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04_regression_coefficients

Plotting Regression Coefficients

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Graphs that appear in paper

Figure 6: Presenting a single regression model using a dotplot with error bars

Figure 7: Using parallel dotplots with error bars to present two regression models

Figure 8: Using “small multiple” plots to present multiple regression models

Figure 6

Table 2 from Stevens (2006): Determinants of Authoritarian Aggression

Variable Coefficient (Standard Error)
Constant .41 (.93)
Countries
Argentina 1.31 (.33)### B,M
Chile .93 (.32)### B,M
Colombia 1.46 (.32) ### B,M
Mexico .07 (.32)A,CH,CO,V
Venezuela .96 (.37)## B,M
Threat
Retrospective egocentric economic perceptions .20 (.13)
Prospective egocentric economic perceptions .22 (.12)#
Retrospective sociotropic economic perceptions -.21 (.12)#
Prospective sociotropic economic perceptions -.32 (.12)##
Ideological Distance from president
Ideology
Ideology .23 (.07) ###
Individual Differences
Age .00 (.01)
Female -.03 (.21)
Education .13 (.14)
Academic Sector .15 (.29)
Business Sector .31 (.25)
Government Sector -.10 (.27)
R2 .15
Adjusted R2 .12
n 500
###p < .01, ##p < .05, #p < .10 (two-tailed)
A Coefficient is significantly different from Argentina’s at p < .05;
B Coefficient is significantly different from Brazil’s at p < .05;
CH Coefficient is significantly different from Chile’s at p < .05;
CO Coefficient is significantly different from Colombia’s at p < .05;
M Coefficient is significantly different from Mexico’s at p < .05;
V Coefficient is significantly different from Venzeluela’s at p < .05

Our graph

Download the R code for this graph.

Figure 7

Table 1 from Pekkanen, Nyblade and Krauss (2006)

Our graph

Download the R code for this graph.

Figure 8

Table 4 from Ansolabehere and Konisky (2006): Registration effects on turnout in New York and Ohio counties: Fixed effects model, 1954–2000

Dependent Variable = County-Level Turnout
Full sample (1) Excluding counties w/partial registration (2) Full sample w/state-year dummies (3) Full sample (4) Excluding counties w/partial registration (5) Full sample w/state-year dummies (6)
% of county with registration –0.039## (0.003) –0.036## (0.003) –0.051## (0.003) –0.037## (0.003) –0.034## (0.003) –0.050## (0.003)
Law change –0.020## (0.005) –0.018## (0.005) –0.023## (0.006)
Log population 0.048## (0.011) 0.036## (0.012) 0.017 (0.010) 0.047## (0.011) –0.035## (0.021) 0.016 (0.010)
Log median family income –0.133## (0.013) –0.142## (0.014) 0.050## (0.013) –0.131## (0.013) –0.139## (0.014) –0.049## (0.013)
% population with h.s. education 0.071# (0.028) 0.070# (0.029) 0.011 (0.024) 0.072# (0.028) 0.071# (0.029) 0.013 (0.024)
% population African American –0.795## (0.056) –0.834## (0.059) –0.532## (0.044) –0.783## (0.055) –0.822## (0.059) –0.521## (0.044)
Constant 1.47## (0.152) 1.70## (0.171) 0.775## (0.124) 1.45## (0.152) 1.68## (0.170) 0.819## (0.127)
R2 0.91 0.91 0.94 0.91 0.91 0.94
N 3572 3153 3572 3572 3153 3572
Note. #p < .05, ##p < .01. Huber-White standard errors in parentheses. Year dummies and state-year dummies are not reported.

Our Graph

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To create this graph, you will also need a function that we wrote called plot.reg, available here.

Graphs that do not appear in paper

Regression plots with multiple confidence intervals

The following graph is identical to Figure 6, except we add vertical tick marks to indicate 90% confidence intervals (i.e. when p < .10).

Download the R code for this graph.

Notes

In general, we don't recommend using multiple confidence intervals, as 95% intervals will usually suffice. (The Journal of Politics, for example, instructs authors not to “use varying numbers of asterisks to indicate different levels of statistical significance.”) However, in cases where multiple intervals are desirable, tick marks (or, alternatively, lines of different width) are useful for differentiating intervals.

Automatic regression plots

We also created functions that exemplify how regression plots could be done automatically. You will need the Zelig R package to try this out. According to their authors, “Zelig is a single, easy-to-use program that can estimate, help interpret, and present the results of a large range of statistical methods.” The functions are in plotReg.R, which also produced Figure 8.

Download the R code for this graph.

04_regression_coefficients.txt · Last modified: 2013/12/15 15:20 (external edit)