Attitudes Regarding the Assignment Process Among Navy Medical Corps Officers
Date: 2023-04-06
This report covers the survey about attitudes collected by Richard Childers, MD and Joel Schofer, MD.
Overall satisfaction was treated as a continuous response variable in a multiple regression. The best fitting model included the three predictor variables of (a) specialty type, (b) officer rank, and (c) billet category, and no interaction terms. Additional variables such as (d) bonus pay, (e) deployment rate, and (f) staffing status did not improve the model fit significantly, and therefore were removed.
Among the five levels in billet category, CONUS MTF had the highest values of satisfaction, and served as the reference group. The CONUS Operational, OCONUS MTF, and Non-operational/non-clinical types were roughly .4 units less satisfied than CONUS MTF (after holding the other two predictors constant). Similarly, physicians currently assigned to OCONUS operational were 1.1 units less satisfied than CONUS MTF.
Among the five levels in specialty type, the non-surgical physicians were most satisfied, and served as the reference group. Residents were .2 units less satisfied, which was not significantly different. However, the difference was significant between the nonsurgical type and the surgical (.3 units), family practice (.4 units) and operational (.7 units) types.
Officer rank was a continuous variable and had a significant positive slope: satisfaction increased with seniority. Note that this does not necessarily mean an officer is expected to grow more satisfied as seniority increases; an alternate explanation is that less-satisfied officers leave the Navy while the more-satisfied officer remain and are promoted. Longitudinal studies are needed to evaluate these competing explanations.
We were comfortable treating the 5-point Likert outcome as a continuous variable in the linear regression, after inspecting the graphs of frequencies and model diagnostics. Thus we are making the assumption that the perceived distance between values 1 and 2 are roughly equivalent to the distance between values 4 and 5. We performed ordered logistic regression that treats the outcome as an ordinal variable, which supported similar conclusions about the roles of the three predictors. We chose to report the conventional regression for ease of interpretability.
1 Summary
1.1 Notes
- The current report covers 951 responses.
- We excluded 17 cases because their orders preceded the year 2012 and
26 cases because the
year_executed_ordervalue was missing.
1.2 Unanswered Questions
1.3 Answered Questions
2 Histograms
2.1 Univariate
Warning: `fct_explicit_na()` was deprecated in forcats 1.0.0.
ℹ Please use `fct_na_value_to_level()` instead.
ℹ The deprecated feature was likely used in the TabularManifest package.
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Satisfaction summary Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.00 3.00 4.00 3.63 5.00 5.00 79 Satisfaction summary (emergency medicine only) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 3.000 4.000 3.759 5.000 5.000 5 Counts by bonus pay & specialty| bonus_pay_cut4 | specialty_type | n |
|---|---|---|
| $0 | operational | 78 |
| $0 | resident | 50 |
| $20-24k | nonsurgical | 322 |
| $20-24k | family | 177 |
| $20-24k | operational | 12 |
| $24-32k | nonsurgical | 70 |
| $24-32k | surgical | 109 |
| $24-32k | unknown | 6 |
| 32k+ | nonsurgical | 79 |
| 32k+ | surgical | 48 |
3 Survey Response
