. 2015 Jul 31;86(5):1489–1506. doi: 10.1111/cdev.12396

Table 3.

Study 1 Multiple Regression Models Predicting Resource Allocation Across Networks

Independent variable	Unstandardized regression coefficient	p value
Study 1 MG‐MRQAP model: R ² _adj = .255, p < .001
Year 9 intercept	2.58	< .001
Year 12 intercept (reference Year 9)	−0.23	.552
Relationship strength	4.82	< .001^a
Reciprocation	−0.17	.820
Reciprocation × Year group	2.64	.040^a
Gender giving	−1.18	.003^a

Multigroup multiple regression with quadratic assignment procedure (MG‐MRQAP) was implemented to predict modified Dictator Game point allocation based on individual difference and network variables and terms specifying their respective interactions with year group (Year 9 and Year 12). Independent variables eliminated from the model were Gender Receiving × Year Group (b = −.02, p = .493), Gender Giving × Year Group (b = −.41, p = .354), Duration of Acquaintance × Year Group (b = −1.17, p = .134), duration of acquaintance (b = −.08, p = .078), gender receiving (b = .29, p = .909), Mach Giving × Group (b = −.09, p = .071), Mach giving (b = −.04, p = .105), and Relationship Strength × Year Group (b = 1.83, p = .062). Note that for the multigroup models in the current study, it is not appropriate to report standardized regression coefficients. In order to investigate interactions, the Year 12 intercept and reciprocation terms were retained. That is, the procedure for eliminating variables was to exclude the variable with the largest p value, unless this belonged to a main effect for which the interaction term was still in the model. In this case, exclusion of main effects took place after exclusion of interaction terms.

^aDenotes a one‐tailed p value; all other p values are two‐tailed.