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Visual Representation and the Prediction of Emotion
Unformatted Document Text:  18 separated into dummy variables. This number of independent variables complicated the analysis to a significant degree. Therefore, in order to limit the number of variables, we used a method of analysis that involved doing separate regressions for each of the dimensions of interest (demographic characteristics, negativity, deviance, and intimacy) and using the significant variables in each of those regressions in a final regression. Because the variables had been pre- selected for their level of significance, they were held to a far more conservative test of significance in the final analysis. The significance test will be explained in detail in the section that follows. Because the two dependent variables, negative affect and positive affect, have opposite valences, making sense out of the signs of coefficients can be complicated. For ease of interpretation, the dependent variable, “negative affect” was multiplied by –1. As a result, a negative coefficient for either negative or positive affect can be interpreted as the respondent experiencing more negative affect. Final Regressions We included all variables that were significant in the first four analyses as the independent variables. The significance test used the critical z score necessary for all of the combined variables in the equation to be significant as the criteria for determining whether each individual variable was significant. This was a far more conservative method than the more common t-test used in the original equations. For the regression on negative affect, variables needed to have a z-score of 3.0 or greater in order to be considered significant at the .05 level, a

Authors: Sherr, Susan.
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18
separated into dummy variables. This number of independent variables
complicated the analysis to a significant degree. Therefore, in order to limit the
number of variables, we used a method of analysis that involved doing separate
regressions for each of the dimensions of interest (demographic characteristics,
negativity, deviance, and intimacy) and using the significant variables in each of
those regressions in a final regression. Because the variables had been pre-
selected for their level of significance, they were held to a far more conservative
test of significance in the final analysis. The significance test will be explained in
detail in the section that follows.
Because the two dependent variables, negative affect and positive affect,
have opposite valences, making sense out of the signs of coefficients can be
complicated. For ease of interpretation, the dependent variable, “negative affect”
was multiplied by –1. As a result, a negative coefficient for either negative or
positive affect can be interpreted as the respondent experiencing more negative
affect.
Final Regressions
We included all variables that were significant in the first four analyses as
the independent variables. The significance test used the critical z score
necessary for all of the combined variables in the equation to be significant as the
criteria for determining whether each individual variable was significant. This
was a far more conservative method than the more common t-test used in the
original equations. For the regression on negative affect, variables needed to have
a z-score of 3.0 or greater in order to be considered significant at the .05 level, a


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