Results

Classical Process Model -- 10.1

Model summary
  AIC BIC Log-likelihood n df
Model 1 981.151 1012.149 -480.575 164 0
R-squared
R²
  Model 1
support 0.085

Path plots

Model 1

Conceptual path plot

Parameter estimates

Important : Parameter estimates can only be interpreted as causal effects if all confounding effects are accounted for and if the causal effect directions are correctly specified.

Model 1

This part of the output contains the main moderation analysis. Moderation is shown up by a significant interaction effect, and in this case the interaction is highly significant, in the attractiveness: spouseWife b = 0.105, 95% CI [0.051, 0.159], z = 3.8, p < 0.001, indicating that the relationship between attractiveness and support is moderated by spouse:

Path coefficients
95% Confidence Interval
      Estimate Std. Error z-value p Lower Upper
attractiveness → support -0.060 0.020 -3.011 0.003 -0.099 -0.021
spouseWife → support -0.442 0.126 -3.507 < .001 -0.689 -0.195
attractiveness:spouseWife → support 0.105 0.028 3.816 < .001 0.051 0.159
Direct and indirect effects
95% Confidence Interval
      spouseWife Estimate Std. Error z-value p Lower Upper
attractiveness → support 0 -0.060 0.020 -3.011 0.003 -0.099 -0.021
attractiveness → support 1 0.046 0.019 2.375 0.018 0.008 0.083

To interpret the moderation effect we can examine the simple slopes, which are shown in the next part of the output. Essentially, the output shows the results of two different regressions: the regression for attractiveness as a predictor of support (1) when the value for `spouse` equals "Wife" (and the dummy variable that JASP uses, `spouseWife` equals 1) or "Husband" (and the dummy variable that JASP uses, `spouseWife` equals 0). We can interpret these regressions as we would any other: we’re interested the value of b (called Estimate in the output), and its significance. From what we have already learnt about regression we can interpret the two models as follows:

  1. For husbands (spouseWife = 0), there is a significant negative relationship between attractiveness and support, b = -0.060, 95% CI [-0.100, -0.021], z = -3.011, p = 0.003.
  2. for wives (spouseWife = 1), there is a significant positive relationship between attractiveness and support, b = 0.046, 95% CI [0.008, 0.083], z = 2.375, p = 0.018.

These results tell us that the relationship between attractiveness of a person and amount of support given to their spouse is different for husbands and wives. Specifically, for wives, as attractiveness increases the level of support that they give to their husbands increases, whereas for husbands, as attractiveness increases the amount of support they give to their wives decreases.


Total effects
95% Confidence Interval
        spouseWife Estimate Std. Error z-value p Lower Upper
Total attractiveness → support 0 -0.060 0.020 -3.011 0.003 -0.099 -0.021
  attractiveness → support 1 0.046 0.019 2.375 0.018 0.008 0.083

Descriptive Statistics -- 10.2

Descriptive Statistics
attractiveness
support
  Husband Wife Husband Wife
Valid 82 82 82 82
Missing 0 0 0 0
Mean 4.433 4.415 0.221 0.245
Std. Deviation 1.119 1.161 0.216 0.204
Minimum 1.790 1.790 -0.310 -0.290
Maximum 7.120 6.870 0.900 0.820

Scatter Plots

To create a plot of an interaction effect, you can either use Descriptive Statistics in the Descriptives module (Scatter plots with a Split variable when you have a categorical moderator) or use Flexplot in the Descriptives module (when you have a continuous moderator). Since Spouse is a catgorical moderator, we use the Scatter plot with a split variable here.


The resulting plot confirms our results from the analysis in the previous task. The direction of the relationship between attractiveness and support is different for husbands and wives: the two regression lines slope in different directions. Specifically, for husbands (green line) the relationship is negative (the regression line slopes downwards), whereas for wives (grey line) the relationship is positive (the regression line slopes upwards). Additionally, the fact that the lines cross indicates a significant interaction effect (moderation). So basically, we can conclude that the relationship between attractiveness and support is positive for wives (more attractive wives give their husbands more support), but negative for husbands (more attractive husbands give their wives less support than unattractive ones). Although they didn’t test moderation, this mimics the findings of McNulty et al. (2008).

attractiveness - support

Classical Process Model -- 10.3

Model summary
  AIC BIC Log-likelihood n df
Model 1 1993.988 2024.987 -986.994 164 0

Path plots

Model 1

Conceptual path plot

Parameter estimates

Important : Parameter estimates can only be interpreted as causal effects if all confounding effects are accounted for and if the causal effect directions are correctly specified.

Model 1

Moderation is shown up by a significant interaction effect, and in this case the interaction is not significant, b = 0.547, 95% CI [-0.64, 1.73], z = 0.903, p = 0.366, indicating that the relationship between attractiveness and relationship satisfaction is not significantly moderated by spouse (i.e. the relationship between attractiveness and relationship satisfaction is not significantly different for husbands and wives)

Path coefficients
95% Confidence Interval
      Estimate Std. Error z-value p Lower Upper
attractiveness → satisfaction -0.884 0.436 -2.028 0.043 -1.738 -0.030
spouseWife → satisfaction -2.442 2.764 -0.883 0.377 -7.860 2.976
attractiveness:spouseWife → satisfaction 0.547 0.605 0.903 0.366 -0.640 1.733
Direct and indirect effects
95% Confidence Interval
      spouseWife Estimate Std. Error z-value p Lower Upper
attractiveness → satisfaction 0 -0.884 0.436 -2.028 0.043 -1.738 -0.030
attractiveness → satisfaction 1 -0.337 0.420 -0.802 0.422 -1.160 0.486
Total effects
95% Confidence Interval
        spouseWife Estimate Std. Error z-value p Lower Upper
Total attractiveness → satisfaction 0 -0.884 0.436 -2.028 0.043 -1.738 -0.030
  attractiveness → satisfaction 1 -0.337 0.420 -0.802 0.422 -1.160 0.486