Overall unweighted mean satisfaction:[1] 3.629587Overall weighted mean satisfaction: mean SE
satisfaction_rank 3.4141 0.056`specialty_type` unweighted mean satisfaction:# A tibble: 6 × 2
specialty_type satisfaction_rank
<fct> <dbl>
1 nonsurgical 3.92
2 surgical 3.68
3 family 3.35
4 operational 2.66
5 resident 3.52
6 unknown 3.4 `specialty_type` weighted mean satisfaction:# A tibble: 5 × 3
specialty_type satisfaction_rank se
<fct> <dbl> <dbl>
1 nonsurgical 3.92 0.0509
2 surgical 3.68 0.0925
3 family 3.35 0.0963
4 operational 2.66 0.140
5 resident 3.52 0.167 `officer_rank` unweighted mean satisfaction:# A tibble: 5 × 2
officer_rank satisfaction_rank
<fct> <dbl>
1 LT 3.14
2 LCDR 3.59
3 CDR 3.93
4 CAPT or Flag 4.13
5 Unknown 2.5 `officer_rank` weighted mean satisfaction:# A tibble: 5 × 3
officer_rank satisfaction_rank se
<fct> <dbl> <dbl>
1 LT 2.93 0.103
2 LCDR 3.50 0.0868
3 CDR 3.96 0.0771
4 CAPT or Flag 4.02 0.151
5 Unknown 1 0 `billet_current` unweighted mean satisfaction:# A tibble: 6 × 2
billet_current satisfaction_rank
<fct> <dbl>
1 CONUS MTF 3.85
2 Administrative 3.65
3 OCONUS MTF 3.41
4 CONUS Operational 3.12
5 OCONUS Operational 2.45
6 Other 4 `billet_current` weighted mean satisfaction:# A tibble: 6 × 3
billet_current satisfaction_rank se
<fct> <dbl> <dbl>
1 CONUS MTF 3.70 0.0625
2 Administrative 3.33 0.275
3 OCONUS MTF 3.50 0.133
4 CONUS Operational 2.94 0.145
5 OCONUS Operational 2.16 0.231
6 Other 3.78 0.418 4 Relationships between Outcomes
| satisfaction rank | transparency rank | favoritism rank | assignment current choice | |
|---|---|---|---|---|
| satisfaction_rank | 1.000 | 0.771 | 0.486 | -0.519 |
| transparency_rank | 0.771 | 1.000 | 0.488 | -0.405 |
| favoritism_rank | 0.486 | 0.488 | 1.000 | -0.325 |
| assignment_current_choice | -0.519 | -0.405 | -0.325 | 1.000 |
5 Analyses - 1 Predictor
5.1 By Rank
5.1.1 satisfaction_rank
Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
ℹ Please use the `fun` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
Data:
ds
Formula:
~ satisfaction_rank 1 + officer_rate_f
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.1361502 | 0.0903269 | 34.720017 | 0.00e+00 |
| officer_rate_f4 | 0.4547589 | 0.1158670 | 3.924834 | 9.37e-05 |
| officer_rate_f5 | 0.7953566 | 0.1268635 | 6.269388 | 0.00e+00 |
| officer_rate_f6 | 0.9934794 | 0.1557247 | 6.379715 | 0.00e+00 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0619066 | 0.0586569 | 1.318277 | 19.04967 | 0 | 3 | -1472.875 | 2955.751 | 2979.593 | 1504.982 | 866 | 870 |
5.2 By Specialty Type
5.2.1 satisfaction_rank
Data:
[ ds ds$specialty_type != “unknown” Formula:
~
satisfaction_rank 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.9172414 | 0.0625729 | 62.602823 | 0.0000000 |
| specialty_typesurgical | -0.2326776 | 0.1238796 | -1.878257 | 0.0606834 |
| specialty_typefamily | -0.5635828 | 0.1195853 | -4.712809 | 0.0000028 |
| specialty_typeoperational | -1.2544507 | 0.1540125 | -8.145121 | 0.0000000 |
| specialty_typeresident | -0.4020899 | 0.2356418 | -1.706360 | 0.0883012 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0811978 | 0.0769342 | 1.305062 | 19.04449 | 0 | 4 | -1458.551 | 2929.103 | 2957.693 | 1468.146 | 862 | 867 |
5.3 By Bonus Pay
5.3.1 satisfaction_rank
Data:
ds
Formula:
~ satisfaction_rank 1 + bonus_pay_cut4
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.8411215 | 0.1285699 | 22.097867 | 0e+00 |
| bonus_pay_cut4$20-24k | 0.8846169 | 0.1423437 | 6.214653 | 0e+00 |
| bonus_pay_cut4$24-32k | 0.9289934 | 0.1633872 | 5.685841 | 0e+00 |
