Results

Classical Process Model

Model summary
	AIC	BIC	Log-likelihood	n	df
Model 1	1619.456	1640.340	-803.728	240	0

R-squared
		R²
		Model 1
desirability		0.110
product_cool		0.025

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 table below shows the results of the linear model predicting desirability from how cool people perceived both the advertising and product to be. Cool advertising significantly predicts product desirability even with product_cool in the model, b = 0.20, z = 3.364, p < .001; product_cool also significantly predicts product desirability, b = 0.23, z = 3.707, p < .001. The 𝑅² values tells us that the model explains 11% of the variance in product desirability. The positive b's for product_cool and advert_cool tell us that as adverts and products increase in how cool they are perceived to be, product desirability increases also (and vice versa). These relationships are in the predicted direction.

The last line of the table shows us the results of the linear model that predicts the perceived ‘coolness’ of the product from the perceived ‘coolness’ of the advertising. We can see that how cool people perceive the advertising to be significantly predicts how cool they think the product is, b = 0.152, z = 2.5, p = .012. The 𝑅² value tells us that cool advertising explains 2.5% of the variance in how cool they think the product is, and the fact that the b is positive tells us that the relationship is positive also: the more ‘cool’ people think the advertising is, the more ‘cool’ they think the product is (and vice versa).

Path coefficients
							95% Confidence Interval
			Estimate	Std. Error	z-value	p	Lower	Upper	Std. Estimate
advert_cool	→	desirability	0.200	0.060	3.364	< .001	0.084	0.317	0.207
product_cool	→	desirability	0.232	0.063	3.707	< .001	0.109	0.354	0.229
advert_cool	→	product_cool	0.152	0.061	2.500	0.012	0.033	0.271	0.159

The next part of the output is the most important because it displays the results for the indirect effect of cool advertising on product desirability (i.e. the effect via product_cool). First, we’re again told the effect of cool advertising on the product desirability in isolation (the total effect). Next, we’re told the effect of cool advertising on the product desirability when product_cool is included as a predictor as well (the direct effect). The first bit of new information is in the second row of the table, which in this case is the indirect effect of cool advertising on the product desirability. We’re given an estimate of this effect (b = 0.035) 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 0.002 and 0.068. 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= 0.036. Put another way, product_cool is a mediator of the relationship between cool advertising and product desirability.

Direct and indirect effects
									95% Confidence Interval
					Estimate	Std. Error	z-value	p	Lower	Upper	Std. Estimate
advert_cool	→	desirability			0.200	0.060	3.364	< .001	0.084	0.317	0.207
advert_cool	→	product_cool	→	desirability	0.035	0.017	2.073	0.038	0.002	0.068	0.036

The next part of the output shows the total effect of cool advertising on product desirability (outcome). The total effect is the effect of the predictor on the outcome when the mediator is not present in the model. When product_cool is not in the model, cool advertising significantly predicts product desirability, b = .235, z = 3.896, p < .001. As is the case when we include product_cool in the model, advert_cool has a positive relationship with product desirability (as shown by the positive b-value).

Total effects
								95% Confidence Interval
				Estimate	Std. Error	z-value	p	Lower	Upper	Std. Estimate
Total	advert_cool	→	desirability	0.235	0.060	3.896	< .001	0.117	0.354	0.244
Total indirect	advert_cool	→	desirability	0.035	0.017	2.073	0.038	0.002	0.068	0.036