6<\/p>\n
Human Resource Management Project<\/p>\n
This summary aims to summarize the three main steps of the analysis of the data sample of Wolfpack Widgets, collected in the engagement survey. The survey was used to determine if the engagement characteristics might work as determinants of employee behaviors. The analysis started from the evaluation of the inter-correlations among engagement measures, such as affective commitment, engagement, social integration, and burnout. The analysis revealed that affirmative commitment positively and strongly associated with engagement (r = .70) and social integration (r = .75), and negatively and weakly associated with burnout (r = -.36). There is a moderate positive association between social integration and engagement (r = .54), and negative moderate association between social integration and burnout (r = -.42). Finally, social integration is negatively and moderately associated with burnout (r = -.40). These findings show that employees, who have higher affirmative commitment, usually are more engaged, and more socially integrated. Such employees have lower burnout rates. On the other hand, employees with high level of burnout are less affirmatively committed, less engaged, and less socially integrated.<\/p>\n
The next step of the analysis was to explore how predictors (engagement measures) and outcomes (employee behaviors) are related to each other. The correlations can be interpreted as in the step above \u2013 they are provided in the appendix. In addition, eight scatter plots were created for four outcome variables. Among the relationships between outcome and predictor variables, retention is mostly correlated with engage (r = .46) and the weakest correlation was with social integration (r = .28). Affirmative commitment after intervention was mostly correlated with engagement (r = .69) and the weakest correlation was with initial burnout (r = -.39). Burnout after intervention was mostly correlated with initial burnout (r = .67) and least correlated with initial affirmative commitment (r = -.33). Finally, the highest correlation with turnover was observed in initial affirmative commitment (r = -.16), and least correlated with initial social integration (r = -.0003).<\/p>\n
The final part of the analysis was conducting a multiple linear regression analysis to see whether the set of four independent variables can significantly predict outcomes. Four regression models were created \u2013 one per each outcome variable. The model created for retention showed that the coefficients of the equation are jointly significant, F = 9.14, p < .001. The linear combination of four predictors explain about 20.16% of the variability in retention. However, only initial engagement appeared to be an important determinant of retention (t = 3.495, p < .001). The second model was aimed to predict affirmative commitment after intervention. The model was significant overall, F = 48.21, p < .001. The model explains about 52.33% of the variance in affirmative commitment after intervention. Such factors as initial engagement (t = 7.00, p < .001) and social integration (t = 3.537, p < .001) were