| bonus_pay_cut432k+ | 0.9110153 | 0.1778978 | 5.121004 | 4e-07 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0472242 | 0.0439312 | 1.329938 | 14.34077 | 0 | 3 | -1483.945 | 2977.89 | 3001.744 | 1535.262 | 868 | 872 |
5.4 By Assignment Current Choice
5.4.1 satisfaction_rank
Data:
ds Formula:
~ satisfaction_rank 1 +
assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.7192430 | 0.0674241 | 69.99342 | 0 |
| assignment_current_choice | -0.5721764 | 0.0342102 | -16.72533 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2693047 | 0.268342 | 1.074932 | 279.7366 | 0 | 1 | -1133.799 | 2273.598 | 2287.502 | 877.0091 | 759 | 761 |
5.5 By Manning Proportion
5.5.1 manning_proportion
5.6 By Critical War
5.7 By Billet Current
Data:
ds Formula:
~ satisfaction_rank 1 + billet_current
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8472469 | 0.0553450 | 69.5139096 | 0.0000000 |
| billet_currentAdministrative | -0.2014136 | 0.1974596 | -1.0200240 | 0.3080020 |
| billet_currentOCONUS MTF | -0.4372469 | 0.1425066 | -3.0682562 | 0.0022201 |
| billet_currentCONUS Operational | -0.7311755 | 0.1358693 | -5.3814634 | 0.0000001 |
| billet_currentOCONUS Operational | -1.3972469 | 0.2148854 | -6.5022872 | 0.0000000 |
| billet_currentOther | 0.1527531 | 0.4412199 | 0.3462063 | 0.7292718 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0731888 | 0.0678376 | 1.313205 | 13.67732 | 0 | 5 | -1471.899 | 2957.797 | 2991.193 | 1493.423 | 866 | 872 |
6 Analyses - 2 Predictors
6.1 By Rank and Specialty Type
6.1.1 satisfaction_rank
Data:
[ ds ds$specialty_type != “unknown” Formula:
~
satisfaction_rank 1 + officer_rate_f * specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.7794118 | 0.1553781 | 24.3239628 | 0.0000000 |
| officer_rate_f4 | -0.0092968 | 0.1832411 | -0.0507355 | 0.9595483 |
| officer_rate_f5 | 0.2560492 | 0.1891705 | 1.3535366 | 0.1762449 |
| officer_rate_f6 | 0.5539216 | 0.2373440 | 2.3338342 | 0.0198372 |
| specialty_typesurgical | -0.1127451 | 0.3654966 | -0.3084710 | 0.7577997 |
| specialty_typefamily | -0.9315857 | 0.2446039 | -3.8085478 | 0.0001499 |
| specialty_typeoperational | -1.3667134 | 0.2240553 | -6.0998925 | 0.0000000 |
| specialty_typeresident | -0.2294118 | 0.3259239 | -0.7038814 | 0.4816999 |
| officer_rate_f4:specialty_typesurgical | -0.2384509 | 0.4064573 | -0.5866567 | 0.5575904 |
| officer_rate_f5:specialty_typesurgical | 0.0516431 | 0.4328109 | 0.1193202 | 0.9050499 |
| officer_rate_f6:specialty_typesurgical | -0.1253501 | 0.4939158 | -0.2537885 | 0.7997205 |
| officer_rate_f4:specialty_typefamily | 0.6244337 | 0.3157016 | 1.9779237 | 0.0482607 |
| officer_rate_f5:specialty_typefamily | 0.3285571 | 0.3403586 | 0.9653265 | 0.3346565 |
| officer_rate_f6:specialty_typefamily | 0.4871412 | 0.3909269 | 1.2461183 | 0.2130651 |
| officer_rate_f4:specialty_typeoperational | 0.6680270 | 0.4205932 | 1.5882972 | 0.1125919 |
| officer_rate_f5:specialty_typeoperational | 2.3312524 | 1.3051914 | 1.7861383 | 0.0744341 |
| officer_rate_f6:specialty_typeoperational | 0.6583800 | 0.5362841 | 1.2276701 | 0.2199115 |
| officer_rate_f4:specialty_typeresident | -0.0791647 | 0.4918785 | -0.1609436 | 0.8721761 |
| officer_rate_f5:specialty_typeresident | NA | NA | NA | NA |
| officer_rate_f6:specialty_typeresident | NA | NA | NA | NA |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1249664 | 0.1074244 | 1.281281 | 7.123859 | 0 | 17 | -1434.353 | 2906.706 | 2997.22 | 1392.145 | 848 | 866 |
Data:
[ ds ds$specialty_type != “unknown”
Formula:
