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 decrease Gas 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 Coefficient This 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.

Or in other words …..

Therefore through some algebraic magic we get the computational formula, which is a bit more manageable.

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

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 =.457r =.336

4. Assumes a linear relationship

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