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Research Methods Lecture 13 Quantitative Methods

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Courtney Thomas

on 5 January 2016

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Transcript of Research Methods Lecture 13 Quantitative Methods

Relationships Between Variables
Know what kind of numbers you have
Nominal, ordinal, interval
Statistical analysis can tell us if there's an association between variables
Whether knowing the value of one improves our odds of correctly guessing the corresponding value for the second
Coefficient of association: number that summarizes the association
0 to 1 or -1 to 1 with numbers closest to zero being a weak association
Direction of the association
Positive or negative
Higher/Higher v. Higher/Lower
Examples: -.87; .2
More Stats...
With a data analysis program like SPSS you can combine variables, assign control variables, and run multivariate regressions
If used, these analysis techniques should be fully explained in the article, either in the text, the notes, or the appendix
To Do Quantitative Analysis
Writing a Research Paper
Research question
Literature review
Theory
Hypotheses
Choosing variables
Independent
Dependent
Rival hypotheses
Choosing methodology
Data collection
Data coding
Data analysis
Developing the thesis
Writing the paper
Conclusions
Operationalizing Variables
How do you define terms?
Democracy?
Development?
Success?
Quality of life?
Security?
And THEN: how do you quantify (assign numbers to) a variable that is not already a number?
How have other researchers quantified variables in the data sets you can access and use OR that are used in the research you are reading?
You have to understand THEIR methodology
WHAT do the numbers MEAN?
Resources Assignment
Yes, research is hard
Sometimes you have to spend hours looking for resources
Sometimes the perfect resource isn't available
BUT there are TONS of journals and databases through the VT library that you can access free of charge
Different methods for a position paper v. a research assignment
Research Methods
PSCI 2024
Winter 2016
Dr. Courtney Thomas
Lecture 13: Quantitative Methods

Quantitative Methods
Look for relationships between quantified variables
Nominal
Categorize by assigning a number
Male = 1 Female = 2
Ordinal
Categorize and rank
Education
Interval
Categorize, rank, distance
Age; income
Validity and Reliability
Validity: Do the values assigned directly relate to or capture the concept we are trying to measure or understand?
Reliability: Are the values assigned consistently and are they related to each other in a meaningful way?
If you are going to do your own quantitative analysis you need additional coursework
STAT 2004: Intro to Stat
STAT 3604: Stat Social Sciences
Look for a course that specifically teaches SPSS or a similar program
Where Do You
Get Your Data?
Collect your own through surveys, interviews, focus groups
Assign values
Code Data
Run analysis
Published datasets
THOUSANDS of them
Available through organizations, libraries, governments, etc.
But you HAVE TO UNDERSTAND how
the data were collected and coded!
Articles, books, etc.
Where did they get their data? How?
Statistical Significance
How likely is it that the association we have measured between two variables actually exists?
Confidence level
90% confidence or statistically significant to .1
95% confidence or statistically significant to .05
99% confidence or statistically significant to .01
Measures of Association
and Significance
Nominal:
Lambda: association (0 to 1)
Chi-Square: statistical significance
Ordinal
Gamma: association (-1 to 1 where 0 is the absence of association)
Tau-b: association with same number of categories
Tau-c: association with different number of categories
Standard score of gamma: significance
Interval:
Pearson
r
or correlation coefficient measures explained variance (-1 to 1)
r2 measures association
Can only determine significance when both variables are normally distributed
Full transcript