Items
| Q | Item | Item name |
|---|---|---|
| Q1 | I am given a real opportunity to improve my skills in my organization | mut_1 |
| Q2 | I feel encouraged to come up with new and better ways of doing things | cvo_4 |
| Q3 | My work gives me a feeling of personal accomplishment. | |
| Q4 | I know what is expected of me on the job. | |
| Q5 | My workload is reasonable. | |
| Q6 | My talents are used well in the workplace. | cvo_5 |
| Q7 | I know how my work relates to the agency’s goals. | proh_2 |
| Q8 | I can disclose a suspected violation of any law, rule or regulation without fear of reprisal. | proh_3 |
| Q9 | I have enough informationXS to do my job well. | |
| Q10 | I receive the training I need to do my job well. | |
| Q11 | I am held accountable for the quality of work I produce. | |
| Q12 | Continually changing work priorities make it hard for me to produce high quality work. | |
| Q13 | I have a clear idea of how well I am doing my job. | |
| Q14 | The people I work with cooperate to get the job done. | |
| Q16 | In my work unit, differences in performance are recognized in a meaningful way. | |
| Q17 | Employees in my work unit share job knowledge. | |
| Q18 | My work unit has the job-relevant knowledge and skills necessary to accomplish organizational goals. | |
| Q19 | Employees in my work unit meet the needs of our customers. | |
| Q20 | Employees in my work unit contribute positively to my agency’s performance. | |
| Q21 | Employees in my work unit produce high-quality work. | |
| Q22 | Employees in my work unit adapt to changing priorities. | |
| Q23 | New hires in my work unit (i.e. hired in the past year) have the right skills to do their jobs. | |
| Q24 | I can influence decisions in my work unit. | cvo_1 |
| Q25 | I know what my work unit’s goals are. | |
| Q26 | My work unit commits resources to develop new ideas (e.g., budget, staff, time, expert support). | |
| Q27 | My work unit successfully manages disruptions to our work. | |
| Q28 | Employees in my work unit consistently look for new ways to improve how they do their work. | |
| Q29 | Employees in my work unit incorporate new ideas into their work. | |
| Q30 | Employees in my work unit approach change as an opportunity. | |
| Q31 | Employees in my work unit consider customer needs a top priority. | |
| Q32 | Employees in my work unit consistently look for ways to improve customer service. | |
| Q33 | Employees in my work unit support my need to balance my work and personal responsibilities. | |
| Q34 | Employees in my work unit are typically under too much pressure to meet work goals. | |
| Q35 | Employees are recognized for providing high quality products and services. | cvo_2 |
| Q36 | Employees are protected from health and safety hazards on the job. | |
| Q37 | My organization is successful at accomplishing its mission. | |
| Q38 | I have a good understanding of my organization’s priorities. | |
| Q39 | My organization effectively adapts to changing government priorities. | |
| Q40 | My organization has prepared me for potential physical security threats. | |
| Q41 | My organization has prepared me for potential cybersecurity threats. | |
| Q42 | In my organization, arbitrary action, personal favoritism and/or political coercion are not tolerated. | exp_4 |
| Q43 | I recommend my organization as a good place to work. | exp_1 |
| Q44 | I believe the results of this survey will be used to make my agency a better place to work. | |
| Q45 | My supervisor is committed to a workforce representative of all segments of society. | |
| Q46 | Supervisors in my work unit support employee development. | lvo_2 |
| Q47 | My supervisor supports my need to balance work and other life issues. | |
| Q48 | My supervisor listens to what I have to say. | |
| Q49 | My supervisor treats me with respect. | |
| Q50 | I have trust and confidence in my supervisor. | |
| Q51 | My supervisor holds me accountable for achieving results. | |
| Q52 | Overall, how good a job do you feel is being done by your immediate supervisor? | |
| Q53 | My supervisor provides me with constructive suggestions to improve my job performance. | lvo_1 |
| Q54 | My supervisor provides me with performance feedback throughout the year. | |
| Q55 | In my organization, senior leaders generate high levels of motivation and commitment in the workforce. | |
| Q56 | My organization’s senior leaders maintain high standards of honesty and integrity. | |
| Q57 | Managers communicate the goals of the organization. | |
| Q58 | Managers promote communication among different work units (for example, about projects, goals, needed resources). | |
| Q59 | Overall, how good a job do you feel is being done by the manager directly above your immediate supervisor? | |
| Q60 | I have a high level of respect for my organization’s senior leaders. | exp_3 |
| Q61 | Senior leaders demonstrate support for Work-Life programs. | |
| Q62 | Management encourages innovation. | |
| Q63 | Management makes effective changes to address challenges facing our organization. | |
| Q64 | Management involves employees in decisions that affect their work. | |
| Q65 | How satisfied are you with your involvement in decisions that affect your work? | cvo_3 |
| Q66 | How satisfied are you with the information you receive from management on what’s going on in your organization? | |
| Q67 | How satisfied are you with the recognition you receive for doing a good job? | cgs_1 |
| Q68 | Considering everything, how satisfied are you with your job? | exp_2 |
| Q69 | Considering everything, how satisfied are you with your pay? | |
| Q70 | Considering everything, how satisfied are you with your organization? | |
| Q71 | My organization’s management practices promote diversity (e.g., outreach, recruitment, promotion opportunities). | |
| Q72 | My supervisor demonstrates a commitment to workforce diversity (e.g., recruitment, promotion opportunities, development). | |
| Q73 | I have similar access to advancement opportunities (e.g., promotion, career development, training) as others in my work unit. | mut_2 |
| Q74 | My supervisor provides opportunities fairly to all employees in my work unit (e.g., promotions, work assignments). | mut_3 |
| Q75 | In my work unit, excellent work is similarly recognized for all employees (e.g., awards, acknowledgements). | mut_4 |
| Q76 | Employees in my work unit treat me as a valued member of the team. | |
| Q77 | Employees in my work unit make me feel I belong. | belong_2 |
| Q78 | Employees in my work unit care about me as a person. | belong_1 |
| Q79 | I am comfortable expressing opinions that are different from other employees in my work unit. | proh_1 |
| Q80 | In my work unit, people’s differences are respected. | |
