Data Analysis Presentation
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![Page 1: Data Analysis Presentation](https://reader033.fdocuments.in/reader033/viewer/2022051513/547d4790b37959932b8b5329/html5/thumbnails/1.jpg)
Data AnalysisRWJF || GRC
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Available SoftwareQuantitative
R
SPSS
STATA
Qualitative
NVivo
Atlas.ti
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The Big Problem
How do you ask a question that a computer can answer?
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Asking the right question
Are you looking for descriptive statistics?
Are you looking for confirmation/refutation of a hypothesis?
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Asking the right question
Descriptive statistics are perhaps the easiest to structure.
Quantitative: Central tendency, quartiles, data range
Qualitative: Frequent words, reoccurring themes
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Hypothesis testing requires additional steps
Formulate hypothesis prior to test, have a clear null and alternative established
Asking the right question
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H0: There is no difference/relationship
Ha: There is a difference/relationship
Asking the right question
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Quantitative
Regression
Is there a relationship between variable X and Y?
Asking the right question
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Quantitative
Difference
Is there a difference between variable X and Y?
Asking the right question
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Many problems with any kind of data analysis software stem from impossible to answer questions.
Asking the right question
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Simplify Questions
Break your question down to individual steps, as small as you can go.
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Simplify Questions
“I need to test the GPA of students who scored below a 26 on the ACT vs. those who scored above.”
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Simplify Questions
To answer this question, you need a software function that can order 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 be easier if you break the problem up into its components.
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Simplify Questions
“I need to test the GPA of students who scored below a 26 on the ACT vs. 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.
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Simplify Questions
Data analysis is a lot easier when each step is made smaller.
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Choosing your Software
Quantitative
R
Pros: Most flexibility, free, customized software
Cons: Very difficult to learn
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Choosing your Software
Quantitative
SPSS
Pros: User friendly, frequently used, allows for infinite cases, has drop-down commands.
Cons: Expensive, can be difficult to interpret results.
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Choosing your Software
Quantitative
STATA
Pros: Allows for syntax-based do-files that create consistent change tracking
Cons: Expensive, can be difficult use
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Choosing your Software
Quantitative
Excel
Pros: Ubiquitous, somewhat smaller learning curve
Cons: Fundamental limitations in formulas
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Choosing your Software
Qualitative
NVivo
Pros: Can structure qualitative data
Cons: Expensive, can be difficult use
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Choosing your Software
Qualitative
ATLAS.ti
Pros: Can structure qualitative data
Cons: Expensive, can be difficult use
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Moving towards your results
Research oftentimes can be broken down into two distinct kinds:
Quantitative – Confirms (Deductive)
Qualitative – Exploratory (Inductive)
In reality, this differentiation is not concrete.
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Where does software fit in?Quantitative
Hypothesis
Test (Software)
Conclusion
Theory
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Where does software fit in?Qualitative
Data
Conclusion
Patterns/Theory
Analysis (Software)