important determinants of affirmative commitment. The burnout regression was significant, F = 8.379, p < .001. The model explains only 14.65% of the variance in burnout after intervention. At the 5% level of significance, only engagement at baseline was an individually significant predictor (t = -2.241, p = .027). Finally, a binary logistic regression analysis was performed in R to estimate the factors that are important in predicting turnover. The analysis showed that only initial affirmative commitment is an important determinant of turnover (z = -2.142, p = .032), with higher commitment associated with lower chance of turnover.<\/p>\n
Appendix<\/p>\n
Step 1.<\/p>\n
\u00a0<\/p>\n
affcom.t1<\/p>\n
engage.t1<\/p>\n
soc.int.t1<\/p>\n
burnout.t1<\/p>\n
affcom.t1<\/p>\n
1.00<\/p>\n
engage.t1<\/p>\n
0.70<\/p>\n
1.00<\/p>\n
soc.int.t1<\/p>\n
0.75<\/p>\n
0.54<\/p>\n
1.00<\/p>\n
burnout.t1<\/p>\n
-0.36<\/p>\n
-0.42<\/p>\n
-0.40<\/p>\n
1.00<\/p>\n
Step 2.<\/p>\n
\u00a0<\/p>\n
affcom.t1<\/p>\n
engage.t1<\/p>\n
soc.int.t1<\/p>\n
burnout.t1<\/p>\n
retention.t2<\/p>\n
affcom.t2<\/p>\n
burnout.t2<\/p>\n
turnover<\/p>\n
affcom.t1<\/p>\n
1.00<\/p>\n
engage.t1<\/p>\n
0.70<\/p>\n
1.00<\/p>\n
soc.int.t1<\/p>\n
0.75<\/p>\n
0.54<\/p>\n
1.00<\/p>\n
burnout.t1<\/p>\n
-0.36<\/p>\n
-0.42<\/p>\n
-0.40<\/p>\n
1.00<\/p>\n
retention.t2<\/p>\n
0.35<\/p>\n
0.46<\/p>\n
0.28<\/p>\n
-0.28<\/p>\n
1.00<\/p>\n
affcom.t2<\/p>\n
0.67<\/p>\n
0.69<\/p>\n
0.57<\/p>\n
-0.39<\/p>\n
0.50<\/p>\n
1.00<\/p>\n
burnout.t2<\/p>\n
-0.33<\/p>\n
-0.36<\/p>\n
-0.35<\/p>\n
0.67<\/p>\n
-0.15<\/p>\n
-0.37<\/p>\n
1.00<\/p>\n
turnover<\/p>\n
-0.16<\/p>\n
-0.11<\/p>\n
0.00<\/p>\n
0.04<\/p>\n
-0.36<\/p>\n
-0.16<\/p>\n
-0.05<\/p>\n
1.00<\/p>\n
Step 3<\/p>\n
Retention<\/p>\n
Regression Statistic<\/p>\n
Multiple R<\/p>\n
0.475785<\/p>\n
R-squared<\/p>\n
0.226372<\/p>\n
Adjusted R-squared<\/p>\n
0.201615<\/p>\n
Standard error<\/p>\n
0.772421<\/p>\n
Observations<\/p>\n
130<\/p>\n
ANOVA<\/p>\n
\u00a0<\/p>\n
df<\/p>\n
SS<\/p>\n
MS<\/p>\n
F<\/p>\n
Significance F<\/p>\n
Regression<\/p>\n
4<\/p>\n
21.82265<\/p>\n
5.455663<\/p>\n
9.144068<\/p>\n
1.64E-06<\/p>\n
Residual<\/p>\n
125<\/p>\n
74.57927<\/p>\n
0.596634<\/p>\n
Total<\/p>\n
129<\/p>\n
96.40192<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
Coefficients<\/p>\n
Standard error<\/p>\n
t-statistic<\/p>\n
P-Value<\/p>\n
Lower 95%<\/p>\n
Upper 95%<\/p>\n
Y-intercept<\/p>\n
2.240345<\/p>\n
0.734919<\/p>\n
3.048423<\/p>\n
0.002808<\/p>\n
0.785848<\/p>\n
3.694841<\/p>\n
affcom.t1<\/p>\n
0.037339<\/p>\n
0.154613<\/p>\n
0.2415<\/p>\n
0.809564<\/p>\n
-0.26866<\/p>\n
0.343337<\/p>\n
engage.t1<\/p>\n
0.489201<\/p>\n
0.139961<\/p>\n
3.495256<\/p>\n
0.000656<\/p>\n
0.2122<\/p>\n
0.766202<\/p>\n
soc.int.t1<\/p>\n
-0.00787<\/p>\n
0.187364<\/p>\n
-0.04202<\/p>\n
0.966548<\/p>\n
-0.37869<\/p>\n
0.362943<\/p>\n
burnout.t1<\/p>\n
-0.10028<\/p>\n
0.083589<\/p>\n
-1.19966<\/p>\n
0.232541<\/p>\n
-0.26571<\/p>\n
0.065155<\/p>\n
Affirmative Commitment<\/p>\n
Regression Statistic<\/p>\n
Multiple R<\/p>\n
0.731045<\/p>\n
R-squared<\/p>\n
0.534427<\/p>\n
Adjusted R-squared<\/p>\n
0.523342<\/p>\n
Standard error<\/p>\n
0.546977<\/p>\n
Observations<\/p>\n