| Q81 | I can be successful in my organization being myself. | cgs_2 |
| Q82 | I can easily make a request of my organization to meet my accessibility needs. | |
| Q83 | My organization responds to my accessibility needs in a timely manner. | |
| Q84 | My organization meets my accessibility needs. | |
| Q85 | My job inspires me. | |
| Q86 | The work I do gives me a sense of accomplishment. | |
| Q87 | I feel a strong personal attachment to my organization. | |
| Q88 | I identify with the mission of my organization. | |
| Q89 | It is important to me that my work contribute to the common good. | |
| Q90 | What percentage of your work time are you currently required to be physically present at your agency worksite (including headquarters, bureau, field offices, etc.)? | |
| Q91 | Please select the response that BEST describes your current remote work or teleworking schedule. | |
| Q92 | Did you have an approved remote work agreement before the 2020 COVID-19 pandemic? | |
| Q93 | Based on your work unit’s current telework or remote work options, are you considering leaving your organization, and if so, why? | |
| Q94 | My agency’s re-entry arrangements are fair in accounting for employees’ diverse needs and situations. | |
| Q95 | Please select the response that BEST describes how employees in your work unit currently report to work: | |
| Q96 | My organization’s senior leaders support policies and procedures to protect employee health and safety. | |
| Q97 | My organization’s senior leaders provide effective communications about what to expect with the return to the physical worksite. | |
| Q98 | My supervisor supports my efforts to stay healthy and safe while working. | |
| Q99 | My supervisor creates an environment where I can voice my concerns about staying healthy and safe. |
Demographics Controls
| variabe_label | Text | Renamed |
|---|---|---|
| DRNO | Race | ethnicity |
| DHISP | Hispanic | hisp |
| DDIS. | Disability | disab |
| DAGEGRP. | Age Group | age |
| DSUPER | Supervisory status | supervisor |
| DFEDTEN. | Time with federal government | tenure |
| DSEX | Sex | sex |
| DMIL | Military service | military |
| DLEAVING | Considering leaving org? | leave |
Alphas
Mutability
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(mut_1, mut_2, mut_3, mut_4)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.86 0.66 7.9 0.00024 3.7 1 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.89 0.89
## Duhachek 0.89 0.89 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## mut_1 0.89 0.89 0.85 0.74 8.3 0.00025 0.0015 0.75
## mut_2 0.85 0.85 0.80 0.65 5.5 0.00036 0.0106 0.59
## mut_3 0.84 0.84 0.78 0.63 5.1 0.00038 0.0031 0.61
## mut_4 0.85 0.85 0.80 0.65 5.5 0.00036 0.0079 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## mut_1 554191 0.81 0.80 0.69 0.65 3.8 1.1
## mut_2 517875 0.88 0.88 0.83 0.78 3.7 1.2
## mut_3 507104 0.90 0.90 0.87 0.81 3.9 1.1
## mut_4 502077 0.88 0.88 0.83 0.78 3.6 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## mut_1 0.05 0.10 0.15 0.43 0.28 0.01
## mut_2 0.08 0.09 0.15 0.40 0.27 0.07
## mut_3 0.06 0.07 0.15 0.40 0.32 0.09
## mut_4 0.08 0.10 0.17 0.36 0.28 0.10Latent voice opportunity
##
## Pearson's product-moment correlation
##
## data: lvo_1 and lvo_2
## t = 690.58, df = 534637, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6852094 0.6880429
## sample estimates:
## cor
## 0.6866287Confidence in voice efficacy
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(cvo_1, cvo_2, cvo_3, cvo_4,
## cvo_5)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.86 0.6 7.4 0.00025 3.6 0.94 0.6
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.88 0.88
## Duhachek 0.88 0.88 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cvo_1 0.87 0.87 0.84 0.62 6.6 0.00029 0.0028 0.62
## cvo_2 0.87 0.87 0.84 0.62 6.6 0.00029 0.0027 0.62
## cvo_3 0.85 0.85 0.82 0.59 5.7 0.00033 0.0049 0.57
## cvo_4 0.84 0.84 0.80 0.57 5.3 0.00035 0.0021 0.57
## cvo_5 0.85 0.85 0.81 0.59 5.7 0.00032 0.0025 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cvo_1 556440 0.78 0.79 0.71 0.66 3.7 1.1
## cvo_2 533114 0.79 0.79 0.71 0.66 3.5 1.2
## cvo_3 534329 0.84 0.84 0.79 0.74 3.4 1.1
## cvo_4 548783 0.87 0.86 0.83 0.78 3.7 1.2
## cvo_5 546839 0.84 0.84 0.79 0.74 3.6 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## cvo_1 0.05 0.10 0.20 0.43 0.21 0.00
## cvo_2 0.08 0.13 0.18 0.43 0.18 0.04
## cvo_3 0.07 0.17 0.25 0.35 0.17 0.04
## cvo_4 0.06 0.12 0.16 0.38 0.28 0.02
## cvo_5 0.07 0.12 0.16 0.41 0.23 0.02Prohibitive voice
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(proh_1, proh_2, proh_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.69 0.61 0.43 2.3 0.00069 4 0.83 0.42
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.69 0.69
## Duhachek 0.69 0.69 0.69
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## proh_1 0.58 0.59 0.42 0.42 1.5 0.00108 NA 0.42
## proh_2 0.66 0.66 0.49 0.49 1.9 0.00092 NA 0.49
## proh_3 0.55 0.55 0.38 0.38 1.2 0.00119 NA 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## proh_1 523674 0.81 0.79 0.62 0.52 3.9 1.08
## proh_2 552613 0.73 0.76 0.56 0.47 4.2 0.89
## proh_3 533120 0.84 0.81 0.66 0.55 3.9 1.17
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## proh_1 0.05 0.08 0.12 0.45 0.31 0.06
## proh_2 0.02 0.03 0.09 0.47 0.38 0.01
## proh_3 0.07 0.08 0.14 0.36 0.35 0.04Belonging
##
## Pearson's product-moment correlation
##
## data: belong_1 and belong_2
## t = 1365.6, df = 517560, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8841496 0.8853332
## sample estimates:
## cor
## 0.8847428Factor Analysis
Note: Latent Voice Opportunity and Belongingness load onto their own factors, so I removed them from this analysis
opm22_clean_fa <- opm22_clean %>%
select(
mut_1, mut_2, mut_3, mut_4,
cvo_1, cvo_2, cvo_3, cvo_4, cvo_5,
proh_1, proh_2, proh_3,
cgs_1, cgs_2
)%>%
drop_na()
psych::fa.parallel(opm22_clean_fa, fa = "fa")
## Parallel analysis suggests that the number of factors = 5 and the number of components = NAWith 5 factors
##
## Call:
## stats::factanal(x = opm22_clean_fa, factors = 5, rotation = "promax")
##
## Uniquenesses:
## mut_1 mut_2 mut_3 mut_4 cvo_1 cvo_2 cvo_3 cvo_4 cvo_5 proh_1 proh_2 proh_3 cgs_1 cgs_2
## 0.230 0.306 0.138 0.210 0.365 0.323 0.360 0.232 0.301 0.374 0.566 0.532 0.188 0.147
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## mut_1 0.933 -0.109
## mut_2 0.193 0.115 0.615 -0.107
## mut_3 0.910
## mut_4 0.525 0.480
## cvo_1 0.148 0.658
## cvo_2 0.114 0.777
## cvo_3 0.286 0.408 0.172
## cvo_4 0.801 0.189
## cvo_5 0.767
## proh_1 -0.104 0.752 0.164
## proh_2 0.620 0.106
## proh_3 0.241 0.238 0.114
## cgs_1 0.933
## cgs_2 0.981 -0.117
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 2.718 1.937 1.626 1.477 0.587
