Unobtrusive Research 1.Content analysis - examine written documents such as editorials. 2.Analyses...
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Transcript of Unobtrusive Research 1.Content analysis - examine written documents such as editorials. 2.Analyses...
Unobtrusive Research
1. Content analysis - examine written documents such as editorials.
2. Analyses of existing statistics.
3. Historical/comparative analysis - historical records.
Comparative & Historical Analysis
• Historically grounded explanations of large-scale and substantively important outcomes
• Sources of Data: Newspapers, Literature Review, Govt. Docs., biographies, diaries, letters, etc.
• Long Tradition: Weber, Durkheim, Marx, Lipset, Skocpol
• Types of Comparative and Historical Analyses– Historical events research: focus on events in
one short historical period– Historical process research: traces a
sequence of events over a number of years– Cross-sectional comparative research:
comparing data from one time period between two or more units.
– Comparative historical research: comparing data from more than one time period in more than one unit
Qualities of Qualitative Historical Research
Case Oriented: focus on unit(s) as a whole
Holistic: how various parts or conditions fit together.
Temporal: taking into account related series of events.
Narrative: researches a story involving actors & events.
Inductive: develops an explanation for what happened
Historical Events Research
Event-Structure Analysis: systematic coding of events/historical information
Griffen (1993) – events leading to the lynching
Oral History: produces written records that can be analyzed
Pagnini & Morgan (1996) – 1170 life histories from the Great Depression
Historical Events Research: an auto manufacturing plant that
produces SUVs closes
Oil embargo
Political crisis
Foreign produced fuel efficient cars increase in popularity
SUV sales decreaseGas
prices increase
Another corporation buys the plant
New owner decreases wages
Workers strike
New owner decides it’s more cost effective to close the plant and move to a less developed country
© Pine Forge Press, an imprint of Sage Publications, 2006
Historical Process Research
• Extends historical events research by focusing on a series of events.
• Can use quantitative data to examine variation over time.
• Example: Number of laws, spending, international agreements, voter turnout, etc.
Comparative Methods
Cross-sectional Comparative Research
Frequently quantitative/variable-oriented research
Comparative Historical Research
Comparisons between cases to highlight the particular features of each case or identify general historical patterns across units.
Paige (1999) - the development of capitalism in various nations.
Cross-Sectional Comparative
Comparative Historical: Lipset (1959)
Comparative & Historical Analysis
Cautions:• Can't trust the accuracy of records - official
or unofficial, primary or secondary.• Must be wary of bias in data sources.• Rarely systematic in data collection
– Based on what’s available– Or on what supports your argument– Measuring across time and contexts
Aviles
• What is the research question?
• What is the theory?
• What is the research design?
• What is the evidence/data?
• What are the findings?
Correlation• MEASURING ASSOCIATION• Establishing a degree of association
between two or more variables gets at the central objective of the scientific enterprise. Scientists spend most of their time figuring out how one thing relates to another and structuring these relationships into explanatory theories. The question of association comes up in normal discourse as well, as in "like father like son“.
Scatterplots
A. scatter diagram
A list of 1,078 pairs of heights would be impossible to grasp. [so we need some method that can examine this data and convert it into a more conceivable format]. One method is plotting the data for the two variables (father's height and son's height) in a graph called a scatter diagram.
B. The Correlation CoefficientThis scatter plot looks like a cloud of points which
visually can give us a nice representation and a gut feeling on the strength of the relationship, and is especially useful for examining outliners or data anomalies, but statistics isn't too fond of simply providing a gut feeling. Statistics is interested in the summary and interpretation of masses of numerical data - so we need to summarize this relationship numerically. How do we do that - yes, with a correlation coefficient.
The correlation coefficient ranges from +1 to -1
r = 1.0
r = .85
r = .42
R = .17
R = - .94
R = - .54
R = - .33
• Computing the Pearson's r correlation coefficient
• Definitional formula is:
Convert each variable to standard units (zscores). The average of the products give the correlation coefficient. But this formula requires you to calculate z-scores for each observation, which means you have to calculate the standard deviation of X and Y before you can get started. For example, look what you have to do for only 5 cases.
Dividing the Sum of ZxZy (2.50) by N (5) get you the correlation coefficient = .50
• The above formula can also be translated into the following – which is a little easier to decipher but is still tedious to use.
yxSSSS
SPr
))(( YYXXSP
2)( XXSSx
2)( YYSS y
• Or in other words …..
22YYXX
YYXXr
• Therefore through some algebraic magic we get the computational formula, which is a bit more manageable.
2222 YNYXNX
YXNXYr
Interpreting correlation coefficients• Strong Association versus Weak
Association: strong: knowing one helps a lot in predicting the other. Weak, information about one variables does not help much in guessing the other. 0 = none; .25 weak; .5 moderate; .75 < strong
• Index of Association• R-squared defined as the proportion of the
variance of one variable accounted for by another variable a.k.a PRE STATISTIC (Proportionate Reduction of Error))
Significance of the correlation
• Null hypothesis?
• Formula:
• Then look to Table C in Appendix B
• Or just look at Table F in Appendix B
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r
Nrt
Limitations of Pearson's r
• 1) at best, one must speak of "strong" and "weak," "some" and "none"-- precisely the vagueness statistical work is meant to cure.
• 2) Assumes Interval level data: Variables measured at different levels require that different statistics be used to test for association.
• 3) Outliers and nonlinearity• The correlation coefficient does not always give a true
indication of the clustering. There are two main exceptional cases: Outliers and nonlinearity.
r = .457 r = .336
4. Assumes a linear relationship
0
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50000
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0 5 10 15 20 25 30
Education
Sala
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4) Christopher Achen in 1977 argues (and shows empirically) that two correlations can differ because the variance in the samples differ, not because the underlying relationship has changed.
Solution?
Regression analysis