130<\/p>\n
ANOVA<\/p>\n
\u00a0<\/p>\n
df<\/p>\n
SS<\/p>\n
MS<\/p>\n
F<\/p>\n
Significance F<\/p>\n
Regression<\/p>\n
3<\/p>\n
43.27213<\/p>\n
14.42404<\/p>\n
48.21139<\/p>\n
8.03E-21<\/p>\n
Residual<\/p>\n
126<\/p>\n
37.6971<\/p>\n
0.299183<\/p>\n
Total<\/p>\n
129<\/p>\n
80.96923<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
Coefficients<\/p>\n
Standard error<\/p>\n
t-statistic<\/p>\n
P-Value<\/p>\n
Lower 95%<\/p>\n
Upper 95%<\/p>\n
Y-intercept<\/p>\n
0.225866<\/p>\n
0.505497<\/p>\n
0.446819<\/p>\n
0.655773<\/p>\n
-0.7745<\/p>\n
1.226229<\/p>\n
engage.t1<\/p>\n
0.588308<\/p>\n
0.08409<\/p>\n
6.996189<\/p>\n
1.38E-10<\/p>\n
0.421897<\/p>\n
0.754719<\/p>\n
soc.int.t1<\/p>\n
0.367822<\/p>\n
0.104001<\/p>\n
3.536712<\/p>\n
0.000568<\/p>\n
0.162007<\/p>\n
0.573637<\/p>\n
burnout.t1<\/p>\n
-0.05197<\/p>\n
0.059114<\/p>\n
-0.87915<\/p>\n
0.380994<\/p>\n
-0.16895<\/p>\n
0.065014<\/p>\n
Burnout<\/p>\n
Regression Statistic<\/p>\n
Multiple R<\/p>\n
0.407828<\/p>\n
R-squared<\/p>\n
0.166324<\/p>\n
Adjusted R-squared<\/p>\n
0.146475<\/p>\n
Standard error<\/p>\n
0.889081<\/p>\n
Observations<\/p>\n
130<\/p>\n
ANOVA<\/p>\n
\u00a0<\/p>\n
df<\/p>\n
SS<\/p>\n
MS<\/p>\n
F<\/p>\n
Significance F<\/p>\n
Regression<\/p>\n
3<\/p>\n
19.87061<\/p>\n
6.623536<\/p>\n
8.379288<\/p>\n
4.02E-05<\/p>\n
Residual<\/p>\n
126<\/p>\n
99.59862<\/p>\n
0.790465<\/p>\n
Total<\/p>\n
129<\/p>\n
119.4692<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
Coefficients<\/p>\n
Standard error<\/p>\n
t-statistic<\/p>\n
P-Value<\/p>\n
Lower 95%<\/p>\n
Upper 95%<\/p>\n
Y-intercept<\/p>\n
5.468385<\/p>\n
0.631395<\/p>\n
8.660796<\/p>\n
1.91E-14<\/p>\n
4.218872<\/p>\n
6.717898<\/p>\n
affcom.t1<\/p>\n
0.035581<\/p>\n
0.177729<\/p>\n
0.200196<\/p>\n
0.84165<\/p>\n
-0.31614<\/p>\n
0.3873<\/p>\n
engage.t1<\/p>\n
-0.34971<\/p>\n
0.156074<\/p>\n
-2.24069<\/p>\n
0.026798<\/p>\n
-0.65858<\/p>\n
-0.04085<\/p>\n
soc.int.t1<\/p>\n
-0.40107<\/p>\n
0.210673<\/p>\n
-1.90378<\/p>\n
0.059219<\/p>\n
-0.81799<\/p>\n
0.01584<\/p>\n","protected":false},"excerpt":{"rendered":"
6 Human Resource Management Project This summary aims to summarize the three main steps of the analysis of the data sample of Wolfpack Widgets, collected in the engagement survey. The survey was used to determine if the engagement characteristics might work as determinants of employee behaviors. The analysis started from the evaluation of the inter-correlations […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[10],"class_list":["post-96476","post","type-post","status-publish","format-standard","hentry","category-research-paper-writing","tag-writing"],"_links":{"self":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts\/96476","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/comments?post=96476"}],"version-history":[{"count":0,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts\/96476\/revisions"}],"wp:attachment":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/media?parent=96476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/categories?post=96476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/tags?post=96476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}