~ satisfaction_rank 1 + officer_rate_f +
specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.6060419 | 0.1138533 | 31.6726901 | 0.0000000 |
| officer_rate_f4 | 0.1894040 | 0.1227398 | 1.5431344 | 0.1231669 |
| officer_rate_f5 | 0.4624532 | 0.1364136 | 3.3900819 | 0.0007306 |
| officer_rate_f6 | 0.7806986 | 0.1587342 | 4.9182747 | 0.0000010 |
| specialty_typesurgical | -0.2466207 | 0.1222314 | -2.0176535 | 0.0439382 |
| specialty_typefamily | -0.5476117 | 0.1188297 | -4.6083747 | 0.0000047 |
| specialty_typeoperational | -1.0520849 | 0.1656575 | -6.3509630 | 0.0000000 |
| specialty_typeresident | -0.1655041 | 0.2383040 | -0.6945081 | 0.4875516 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.113058 | 0.1058219 | 1.282431 | 15.62411 | 0 | 7 | -1440.206 | 2898.412 | 2941.287 | 1411.091 | 858 | 866 |
6.2 By Rank and Assignment Current Choice
6.3 By Rank and Bonus Pay
6.3.1 satisfaction_rank
Data:
ds Formula:
~ satisfaction_rank 1 + officer_rate_f +
bonus_pay
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9244122 | 0.1132714 | 25.817747 | 0.0000000 |
| officer_rate_f4 | 0.2934115 | 0.1267040 | 2.315724 | 0.0208064 |
| officer_rate_f5 | 0.6032909 | 0.1408814 | 4.282261 | 0.0000206 |
| officer_rate_f6 | 0.8478220 | 0.1620620 | 5.231467 | 0.0000002 |
| bonus_pay | 0.0000158 | 0.0000051 | 3.072155 | 0.0021917 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0720318 | 0.0677406 | 1.311901 | 16.786 | 0 | 4 | -1468.155 | 2948.309 | 2976.92 | 1488.738 | 865 | 870 |
Data:
ds Formula:
~ satisfaction_rank 1 +
officer_rate_f * bonus_pay
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.6738295 | 0.1412220 | 18.933520 | 0.0000000 |
| officer_rate_f4 | 0.9876528 | 0.2498733 | 3.952615 | 0.0000836 |
| officer_rate_f5 | 0.8154021 | 0.3806246 | 2.142274 | 0.0324510 |
| officer_rate_f6 | 1.1783927 | 0.4842708 | 2.433334 | 0.0151628 |
| bonus_pay | 0.0000345 | 0.0000081 | 4.232110 | 0.0000256 |
| officer_rate_f4:bonus_pay | -0.0000374 | 0.0000115 | -3.245845 | 0.0012162 |
| officer_rate_f5:bonus_pay | -0.0000172 | 0.0000157 | -1.096670 | 0.2730919 |
| officer_rate_f6:bonus_pay | -0.0000222 | 0.0000213 | -1.042449 | 0.2974956 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0832837 | 0.0758394 | 1.30619 | 11.18754 | 0 | 7 | -1462.848 | 2943.696 | 2986.612 | 1470.687 | 862 | 870 |
Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + officer_rate_f + bonus_pay Model 2:
satisfaction_rank ~ 1 + officer_rate_f * bonus_pay Res.Df RSS Df Sum of
Sq F Pr(>F) 1 865 1488.7
2 862 1470.7 3 18.052 3.5268 0.01461
6.4 By Billet Current and Critical War
6.4.1 satisfaction_rank
Data:
ds Formula:
~ satisfaction_rank 1 + billet_current +
critical_war
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8377622 | 0.1180247 | 32.5166107 | 0.0000000 |
| billet_currentAdministrative | -0.2015313 | 0.1975770 | -1.0200137 | 0.3080071 |
| billet_currentOCONUS MTF | -0.4370929 | 0.1425983 | -3.0652035 | 0.0022428 |
| billet_currentCONUS Operational | -0.7312263 | 0.1359483 | -5.3787096 | 0.0000001 |
| billet_currentOCONUS Operational | -1.3975988 | 0.2150434 | -6.4991486 | 0.0000000 |
| billet_currentOther | 0.1509960 | 0.4418948 | 0.3417011 | 0.7326588 |
| critical_warLow Deployer | 0.0112418 | 0.1235363 | 0.0910001 | 0.9275136 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0731976 | 0.0667689 | 1.313958 | 11.38609 | 0 | 6 | -1471.894 | 2959.789 | 2997.955 | 1493.409 | 865 | 872 |
6.5 By Bonus_pay and Manning_proportion
6.5.1 satisfaction_rank
Data:
ds Formula:
