Results

Classical Process Model

Model summary
	AIC	BIC	Log-likelihood	n	df
Model 1	1344.045	1364.903	-666.022	239	0

R-squared
		R²
		Model 1
hook_ups		0.140
commit		0.020

Path plots

Model 1

Statistical 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

The output below shows the results of the linear model that predicts the number of hook-ups from both pornography consumption and commitment. We can see that pornography consumption significantly predicts number of hook-ups even with relationship commitment in the model, b = 1.28, z = 3.069, p = 0.02; relationship commitment also significantly predicts number of hook-ups, b = −0.62, z = −4.934, p < .001. The 𝑅² value tells us that the model explains 14.0% of the variance in number of hook-ups. The negative b for commitment tells us that as commitment increases, number of hook-ups declines (and vice versa), but the positive b for consumptions indicates that as pornography consumption increases, the number of hook-ups increases also. These relationships are in the predicted direction.

The last line in the output below shows us the results of the linear model that predicts commitment from pornography consumption. Pornography consumption significantly predicts relationship commitment, b = -0.47, z = -2.215, p = 0.027. The 𝑅² value tells us that pornography consumption explains 2% of the variance in relationship commitment, and the fact that the b is negative tells us that the relationship is negative also: as consumption increases, commitment declines (and vice versa).

Path coefficients
							95% Confidence Interval
			Estimate	Std. Error	z-value	p	Lower	Upper	Std. Estimate
ln_porn	→	hook_ups	1.281	0.417	3.069	0.002	0.463	2.099	0.186
commit	→	hook_ups	-0.622	0.126	-4.934	< .001	-0.869	-0.375	-0.299
ln_porn	→	commit	-0.470	0.212	-2.215	0.027	-0.885	-0.054	-0.142

The next part of the output (Direct and indirect effects table below) is the most important because it displays the results for the indirect effect of pornography consumption on number of hook-ups (i.e. the effect via relationship commitment). We’re told the effect of pornography consumption on the number of hook-ups when relationship commitment is included as a predictor as well (the direct effect). The first bit of new information is the Indirect effects, which in this case is the indirect effect of pornography consumption on the number of hook-ups. We’re given an estimate of this effect (b = 0.292) as well as a standard error and confidence interval. As we have seen many times before, 95% confidence intervals contain the true value of a parameter in 95% of samples. Assuming our sample is one of the 95% that ‘hits’ the true value, we can infer that the true b-value for the indirect effect falls between 0.009 and 0.575. This range does not include zero, and remember that b = 0 would mean ‘no effect whatsoever’; therefore, the fact that the confidence interval does not contain zero means that there is likely to be a genuine indirect effect. The standardized effect is 𝑎𝑏_CS= 0.042. Put another way, relationship commitment is a mediator of the relationship between pornography consumption and the number of hook-ups.

Direct and indirect effects
									95% Confidence Interval
					Estimate	Std. Error	z-value	p	Lower	Upper	Std. Estimate
ln_porn	→	hook_ups			1.281	0.417	3.069	0.002	0.463	2.099	0.186
ln_porn	→	commit	→	hook_ups	0.292	0.145	2.020	0.043	0.009	0.575	0.042

The last part of the output (Total effects table below) shows the total effect of pornography consumption on number of hook-ups (outcome). When relationship commitment is not in the model, pornography consumption significantly predicts the number of hook-ups, b = 1.573, z = 3.627, p = < .001. As is the case when we include relationship commitment in the model, pornography consumption has a positive relationship with number of hook-ups (as shown by the positive b-value).

Total effects
								95% Confidence Interval
				Estimate	Std. Error	z-value	p	Lower	Upper	Std. Estimate
Total	ln_porn	→	hook_ups	1.573	0.434	3.627	< .001	0.723	2.423	0.228
Total indirect	ln_porn	→	hook_ups	0.292	0.145	2.020	0.043	0.009	0.575	0.042