## Proportion Var 0.194 0.138 0.116 0.105 0.042
## Cumulative Var 0.194 0.332 0.449 0.554 0.596
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4 Factor5
## Factor1 1.000 -0.698 -0.728 0.754 0.648
## Factor2 -0.698 1.000 0.748 -0.783 -0.764
## Factor3 -0.728 0.748 1.000 -0.709 -0.771
## Factor4 0.754 -0.783 -0.709 1.000 0.731
## Factor5 0.648 -0.764 -0.771 0.731 1.000
##
## Test of the hypothesis that 5 factors are sufficient.
## The chi square statistic is 27053.18 on 31 degrees of freedom.
## The p-value is 0Loadings (with 6 as a cutoff)
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## mut_1 0.933
## mut_2 0.615
## mut_3 0.910
## mut_4
## cvo_1 0.658
## cvo_2 0.777
## cvo_3
## cvo_4 0.801
## cvo_5 0.767
## proh_1 0.752
## proh_2 0.620
## proh_3
## cgs_1 0.933
## cgs_2 0.981
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 2.718 1.937 1.626 1.477 0.587
## Proportion Var 0.194 0.138 0.116 0.105 0.042
## Cumulative Var 0.194 0.332 0.449 0.554 0.596
Correlations
Linear Regressions
##
## Call:
## lm(formula = lvo ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9570 -0.2898 0.0430 0.3397 3.0069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2521050 0.0036002 347.8 <2e-16 ***
## mut 0.7409827 0.0009221 803.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6383 on 474974 degrees of freedom
## (82802 observations deleted due to missingness)
## Multiple R-squared: 0.5762, Adjusted R-squared: 0.5762
## F-statistic: 6.458e+05 on 1 and 474974 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = cvo ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5737 -0.2680 0.0204 0.3909 3.5320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6916243 0.0030807 224.5 <2e-16 ***
## mut 0.7764109 0.0007874 986.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5322 on 453318 degrees of freedom
## (104458 observations deleted due to missingness)
## Multiple R-squared: 0.682, Adjusted R-squared: 0.682
## F-statistic: 9.724e+05 on 1 and 453318 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = proh ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7358 -0.2851 0.0482 0.3159 2.6681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7309233 0.0032589 531.1 <2e-16 ***
## mut 0.6009827 0.0008342 720.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5653 on 452404 degrees of freedom
## (105372 observations deleted due to missingness)
## Multiple R-squared: 0.5343, Adjusted R-squared: 0.5343
## F-statistic: 5.19e+05 on 1 and 452404 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = cgs ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7188 -0.3160 0.0868 0.2882 3.5036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6908120 0.0033140 208.5 <2e-16 ***
## mut 0.8055989 0.0008491 948.8 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5842 on 467664 degrees of freedom
## (90112 observations deleted due to missingness)
## Multiple R-squared: 0.6581, Adjusted R-squared: 0.6581
## F-statistic: 9.001e+05 on 1 and 467664 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = belong ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8752 -0.2634 0.1248 0.4143 2.7038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.651497 0.004006 412.2 <2e-16 ***
## mut 0.644744 0.001026 628.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7066 on 469584 degrees of freedom
## (88192 observations deleted due to missingness)
## Multiple R-squared: 0.4568, Adjusted R-squared: 0.4568
## F-statistic: 3.949e+05 on 1 and 469584 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = exp_1 ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7315 -0.4047 0.1117 0.4580 3.3012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.940594 0.004483 209.8 <2e-16 ***
## mut 0.758186 0.001150 659.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8006 on 478548 degrees of freedom
## (79228 observations deleted due to missingness)
## Multiple R-squared: 0.4761, Adjusted R-squared: 0.4761
## F-statistic: 4.349e+05 on 1 and 478548 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = exp_2 ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7006 -0.5122 0.0530 0.4298 3.3139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.932487 0.004545 205.2 <2e-16 ***
## mut 0.753621 0.001165 646.8 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8069 on 473580 degrees of freedom
## (84196 observations deleted due to missingness)
## Multiple R-squared: 0.469, Adjusted R-squared: 0.469
## F-statistic: 4.184e+05 on 1 and 473580 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = exp_3 ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5139 -0.5139 0.1764 0.5215 3.2473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.062467 0.005455 194.8 <2e-16 ***
## mut 0.690281 0.001398 493.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9689 on 474424 degrees of freedom
## (83352 observations deleted due to missingness)
## Multiple R-squared: 0.3393, Adjusted R-squared: 0.3393
## F-statistic: 2.437e+05 on 1 and 474424 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = exp_4 ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4511 -0.5364 0.1735 0.5489 3.8800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.287196 0.005307 54.12 <2e-16 ***
## mut 0.832771 0.001363 611.