~ satisfaction_rank 1 +
manning_proportion_cut3 + bonus_pay_cut3
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.4680836 | 0.1647969 | 14.976514 | 0.0000000 |
| manning_proportion_cut3Balanced | 0.3928698 | 0.1259855 | 3.118373 | 0.0018786 |
| manning_proportion_cut3Over | 0.4651373 | 0.1209922 | 3.844358 | 0.0001297 |
| bonus_pay_cut3$20-24k | 1.0172580 | 0.1493703 | 6.810311 | 0.0000000 |
| bonus_pay_cut3$24k+ | 0.8796862 | 0.1546106 | 5.689687 | 0.0000000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0642006 | 0.0598832 | 1.318796 | 14.87015 | 0 | 4 | -1476.106 | 2964.213 | 2992.838 | 1507.907 | 867 | 872 |
No interaction between manning_proportion_cut3 &
bonus_pay_cut3Analysis of Variance Table
2 867 1507.9 -4 -8.1779 1.1765 0.3197
Data:
ds
Formula:
~ satisfaction_rank 1 + billet_current +
critical_war
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8377622 | 0.1180247 | 32.5166107 | 0.0000000 |
| billet_currentAdministrative | -0.2015313 | 0.1975770 | -1.0200137 | 0.3080071 |
| billet_currentOCONUS MTF | -0.4370929 | 0.1425983 | -3.0652035 | 0.0022428 |
| billet_currentCONUS Operational | -0.7312263 | 0.1359483 | -5.3787096 | 0.0000001 |
| billet_currentOCONUS Operational | -1.3975988 | 0.2150434 | -6.4991486 | 0.0000000 |
| billet_currentOther | 0.1509960 | 0.4418948 | 0.3417011 | 0.7326588 |
| critical_warLow Deployer | 0.0112418 | 0.1235363 | 0.0910001 | 0.9275136 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0731976 | 0.0667689 | 1.313958 | 11.38609 | 0 | 6 | -1471.894 | 2959.789 | 2997.955 | 1493.409 | 865 | 872 |
6.6 By Rank and Billet Type
6.6.1 satisfaction_rank
Conculsion:officer_rate has a significant
positive slope –sig predicting beyond billet_current. But
the billet levels have the same slope.Data:
ds_no_other_or_unknown Formula:
~ satisfaction_rank 1
+ billet_current + officer_rate
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.3990535 | 0.2023713 | 11.854711 | 0.0000000 |
| billet_currentAdministrative | -0.5251624 | 0.1972434 | -2.662510 | 0.0079021 |
| billet_currentOCONUS MTF | -0.4583431 | 0.1377637 | -3.327024 | 0.0009153 |
| billet_currentCONUS Operational | -0.6710109 | 0.1320160 | -5.082800 | 0.0000005 |
| billet_currentOCONUS Operational | -1.3770516 | 0.2076934 | -6.630213 | 0.0000000 |
| officer_rate | 0.3440959 | 0.0462720 | 7.436374 | 0.0000000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1298574 | 0.1247569 | 1.26896 | 25.45982 | 0 | 5 | -1420.469 | 2854.939 | 2888.229 | 1373.551 | 853 | 859 |
Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current Model 2:
satisfaction_rank ~ 1 + billet_current + officer_rate Res.Df RSS Df Sum
of Sq F Pr(>F) 1 854 1462.6
2 853 1373.5 1 89.047 55.3 2.521e-13 Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current + officer_rate Model
2: satisfaction_rank ~ 1 + billet_current * officer_rate Res.Df RSS Df
Sum of Sq F Pr(>F) 1 853 1373.5
2 849 1365.3 4 8.2102 1.2763 0.2776 
7 Ordinal Models
7.1 1-predictor ordinal model: billet
Data:
ds_no_other_or_unknown Formula:
~
ordered(satisfaction_rank) billet_current
| term | estimate | std.error | statistic | coef.type |
|---|---|---|---|---|
| billet_currentAdministrative | -0.1598301 | 0.2847549 | -0.5612903 | coefficient |
| billet_currentOCONUS MTF | -0.5408170 | 0.1975592 | -2.7374939 | coefficient |
| billet_currentCONUS Operational | -0.9174521 | 0.1903276 | -4.8203829 | coefficient |
| billet_currentOCONUS Operational | -1.8141170 | 0.3021047 | -6.0049280 | coefficient |
| 1|2 | -2.3551770 | 0.1224293 | -19.2370433 | scale |
