**3. Results**

#### *3.1. Demographics and Life Satisfaction*

None of the demographic variables examined (age, sex, education, and employee status) had a significant association with life satisfaction. Age had a correlation of −0.082, *p* = 0.380. The analysis of variance on sex yielded an *F* < 1.0; education, *F* = 1.21, *p* = 0.309; and employee status, *F* = 1.28, *p* = 0.275 in all cases.

## *3.2. Preliminary Analyses*

Given that the sample consisted of 78 instructional faculty, four administrators, and 35 staff members, an initial analysis was conducted to see whether the data could be combined across these groups for various analyses. Additionally, because there were only four administrators, their data were combined with 35 staff members to form one group (administrators and staff). A multivariate analysis on the eight variables (life satisfaction, work discrimination, negative stereotypes, climate—PIA, climate—WGI, physical engagement, emotional engagement, and cognitive engagement) revealed that the mean score for the two groups did not differ significantly (Mult *F*(8, 107) < 1.0) on these variables. A further

univariate analysis also showed that the mean scores did not differ significantly on any of the variables and all effect sizes (η2) lower than 0.024 for all variables. Thus, the rest of the analyses were performed on all participants.

#### *3.3. Correlations among Variables*

Table 1 provides Pearson correlation coefficients between each of the job-related variables and life satisfaction and also among the job-related variables. As expected, (a) experiencing discrimination in the workplace had a negative significant correlation with life satisfaction (*r* = −0.205, *p* < 0.028), indicating that those who reported higher levels of discrimination at work were more likely to report lower levels of life satisfaction, (b) agreeing more strongly that negative stereotypes exist at the workplace correlated significantly and negatively with life satisfaction (*r* = −0.259, *p* < 0.008), indicating that those who reported a lower prevalence of negative stereotypes were more likely to report higher levels of life satisfaction, (c) contrary to expectations, neither of the climate variables nor the total climate score correlated with life satisfaction, and (d) neither physical nor cognitive engagemen<sup>t</sup> had significant correlations with life satisfaction, but emotional engagemen<sup>t</sup> had significant positive correlation (*r* = 0.249, *p* < 0.008).


**Table 1.** Correlations among the variables (numbers vary between 115 and 117).

\* *p* < 0.028; \*\* *p* < 0.008; diagonal values = Cronbach α; PIA = positive intergenerational affect, WGI = workplace generational inclusiveness.

An examination of Table 1 suggests that Work Discrimination and Negative Stereotypes had a significant strong positive correlation (*r* = 0.543, *p* < 0.008). Furthermore, the two climate variables had strong significant positive correlations with each other (*r* = 0.594, *p* < 0.008) and the three job engagemen<sup>t</sup> scales had strong significant positive correlations with each other (0.673 to 0.830).

Given these strong correlations among the predictor scales, we conducted a Principal Axis Factor Analysis (PFA) with Promax rotation (kappa = 4) to allow for correlated factors and to reduce the number of variables to avoid issues of multicollinearity among the predictor variables in a hierarchical multiple regression analysis. The Promax rotation yields two matrices: pattern and structure, and the former matrix is most often interpreted. The PFA yielded three factors, which accounted for 82.420% of the total variance, with eigenvalues of 2.604 (37.196%), 2.086 (29.508%), and 1.100 (15.716%), respectively. The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.63, suggesting adequate sampling.

Table 2 provides a summary of the PAF analysis rotated pattern matrix. The results suggested three factors to underlie the seven variables: Job Engagement (combination of physical, emotional, and cognitive aspects), Experience of Ageism (combination of work discrimination and negative stereotypes), and Work Intergeneration Climate (combination of the two climate variables).


**Table 2.** Rotated pattern matrix loading of the predictor variables.

Regression-based factor scores were then computed for each of the factors and correlated with Life Satisfaction, which were as follows: Job Engagement *r* = 0.057 (*p* = 0.544), Experience of Ageism = *r* = −0.245 (*p* = 0.008), and Work Intergenerational Climate *r* = 0.070 (*p* = 457), respectively. Factors 1 and 2 correlated = −0.147, *p* = 0.118, factors 1 and 3 correlated = 0.066, *p* = 0.482, and factors 2 and 3 correlated = −0.649, *p* = 0.000, suggesting that higher levels of ageist experiences and attitudes are significantly associated with lower levels of expressed positive intergenerational affect and inclusiveness climate.

#### *3.4. Regression Analysis: Predicting Life Satisfaction*

A hierarchical regression analysis was conducted to examine the additional variance accounted for in the prediction of Life Satisfaction by the job-related factor variables Ageism, Work Intergenerational Climate, and Job Engagement, beyond that accounted for by the demographic variables of age, sex, and education. The regression analysis permitted examining the relative variance accounted for by each of the job-related variables. There was no evidence of heteroskedasticity in either model; however, as per Hayes and Cai's [40] recommendation, we employed robust standard errors (HC3) that do not assume heteroskedasticity.

The three demographic (Sex, Age, Education) variables were included in the first step, and three job-related variables (Ageism, Work Intergenerational Climate, and Job Engagement) were included in the second step.

Per Table 3, prediction with demographic variables alone (Model 1) was not significant (*R*<sup>2</sup> = 0.023, *p* = 0.458); they collectively accounted for about 2.3% of the variance, but when the factor-based variables were added in the second step (Model 2), an additional 6.4% of the variance ( *R*<sup>2</sup> = 0.087, *p* = 0.060) was accounted for. Table 3 shows the results of the regression analysis for predicting life satisfaction in Models 1 and 2.

**Table 3.** R and R<sup>2</sup> change from Models 1 and 2.


Model 1: Demographic Variables: age, sex, Education; Model 2: Job-Related Factor-Based Variables. Adjusted R<sup>2</sup> for models 1 and 2 were 0.003 and 0.037, respectively.

Per Table 4, in Model 2, only Ageism had a significant beta weight (*b* = −2.386, *t* = −2.545, *p* = 0.0120, suggesting that ageism is a significant predictor of life satisfaction with other variables partialled out; the negative regression coefficient indicates that people who experienced a higher degree of ageism expressed lower levels of life satisfaction. The tolerance values ranged between 0.519 and 0.985, all greater than 0.10, suggesting acceptable levels for the absence of multicollinearity.


**Table 4.** Regression analysis: *t*-test based on robust standard errors (SE).

\* Female = 1, Male = 2.
