Choosing Research Designs II Nonexperimental Methods
The Purpose of Control Variables We use control variables to account for possible alternative explanations we can think of. For example, when I examined whether democracies are generally more peaceful than autocracies I included several control variables.
Explaining Pacifistic Democracy Peace (Y) = Democracy (X1) + State Power (X2) + Development (X3) + # of Bordering States (X4) In the model above, I have more confidence that Democracy is related to peace considering I control for the other variables that may skew my test.
We need to take care that our theory is not missing other factors that may undermine the validity of our theory and tests. Our inferences will be flawed if we are actually capturing other processes through our variables. This means that the validity of our measures would be undermined.
Several possible problems arise that are related to model misspecification and spurious relationships. Thus, we need to control for confounding factors and alternative explanations!!!
Model Misspecification and Spuriousness Antecedent variable: A variable that indirectly affects the relationship between two other variables. For example, Ivy league education increases income. However, parental wealth and legacy admissions affect Ivy league education. Thus, income of graduates from Ivy League schools may not be random.
Here Ivy League Parents is an antecedent variable Ivy League Parents Ivy League Kids high income kids Hence, admission to Ivy schools clearly not random or pure merit-based, and thus the income earned by these people.
Model Misspecification and Spuriousness Intervening Variable: These may be spuriously related to another relationship. How can states fight each other if they are not contiguous with each other? Only the strongest, with large navies, bases, etc., could do so. Hence, geographic contiguity or distance is an intervening variable. States may or may not be more peaceful, but it is hard to avoid conflict when it is on your borders.
Model Misspecification and Spuriousness Alternative Variables: We also want to control for variables that would bias our results if omitted. In this case, the X variables in a model would produce biased estimates, undermining their validity and producing error that leads to inaccurate inferences.
Here is a spurious relationship from my research IGOs + conflicts + + Powerful states Powerful states both in more IGOs and conflicts, but these two variables not directly related but a function of state power.
Classic Spurious Case Ice Cream Consumption??? + Crime + + Summer Temperatures Hence we see that despite the fact that ice cream consumption is correlated with crime, the real cause is that summer temperatures increase both ice cream consumption and crime.
Veronica Says, Beat Marshall!!! Go Miners!!! UTEP Fight! UTEP Win! I m going to Homecoming, Are you?
Non-Experimental Designs These studies use data collected or aggregated from surveys, history, or government indicators: Cross sectional studies Panel (cross sectional over a few time points) Longitudinal (time series and pooled cross-sectional time series) Case studies and focus groups
CROSS SECTIONAL Designs Statistical or case studies that compare individuals or subjects across several variables: Surveys comparing peoples political views Comparison of countries, groups, organizations along different dimensions, such as countries with different levels of development (low, medium, high) relative to other factors.
Non-Experimental Designs These studies use data collected or aggregated from surveys, history, or government indicators: Cross sectional studies Panel (somewhat rare) Longitudinal (time series and pooled cross-sectional time series) Case studies and focus groups
CROSS SECTIONAL Designs Statistical or case studies that compare individuals or subjects across several variables: Surveys comparing peoples political views Comparison of countries, groups, organizations along different dimensions, such as countries with different levels of development (low, medium, high) relative to other factors.
Cross Cross-Sectional Data Sectional Data 75 62 18.3 Kansas 16 64.6 49.9 9.8 Iowa 15 69 59.9 1.6 Indiana 14 48.9 44.5 25.6 Illinois 13 9 68.4 5.8 Idaho 12 63.7 58 21.2 Georgia 1 4 45.8 34.4 Delaware 8 37.6 44 23 Connecticut 7 67.8 51.7 18 Colorado 6 41 44.4 33.4 California 5 48 54.3 11.1 Arkansas 4 67 54.8 19.1 Arizona 3 73 62.5 15 Alabama 1 Conservative score for House delegation %Bush 4 Abortions/1, women State ID
Example of a Panel Study Example of a Panel Study 1.497 59.2 6 Bangladesh 1.445 61.9 6 Bangladesh95 1.417 65 6 Bangladesh91 1.746 3-7 Azerbaijan 1. 3-6 Azerbaijan95 1. 3-3 Azerbaijan91.942 1 Australia.932 1 Australia95.892 1 Australia91.754 1.69 5 Armenia.78 2 3 Armenia95.751 2.57 7 Armenia91.854 3.3 8 Argentina.832 3.7 7 Argentina95.81 4.3 7 Argentina91 Islamic HDI Illiteracy Democracy State
Time Series Observations are made over time, which can provide descriptive information or used to test hypotheses. If testing hypotheses, we track data for a dependent variable and at least one independent variable over time (based on some measure e.g. days, weeks, months, or years)
1 9 8 7 6 5 4 3 2 1 Example of a Time Series: Presidential Approval Approval Economic approval 1982 1983 1984 1985 1986 1987 1988 1989 199 1991 1992 1993 1994 1995 1996 1997 1998 1999 2 21 1981 Month Approval