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9339 on 456123 degrees of freedom
## (101653 observations deleted due to missingness)
## Multiple R-squared: 0.4503, Adjusted R-squared: 0.4503
## F-statistic: 3.736e+05 on 1 and 456123 DF, p-value: < 2.2e-16Latent voice opportunity
##
## Call:
## lm(formula = lvo ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9570 -0.2898 0.0430 0.3397 3.0069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2521050 0.0036002 347.8 <2e-16 ***
## mut 0.7409827 0.0009221 803.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6383 on 474974 degrees of freedom
## (82802 observations deleted due to missingness)
## Multiple R-squared: 0.5762, Adjusted R-squared: 0.5762
## F-statistic: 6.458e+05 on 1 and 474974 DF, p-value: < 2.2e-16Confidence in gaining status
##
## Call:
## lm(formula = lvo ~ cgs, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8790 -0.3790 0.1064 0.4495 2.7487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.594419 0.003938 404.9 <2e-16 ***
## cgs 0.656911 0.001026 640.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7279 on 511166 degrees of freedom
## (46610 observations deleted due to missingness)
## Multiple R-squared: 0.4452, Adjusted R-squared: 0.4452
## F-statistic: 4.103e+05 on 1 and 511166 DF, p-value: < 2.2e-16Confidence in voice efficacy
##
## Call:
## lm(formula = cvo ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5737 -0.2680 0.0204 0.3909 3.5320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6916243 0.0030807 224.5 <2e-16 ***
## mut 0.7764109 0.0007874 986.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5322 on 453318 degrees of freedom
## (104458 observations deleted due to missingness)
## Multiple R-squared: 0.682, Adjusted R-squared: 0.682
## F-statistic: 9.724e+05 on 1 and 453318 DF, p-value: < 2.2e-16Prohibitive voioce
##
## Call:
## lm(formula = proh ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7358 -0.2851 0.0482 0.3159 2.6681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7309233 0.0032589 531.1 <2e-16 ***
## mut 0.6009827 0.0008342 720.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5653 on 452404 degrees of freedom
## (105372 observations deleted due to missingness)
## Multiple R-squared: 0.5343, Adjusted R-squared: 0.5343
## F-statistic: 5.19e+05 on 1 and 452404 DF, p-value: < 2.2e-16Belonging
##
## Call:
## lm(formula = belong ~ mut, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8752 -0.2634 0.1248 0.4143 2.7038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.651497 0.004006 412.2 <2e-16 ***
## mut 0.644744 0.001026 628.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7066 on 469584 degrees of freedom
## (88192 observations deleted due to missingness)
## Multiple R-squared: 0.4568, Adjusted R-squared: 0.4568
## F-statistic: 3.949e+05 on 1 and 469584 DF, p-value: < 2.2e-16Controls
## $`as dv: mut`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.000e-12 -1.000e-14 0.000e+00 1.000e-14 2.405e-09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.377e-11 3.501e-14 3.934e+02 < 2e-16 ***
## mut 1.000e+00 6.038e-15 1.656e+14 < 2e-16 ***
## sexMale 1.260e-14 1.134e-14 1.111e+00 0.267
## leaveYes, - Fed -9.162e-15 1.593e-14 -5.750e-01 0.565
## leaveYes, - NotFed -1.022e-14 2.799e-14 -3.650e-01 0.715
## leaveYes, Other -1.009e-14 1.756e-14 -5.750e-01 0.566
## ageUnder 40 -8.456e-15 1.325e-14 -6.380e-01 0.523
## superSupervisor -1.054e-14 1.325e-14 -7.960e-01 0.426
## agencyAG 2.335e-15 3.059e-14 7.600e-02 0.939
## agencyAM 4.919e-15 1.004e-13 4.900e-02 0.961
## agencyAR 3.237e-16 2.852e-14 1.100e-02 0.991
## agencyCM 9.960e-16 3.695e-14 2.700e-02 0.978
## agencyCU -4.238e-16 1.456e-13 -3.000e-03 0.998
## agencyDD 8.187e-16 3.280e-14 2.500e-02 0.980
## agencyDJ 1.889e-15 3.504e-14 5.400e-02 0.957
## agencyDL 1.976e-15 5.412e-14 3.700e-02 0.971
## agencyDN 1.329e-16 4.984e-14 3.000e-03 0.998
## agencyDR 1.841e-15 1.313e-13 1.400e-02 0.989
## agencyED 2.591e-15 8.381e-14 3.100e-02 0.975
## agencyEE 3.634e-15 1.369e-13 2.700e-02 0.979
## agencyEP 1.462e-15 5.285e-14 2.800e-02 0.978
## agencyFT 2.479e-15 1.576e-13 1.600e-02 0.987
## agencyGS 1.362e-15 5.222e-14 2.600e-02 0.979
## agencyHE 2.954e-15 2.983e-14 9.900e-02 0.921
## agencyHS 8.560e-16 2.776e-14 3.100e-02 0.975
## agencyHU 2.835e-15 6.539e-14 4.300e-02 0.965
## agencyIN 1.927e-15 3.420e-14 5.600e-02 0.955
## agencyNF 2.284e-15 1.344e-13 1.700e-02 0.986
## agencyNQ 1.314e-15 1.156e-13 1.100e-02 0.991
## agencyNU 5.171e-16 9.880e-14 5.000e-03 0.996
## agencyNV 6.901e-17 3.096e-14 2.000e-03 0.998
## agencyOM 2.518e-15 1.125e-13 2.200e-02 0.982
## agencySB 1.109e-15 7.538e-14 1.500e-02 0.988
## agencyST 3.038e-15 5.163e-14 5.900e-02 0.953
## agencySZ 2.362e-15 3.488e-14 6.800e-02 0.946
## agencyTD -1.220e-15 3.744e-14 -3.300e-02 0.974
## agencyTR 1.932e-15 3.243e-14 6.000e-02 0.952
## agencyXX 3.563e-13 4.998e-14 7.129e+00 1.01e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.638e-12 on 436961 degrees of freedom
## (120779 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 8.935e+26 on 37 and 436961 DF, p-value: < 2.2e-16
##
##
## $`as dv: lvo`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0703 -0.3129 0.0325 0.3427 3.1234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.287731 0.006134 209.923 < 2e-16 ***
