoftwareQualitative NVivo Pros: Can structure qualitative data Cons: Expensive, can be difficult use
21. 21. Choosing your SoftwareQualitative ATLAS.ti Pros: Can structure qualitative data Cons: Expensive, can be difficult use
22. 22. Moving towards your resultsResearch oftentimes can be broken down into two distinct kinds: Quantitative – Confirms (Deductive) Qualitative – Exploratory (Inductive)In reality, this differentiation is not concrete.
23. 23. Where does software fit in?QuantitativeTheory Hypothesis Test (Software) Conclusion
24. 24. Where does software fit in?Qualitative Data Analysis (Software) Patterns/Theory Conclusion
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