## mut 0.730906 0.001058 690.781 < 2e-16 ***
## sexMale -0.006010 0.001984 -3.030 0.002446 **
## leaveYes, - Fed -0.058248 0.002788 -20.891 < 2e-16 ***
## leaveYes, - NotFed -0.071020 0.004902 -14.487 < 2e-16 ***
## leaveYes, Other -0.051161 0.003076 -16.630 < 2e-16 ***
## ageUnder 40 -0.010139 0.002317 -4.376 1.21e-05 ***
## superSupervisor -0.074735 0.002315 -32.285 < 2e-16 ***
## agencyAG 0.054639 0.005351 10.211 < 2e-16 ***
## agencyAM 0.038908 0.017563 2.215 0.026735 *
## agencyAR 0.009747 0.004989 1.954 0.050736 .
## agencyCM 0.057609 0.006462 8.915 < 2e-16 ***
## agencyCU 0.188273 0.025398 7.413 1.24e-13 ***
## agencyDD 0.046402 0.005736 8.089 6.01e-16 ***
## agencyDJ 0.024205 0.006134 3.946 7.94e-05 ***
## agencyDL 0.091215 0.009471 9.631 < 2e-16 ***
## agencyDN 0.077916 0.008708 8.948 < 2e-16 ***
## agencyDR 0.114417 0.022902 4.996 5.86e-07 ***
## agencyED 0.138753 0.014650 9.471 < 2e-16 ***
## agencyEE 0.079095 0.023969 3.300 0.000967 ***
## agencyEP 0.041112 0.009244 4.448 8.69e-06 ***
## agencyFT 0.120294 0.027680 4.346 1.39e-05 ***
## agencyGS 0.126296 0.009120 13.848 < 2e-16 ***
## agencyHE 0.073393 0.005216 14.070 < 2e-16 ***
## agencyHS 0.060058 0.004858 12.363 < 2e-16 ***
## agencyHU 0.116285 0.011438 10.167 < 2e-16 ***
## agencyIN 0.005861 0.005982 0.980 0.327195
## agencyNF 0.021402 0.023412 0.914 0.360651
## agencyNQ 0.082430 0.020182 4.084 4.42e-05 ***
## agencyNU 0.158004 0.017301 9.133 < 2e-16 ***
## agencyNV -0.003326 0.005414 -0.614 0.538981
## agencyOM 0.117857 0.019682 5.988 2.12e-09 ***
## agencySB 0.134090 0.013212 10.149 < 2e-16 ***
## agencyST -0.009757 0.009036 -1.080 0.280241
## agencySZ 0.043479 0.006109 7.118 1.10e-12 ***
## agencyTD 0.067440 0.006550 10.296 < 2e-16 ***
## agencyTR 0.077833 0.005678 13.708 < 2e-16 ***
## agencyXX 0.067863 0.008748 7.758 8.67e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6336 on 433109 degrees of freedom
## (124631 observations deleted due to missingness)
## Multiple R-squared: 0.5776, Adjusted R-squared: 0.5775
## F-statistic: 1.6e+04 on 37 and 433109 DF, p-value: < 2.2e-16
##
##
## $`as dv: cvo`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5181 -0.2925 0.0524 0.3304 3.4803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0032571 0.0050916 197.040 < 2e-16 ***
## mut 0.7327509 0.0008790 833.638 < 2e-16 ***
## sexMale -0.0231220 0.0016454 -14.053 < 2e-16 ***
## leaveYes, - Fed -0.1931372 0.0023171 -83.354 < 2e-16 ***
## leaveYes, - NotFed -0.3099966 0.0040630 -76.297 < 2e-16 ***
## leaveYes, Other -0.1473387 0.0025647 -57.448 < 2e-16 ***
## ageUnder 40 -0.0914952 0.0019158 -47.759 < 2e-16 ***
## superSupervisor 0.1160180 0.0019164 60.541 < 2e-16 ***
## agencyAG -0.1389001 0.0044350 -31.319 < 2e-16 ***
## agencyAM -0.0714487 0.0144903 -4.931 8.19e-07 ***
## agencyAR -0.0047918 0.0041292 -1.160 0.245854
## agencyCM -0.1902763 0.0053575 -35.516 < 2e-16 ***
## agencyCU -0.1067713 0.0210235 -5.079 3.80e-07 ***
## agencyDD -0.0415402 0.0047523 -8.741 < 2e-16 ***
## agencyDJ -0.1001390 0.0050779 -19.721 < 2e-16 ***
## agencyDL -0.0666034 0.0078479 -8.487 < 2e-16 ***
## agencyDN -0.0045539 0.0071876 -0.634 0.526356
## agencyDR -0.0124038 0.0188950 -0.656 0.511531
## agencyED -0.0459088 0.0122105 -3.760 0.000170 ***
## agencyEE -0.1006407 0.0200779 -5.012 5.37e-07 ***
## agencyEP 0.0105665 0.0076299 1.385 0.166091
## agencyFT -0.1286485 0.0227440 -5.656 1.55e-08 ***
## agencyGS 0.0104654 0.0075456 1.387 0.165453
## agencyHE 0.0006267 0.0043156 0.145 0.884536
## agencyHS -0.1343345 0.0040230 -33.392 < 2e-16 ***
## agencyHU -0.0331013 0.0094817 -3.491 0.000481 ***
## agencyIN -0.0417876 0.0049504 -8.441 < 2e-16 ***
## agencyNF 0.0121304 0.0193443 0.627 0.530608
## agencyNQ -0.0597959 0.0168140 -3.556 0.000376 ***
## agencyNU -0.0120407 0.0142284 -0.846 0.397415
## agencyNV -0.0242466 0.0044795 -5.413 6.21e-08 ***
## agencyOM -0.0002396 0.0163358 -0.015 0.988298
## agencySB -0.0174225 0.0110226 -1.581 0.113968
## agencyST -0.0520484 0.0074750 -6.963 3.34e-12 ***
## agencySZ -0.2036626 0.0050830 -40.067 < 2e-16 ***
## agencyTD -0.0586075 0.0054207 -10.812 < 2e-16 ***
## agencyTR -0.1467289 0.0047079 -31.166 < 2e-16 ***
## agencyXX -0.0489306 0.0072334 -6.765 1.34e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5137 on 413787 degrees of freedom
## (143953 observations deleted due to missingness)
## Multiple R-squared: 0.7007, Adjusted R-squared: 0.7006
## F-statistic: 2.618e+04 on 37 and 413787 DF, p-value: < 2.2e-16
##
##
## $`as dv: proh`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3315 -0.2855 0.0508 0.3177 2.7167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8825322 0.0054903 342.884 < 2e-16 ***
## mut 0.5750798 0.0009494 605.703 < 2e-16 ***
## sexMale -0.0070809 0.0017829 -3.972 7.14e-05 ***
## leaveYes, - Fed -0.0823481 0.0025033 -32.896 < 2e-16 ***
## leaveYes, - NotFed -0.1564929 0.0043935 -35.619 < 2e-16 ***
## leaveYes, Other -0.1061840 0.0027674 -38.370 < 2e-16 ***
## ageUnder 40 0.0064570 0.0020834 3.099 0.001940 **
## superSupervisor 0.0629091 0.0020690 30.406 < 2e-16 ***
## agencyAG -0.0934087 0.0047856 -19.519 < 2e-16 ***
## agencyAM -0.0173387 0.0157026 -1.104 0.269511
## agencyAR 0.0111623 0.0044507 2.508 0.012143 *
## agencyCM -0.0617781 0.0058342 -10.589 < 2e-16 ***
## agencyCU 0.0399990 0.0228411 1.751 0.079915 .
## agencyDD -0.0037034 0.0051332 -0.721 0.470628
## agencyDJ -0.0473836 0.0054912 -8.629 < 2e-16 ***
## agencyDL 0.0097610 0.0085191 1.146 0.251883
## agencyDN 0.0291655 0.0077926 3.743 0.000182 ***
## agencyDR 0.0925272 0.0207818 4.452 8.50e-06 ***
## agencyED 0.0119252 0.0133075 0.896 0.370185
## agencyEE -0.0495948 0.0218068 -2.274 0.022949 *
## agencyEP 0.0097149 0.0083277 1.167 0.243383
## agencyFT -0.0378540 0.0249376 -1.518 0.129027
## agencyGS -0.0038205 0.0082174 -0.465 0.641980
## agencyHE -0.0085161 0.0046741 -1.822 0.068462 .
## agencyHS -0.0610933 0.0043400 -14.077 < 2e-16 ***
## agencyHU -0.0269148 0.0103490 -2.601 0.009303 **
## agencyIN -0.0756720 0.0053457 -14.156 < 2e-16 ***
## agencyNF 0.0452335 0.0212486 2.129 0.033273 *
## agencyNQ -0.0082672 0.0182620 -0.453 0.650766
## agencyNU 0.0720805 0.0154655 4.661 3.15e-06 ***
## agencyNV 0.0036130 0.0048319 0.748 0.454616
## agencyOM 0.0311128 0.0178986 1.738 0.082162 .
## agencySB 0.0896386 0.0118841 7.543 4.61e-14 ***
## agencyST -0.0392458 0.0081167 -4.835 1.33e-06 ***
## agencySZ 0.0006967 0.0054766 0.127 0.898776
## agencyTD -0.0073951 0.0058696 -1.260 0.207710
## agencyTR -0.0162027 0.0050949 -3.180 0.001472 **
## agencyXX 0.0034985 0.0079006 0.443 0.657897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5564 on 413669 degrees of freedom
## (144071 observations deleted due to missingness)
## Multiple R-squared: 0.5385, Adjusted R-squared: 0.5385
## F-statistic: 1.305e+04 on 37 and 413669 DF, p-value: < 2.2e-16
##
##
## $`as dv: cgs`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7586 -0.3100 0.0511 0.3258 3.5604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9225188 0.0055920 164.970 < 2e-16 ***
## mut 0.7750519 0.0009647 803.403 < 2e-16 ***
## sexMale -0.0252849 0.0018103 -13.967 < 2e-16 ***
## leaveYes, - Fed -0.1673907 0.0025425 -65.837 < 2e-16 ***
## leaveYes, - NotFed -0.2600088 0.0044685 -58.187 < 2e-16 ***
## leaveYes, Other -0.1249155 0.0028096 -44.460 < 2e-16 ***
## ageUnder 40 -0.0551910 0.0021123 -26.129 < 2e-16 ***
## superSupervisor -0.0201924 0.0021143 -9.550 < 2e-16 ***
## agencyAG -0.0917815 0.0048817 -18.801 < 2e-16 ***
## agencyAM -0.1308357 0.0159810 -8.187 2.69e-16 ***
## agencyAR 0.0063491 0.0045504 1.395 0.162934
## agencyCM -0.0652572 0.0059029 -11.055 < 2e-16 ***
## agencyCU -0.0761084 0.0231689 -3.285 0.001020 **
## agencyDD -0.0062027 0.0052333 -1.185 0.235917
## agencyDJ -0.0178775 0.0055963 -3.195 0.001401 **
## agencyDL 0.0040406 0.0086233 0.469 0.639377
## agencyDN 0.0011589 0.0079408 0.146 0.883970
## agencyDR 0.0782399 0.0208674 3.749 0.000177 ***
## agencyED 0.0145000 0.0133868 1.083 0.278739
## agencyEE 0.0212794 0.0217321 0.979 0.327497
## agencyEP 0.0160665 0.0084139 1.910 0.056198 .
## agencyFT 0.0036740 0.0250494 0.147 0.883392
## agencyGS 0.0161116 0.0083315 1.934 0.053135 .
## agencyHE 0.0126849 0.0047586 2.666 0.007684 **
## agencyHS -0.0544070 0.0044304 -12.280 < 2e-16 ***
## agencyHU 0.0342810 0.0104432 3.283 0.001029 **
## agencyIN -0.0377177 0.0054520 -6.918 4.58e-12 ***
## agencyNF 0.0495648 0.0214065 2.315 0.020591 *
## agencyNQ -0.0082552 0.0185526 -0.445 0.656349
## agencyNU -0.0142936 0.0157568 -0.907 0.364336
## agencyNV -0.0203672 0.0049383 -4.124 3.72e-05 ***
## agencyOM 0.0355539 0.0179492 1.981 0.047614 *
## agencySB 0.1265520 0.0120823 10.474 < 2e-16 ***
## agencyST -0.1011422 0.0082440 -12.269 < 2e-16 ***
## agencySZ -0.0512133 0.0055785 -9.180 < 2e-16 ***
## agencyTD -0.0130470 0.0059743 -2.184 0.028974 *
## agencyTR -0.0353936 0.0051781 -6.835 8.20e-12 ***
## agencyXX -0.0001958 0.0079796 -0.025 0.980427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5743 on 427222 degrees of freedom
## (130518 observations deleted due to missingness)
## Multiple R-squared: 0.6661, Adjusted R-squared: 0.6661
## F-statistic: 2.304e+04 on 37 and 427222 DF, p-value: < 2.2e-16
##
##
## $`as dv: belong`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9175 -0.3056 0.0871 0.3704 2.7618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.704575 0.006805 250.500 < 2e-16 ***
## mut 0.629962 0.001175 536.053 < 2e-16 ***
## sexMale 0.005179 0.002203 2.351 0.018725 *
## leaveYes, - Fed -0.064483 0.003098 -20.817 < 2e-16 ***
## leaveYes, - NotFed -0.087672 0.005447 -16.096 < 2e-16 ***
## leaveYes, Other -0.077167 0.003417 -22.581 < 2e-16 ***
## ageUnder 40 0.011027 0.002574 4.283 1.84e-05 ***
## superSupervisor -0.017785 0.002569 -6.922 4.46e-12 ***
## agencyAG 0.030518 0.005935 5.142 2.72e-07 ***
## agencyAM 0.122976 0.019406 6.337 2.35e-10 ***
## agencyAR 0.018979 0.005530 3.432 0.000600 ***
## agencyCM -0.017709 0.007216 -2.454 0.014124 *
## agencyCU 0.097477 0.028309 3.443 0.000575 ***
## agencyDD 0.028412 0.006367 4.462 8.11e-06 ***
## agencyDJ 0.022026 0.006797 3.241 0.001193 **
## agencyDL 0.072769 0.010533 6.908 4.91e-12 ***
## agencyDN 0.061688 0.009673 6.377 1.80e-10 ***
## agencyDR 0.120075 0.025454 4.717 2.39e-06 ***
## agencyED 0.151209 0.016291 9.282 < 2e-16 ***
## agencyEE 0.053699 0.026618 2.017 0.043655 *
## agencyEP 0.085839 0.010246 8.377 < 2e-16 ***
## agencyFT 0.194322 0.030649 6.340 2.30e-10 ***
## agencyGS 0.078871 0.010135 7.782 7.17e-15 ***
## agencyHE 0.058546 0.005787 10.117 < 2e-16 ***
## agencyHS 0.022821 0.005387 4.237 2.27e-05 ***
## agencyHU 0.067155 0.012702 5.287 1.24e-07 ***
## agencyIN 0.015585 0.006636 2.349 0.018841 *
## agencyNF 0.060239 0.026078 2.310 0.020892 *
## agencyNQ 0.050052 0.022519 2.223 0.026238 *
## agencyNU 0.096883 0.019168 5.054 4.32e-07 ***
## agencyNV 0.013948 0.006007 2.322 0.020226 *
## agencyOM 0.081893 0.021860 3.746 0.000180 ***
## agencySB 0.134484 0.014639 9.186 < 2e-16 ***
## agencyST 0.105842 0.010010 10.574 < 2e-16 ***
## agencySZ 0.021422 0.006787 3.156 0.001599 **
## agencyTD 0.052107 0.007270 7.167 7.67e-13 ***
## agencyTR 0.003972 0.006308 0.630 0.528908
## agencyXX 0.111004 0.009709 11.433 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7002 on 428646 degrees of freedom
## (129094 observations deleted due to missingness)
## Multiple R-squared: 0.4584, Adjusted R-squared: 0.4584
## F-statistic: 9807 on 37 and 428646 DF, p-value: < 2.2e-16
##
##
## $`as dv: exp_1`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9131 -0.4098 0.1034 0.4523 3.5712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.468209 0.007373 199.123 < 2e-16 ***
## mut 0.671074 0.001272 527.644 < 2e-16 ***
## sexMale -0.019954 0.002388 -8.355 < 2e-16 ***
## leaveYes, - Fed -0.453524 0.003355 -135.160 < 2e-16 ***
## leaveYes, - NotFed -0.631978 0.005894 -107.231 < 2e-16 ***
## leaveYes, Other -0.333356 0.003699 -90.124 < 2e-16 ***
## ageUnder 40 -0.049925 0.002791 -17.890 < 2e-16 ***
## superSupervisor -0.008591 0.002791 -3.079 0.002080 **
## agencyAG -0.074776 0.006444 -11.604 < 2e-16 ***
## agencyAM -0.034824 0.021152 -1.646 0.099686 .
## agencyAR 0.036581 0.006007 6.090 1.13e-09 ***
## agencyCM 0.011885 0.007783 1.527 0.126772
## agencyCU 0.105479 0.030662 3.440 0.000582 ***
## agencyDD -0.003126 0.006909 -0.452 0.650940
## agencyDJ -0.038210 0.007380 -5.177 2.25e-07 ***
## agencyDL 0.031284 0.011402 2.744 0.006076 **
## agencyDN 0.045466 0.010500 4.330 1.49e-05 ***
## agencyDR 0.269780 0.027653 9.756 < 2e-16 ***
## agencyED -0.105766 0.017660 -5.989 2.11e-09 ***
## agencyEE -0.014346 0.028843 -0.497 0.618907
## agencyEP 0.112037 0.011131 10.065 < 2e-16 ***
## agencyFT -0.159442 0.033172 -4.807 1.54e-06 ***
## agencyGS 0.165818 0.011004 15.068 < 2e-16 ***
## agencyHE 0.089480 0.006283 14.241 < 2e-16 ***
## agencyHS -0.123112 0.005848 -21.054 < 2e-16 ***
## agencyHU -0.090164 0.013781 -6.543 6.06e-11 ***
## agencyIN -0.022293 0.007201 -3.096 0.001964 **
## agencyNF 0.212900 0.028304 7.522 5.41e-14 ***
## agencyNQ -0.184372 0.024360 -7.569 3.78e-14 ***
## agencyNU -0.078218 0.020797 -3.761 0.000169 ***
## agencyNV -0.013598 0.006521 -2.085 0.037043 *
## agencyOM 0.063921 0.023736 2.693 0.007081 **
## agencySB 0.163496 0.015880 10.296 < 2e-16 ***
## agencyST -0.029926 0.010878 -2.751 0.005939 **
## agencySZ -0.183899 0.007347 -25.032 < 2e-16 ***
## agencyTD 0.069733 0.007888 8.841 < 2e-16 ***
## agencyTR -0.032266 0.006831 -4.723 2.32e-06 ***
## agencyXX -0.021842 0.010533 -2.074 0.038109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7656 on 436091 degrees of freedom
## (121649 observations deleted due to missingness)
## Multiple R-squared: 0.5148, Adjusted R-squared: 0.5147
## F-statistic: 1.25e+04 on 37 and 436091 DF, p-value: < 2.2e-16
##
##
## $`as dv: exp_2`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8783 -0.4017 0.1014 0.4403 3.8540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.587685 0.007324 216.781 < 2e-16 ***
## mut 0.657706 0.001263 520.607 < 2e-16 ***
## sexMale -0.031139 0.002371 -13.133 < 2e-16 ***
## leaveYes, - Fed -0.565849 0.003332 -169.836 < 2e-16 ***
## leaveYes, - NotFed -0.816224 0.005853 -139.463 < 2e-16 ***
## leaveYes, Other -0.401124 0.003676 -109.126 < 2e-16 ***
## ageUnder 40 -0.161417 0.002770 -58.280 < 2e-16 ***
## superSupervisor -0.052696 0.002771 -19.020 < 2e-16 ***
## agencyAG -0.090653 0.006396 -14.173 < 2e-16 ***
## agencyAM -0.153258 0.020983 -7.304 2.80e-13 ***
## agencyAR 0.033258 0.005965 5.575 2.47e-08 ***
## agencyCM -0.100409 0.007725 -12.997 < 2e-16 ***
## agencyCU -0.081316 0.030405 -2.674 0.00749 **
## agencyDD -0.014981 0.006858 -2.184 0.02894 *
## agencyDJ -0.013711 0.007331 -1.870 0.06146 .
## agencyDL -0.012297 0.011305 -1.088 0.27672
## agencyDN -0.020865 0.010412 -2.004 0.04508 *
## agencyDR 0.031224 0.027384 1.140 0.25418
## agencyED -0.038369 0.017490 -2.194 0.02825 *
## agencyEE -0.016350 0.028473 -0.574 0.56582
## agencyEP 0.003935 0.011040 0.356 0.72154
## agencyFT -0.269663 0.032885 -8.200 2.41e-16 ***
## agencyGS 0.014193 0.010901 1.302 0.19290
## agencyHE 0.016374 0.006235 2.626 0.00864 **
## agencyHS -0.068234 0.005807 -11.751 < 2e-16 ***
## agencyHU -0.010123 0.013673 -0.740 0.45908
## agencyIN -0.042045 0.007148 -5.882 4.05e-09 ***
## agencyNF 0.045429 0.028032 1.621 0.10511
## agencyNQ -0.035729 0.024212 -1.476 0.14003
## agencyNU -0.061875 0.020642 -2.997 0.00272 **
## agencyNV -0.021254 0.006475 -3.282 0.00103 **
## agencyOM 0.034900 0.023552 1.482 0.13839
## agencySB 0.213810 0.015756 13.570 < 2e-16 ***
## agencyST -0.112593 0.010792 -10.433 < 2e-16 ***
## agencySZ -0.195332 0.007298 -26.764 < 2e-16 ***
## agencyTD 0.002963 0.007826 0.379 0.70493
## agencyTR -0.091886 0.006779 -13.555 < 2e-16 ***
## agencyXX -0.030802 0.010440 -2.951 0.00317 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7561 on 431597 degrees of freedom
## (126143 observations deleted due to missingness)
## Multiple R-squared: 0.53, Adjusted R-squared: 0.5299
## F-statistic: 1.315e+04 on 37 and 431597 DF, p-value: < 2.2e-16
##
##
## $`as dv: exp_3`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7946 -0.5298 0.1582 0.5881 4.0441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.608954 0.009138 176.081 < 2e-16 ***
## mut 0.637127 0.001577 404.090 < 2e-16 ***
## sexMale -0.132168 0.002959 -44.673 < 2e-16 ***
## leaveYes, - Fed -0.273275 0.004157 -65.732 < 2e-16 ***
## leaveYes, - NotFed -0.492735 0.007309 -67.412 < 2e-16 ***
## leaveYes, Other -0.274483 0.004584 -59.878 < 2e-16 ***
## ageUnder 40 -0.022907 0.003460 -6.620 3.59e-11 ***
## superSupervisor -0.054312 0.003451 -15.737 < 2e-16 ***
## agencyAG -0.286507 0.007985 -35.882 < 2e-16 ***
## agencyAM -0.121995 0.026108 -4.673 2.97e-06 ***
## agencyAR -0.041169 0.007436 -5.537 3.08e-08 ***
## agencyCM -0.195429 0.009655 -20.241 < 2e-16 ***
## agencyCU -0.150209 0.037858 -3.968 7.26e-05 ***
## agencyDD -0.069011 0.008552 -8.070 7.06e-16 ***
## agencyDJ -0.213106 0.009140 -23.315 < 2e-16 ***
## agencyDL -0.074264 0.014114 -5.262 1.43e-07 ***
## agencyDN -0.187005 0.012988 -14.398 < 2e-16 ***
## agencyDR 0.123195 0.034201 3.602 0.000316 ***
## agencyED -0.173707 0.021802 -7.967 1.62e-15 ***
## agencyEE -0.096319 0.035752 -2.694 0.007059 **
## agencyEP -0.115771 0.013764 -8.411 < 2e-16 ***
## agencyFT -1.016944 0.041072 -24.760 < 2e-16 ***
## agencyGS -0.030253 0.013610 -2.223 0.026231 *
## agencyHE -0.017643 0.007779 -2.268 0.023327 *
## agencyHS -0.213383 0.007239 -29.476 < 2e-16 ***
## agencyHU -0.175678 0.017047 -10.306 < 2e-16 ***
## agencyIN -0.294979 0.008931 -33.029 < 2e-16 ***
## agencyNF -0.026564 0.035033 -0.758 0.448303
## agencyNQ -0.361217 0.030168 -11.974 < 2e-16 ***
## agencyNU -0.333938 0.025772 -12.958 < 2e-16 ***
## agencyNV -0.094917 0.008073 -11.757 < 2e-16 ***
## agencyOM 0.012351 0.029311 0.421 0.673485
## agencySB 0.090593 0.019711 4.596 4.31e-06 ***
## agencyST -0.099466 0.013457 -7.391 1.46e-13 ***
## agencySZ -0.208810 0.009116 -22.907 < 2e-16 ***
## agencyTD -0.234038 0.009769 -23.958 < 2e-16 ***
## agencyTR -0.198028 0.008465 -23.392 < 2e-16 ***
## agencyXX -0.202916 0.013030 -15.573 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9444 on 432502 degrees of freedom
## (125238 observations deleted due to missingness)
## Multiple R-squared: 0.3659, Adjusted R-squared: 0.3659
## F-statistic: 6746 on 37 and 432502 DF, p-value: < 2.2e-16
##
##
## $`as dv: exp_4`
##
## Call:
## lm(formula = y ~ mut + sex + leave + age + super + agency, data = opm22_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7030 -0.5285 0.1380 0.5656 4.1339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6141706 0.0090259 68.045 < 2e-16 ***
## mut 0.7841557 0.0015569 503.659 < 2e-16 ***
## sexMale 0.0264013 0.0029433 8.970 < 2e-16 ***
## leaveYes, - Fed -0.2151555 0.0041271 -52.132 < 2e-16 ***
## leaveYes, - NotFed -0.2471967 0.0072401 -34.143 < 2e-16 ***
## leaveYes, Other -0.1467412 0.0045448 -32.288 < 2e-16 ***
## ageUnder 40 0.0017810 0.0034494 0.516 0.605630
## superSupervisor 0.0687765 0.0034072 20.186 < 2e-16 ***
## agencyAG -0.1046711 0.0078952 -13.258 < 2e-16 ***
## agencyAM -0.2245785 0.0258141 -8.700 < 2e-16 ***
## agencyAR -0.0324920 0.0073434 -4.425 9.66e-06 ***
## agencyCM -0.0494613 0.0096724 -5.114 3.16e-07 ***
## agencyCU -0.0807131 0.0382674 -2.109 0.034929 *
## agencyDD -0.0490478 0.0084795 -5.784 7.29e-09 ***
## agencyDJ -0.2944708 0.0090323 -32.602 < 2e-16 ***
## agencyDL 0.0104844 0.0141401 0.741 0.458411
## agencyDN -0.0594849 0.0129346 -4.599 4.25e-06 ***
## agencyDR 0.0543197 0.0344405 1.577 0.114749
## agencyED -0.0739641 0.0221695 -3.336 0.000849 ***
## agencyEE -0.0534772 0.0360676 -1.483 0.138157
## agencyEP -0.0463480 0.0137872 -3.362 0.000775 ***
## agencyFT -0.2235246 0.0410403 -5.446 5.14e-08 ***
## agencyGS -0.0173778 0.0136323 -1.275 0.202398
## agencyHE -0.0592210 0.0077325 -7.659 1.88e-14 ***
## agencyHS -0.2868424 0.0071507 -40.114 < 2e-16 ***
## agencyHU -0.1352921 0.0171977 -7.867 3.64e-15 ***
## agencyIN -0.1400927 0.0088344 -15.858 < 2e-16 ***
## agencyNF -0.0009681 0.0347197 -0.028 0.977755
## agencyNQ -0.0309183 0.0300172 -1.030 0.303002
## agencyNU -0.1675256 0.0258190 -6.488 8.68e-11 ***
## agencyNV -0.0517063 0.0079730 -6.485 8.87e-11 ***
## agencyOM -0.0389241 0.0295956 -1.315 0.188444
## agencySB 0.1416854 0.0196595 7.207 5.73e-13 ***
## agencyST -0.3188363 0.0132773 -24.014 < 2e-16 ***
## agencySZ -0.0242879 0.0090160 -2.694 0.007063 **
## agencyTD -0.0526204 0.0096933 -5.429 5.69e-08 ***
## agencyTR -0.0340090 0.0084208 -4.039 5.38e-05 ***
## agencyXX -0.1412484 0.0130185 -10.850 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.921 on 416049 degrees of freedom
## (141691 observations deleted due to missingness)
## Multiple R-squared: 0.4598, Adjusted R-squared: 0.4598
## F-statistic: 9573 on 37 and 416049 DF, p-value: < 2.2e-16Moderation
Because we are so highly powered, all of the interactions are significant - but when I plot the interactions, the lines pretty much overlap. So I don’t include any in this report.
## [1] "mut"
## [1] "lvo"
## [1] "cvo"
## [1] "proh"
## [1] "cgs"
## [1] "belong"
## [1] "exp_1"
## [1] "exp_2"
## [1] "exp_3"
## [1] "exp_4"Multilevel models (nesting department)
## dvs output
## [1,] "mut" "0.0271756556342543"
## [2,] "lvo" "0.0226570306174541"
## [3,] "cvo" "0.0273716808141622"
## [4,] "proh" "0.0194175241922053"
## [5,] "cgs" "0.0252733564813611"
## [6,] "belong" "0.0186458148578086"
## [7,] "exp_1" "0.0317611694052896"
## [8,] "exp_2" "0.0192776223961834"
## [9,] "exp_3" "0.0317929491552445"
## [10,] "exp_4" "0.022357541254379"