| 2|3 | -1.6150921 | 0.1001752 | -16.1226674 | scale |
| 3|4 | -0.7152669 | 0.0844094 | -8.4737827 | scale |
| 4|5 | 0.3971137 | 0.0821859 | 4.8318944 | scale |
| edf | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|
| 8 | -1262.722 | 2541.445 | 2579.5 | 2525.445 | 852 | 860 |
7.2 1-predictor ordinal model: offier rate
Data:
ds_no_other_or_unknown Formula:
~
ordered(satisfaction_rank) officer_rate
| term | estimate | std.error | statistic | coef.type |
|---|---|---|---|---|
| officer_rate | 0.4972550 | 0.0666400 | 7.4618072 | coefficient |
| 1|2 | 0.0181495 | 0.2869621 | 0.0632469 | scale |
| 2|3 | 0.7415462 | 0.2826536 | 2.6235159 | scale |
| 3|4 | 1.6279511 | 0.2856359 | 5.6993933 | scale |
| 4|5 | 2.7581936 | 0.2963643 | 9.3067684 | scale |
| edf | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|
| 5 | -1259.639 | 2529.279 | 2553.058 | 2519.279 | 854 | 859 |
7.3 1-predictor ordinal model: specialty
Data:
ds_no_other_or_unknown Formula:
~
ordered(satisfaction_rank) specialty_type
| term | estimate | std.error | statistic | coef.type |
|---|---|---|---|---|
| specialty_typesurgical | -0.3465013 | 0.1714193 | -2.021366 | coefficient |
| specialty_typefamily | -0.7839712 | 0.1673401 | -4.684897 | coefficient |
| specialty_typeoperational | -1.6647223 | 0.2223020 | -7.488563 | coefficient |
| specialty_typeresident | -0.6376696 | 0.3092219 | -2.062175 | coefficient |
| 1|2 | -2.4995064 | 0.1323535 | -18.885079 | scale |
| 2|3 | -1.7510557 | 0.1114135 | -15.716735 | scale |
| 3|4 | -0.8437125 | 0.0965107 | -8.742168 | scale |
| 4|5 | 0.2778043 | 0.0924397 | 3.005250 | scale |
| edf | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|
| 8 | -1257.197 | 2530.395 | 2568.45 | 2514.395 | 852 | 860 |
7.4 3-predictor ordinal model
Call:
MASS::polr(formula = ordered(satisfaction_rank) ~ billet_current +
officer_rate + specialty_type, data = ds_no_other_or_unknown)
Coefficients:
Value Std. Error t value
billet_currentAdministrative -0.6155 0.29779 -2.067
billet_currentOCONUS MTF -0.5507 0.20021 -2.751
billet_currentCONUS Operational -0.5241 0.23051 -2.274
billet_currentOCONUS Operational -1.6041 0.31659 -5.067
officer_rate 0.4628 0.07274 6.363
specialty_typesurgical -0.3982 0.17324 -2.299
specialty_typefamily -0.5966 0.17200 -3.469
specialty_typeoperational -0.8514 0.27879 -3.054
specialty_typeresident -0.3633 0.32237 -1.127
Intercepts:
Value Std. Error t value
1|2 -0.7832 0.3277 -2.3899
2|3 0.0048 0.3214 0.0149
3|4 0.9711 0.3213 3.0225
4|5 2.1614 0.3285 6.5795
Residual Deviance: 2442.231
AIC: 2468.231
(77 observations deleted due to missingness)
Data:
ds_no_other_or_unknown Formula:
~
ordered(satisfaction_rank) billet_current + officer_rate +
specialty_type
| term | estimate | std.error | statistic | coef.type |
|---|---|---|---|---|
| billet_currentAdministrative | -0.6155252 | 0.2977934 | -2.0669541 | coefficient |
| billet_currentOCONUS MTF | -0.5507024 | 0.2002124 | -2.7505908 | coefficient |
| billet_currentCONUS Operational | -0.5240958 | 0.2305119 | -2.2736173 | coefficient |
| billet_currentOCONUS Operational | -1.6041125 | 0.3165904 | -5.0668382 | coefficient |
| officer_rate | 0.4628157 | 0.0727351 | 6.3630323 | coefficient |
| specialty_typesurgical | -0.3982413 | 0.1732421 | -2.2987560 | coefficient |
| specialty_typefamily | -0.5965928 | 0.1719977 | -3.4686098 | coefficient |
| specialty_typeoperational | -0.8513858 | 0.2787879 | -3.0538833 | coefficient |
| specialty_typeresident | -0.3633346 | 0.3223665 | -1.1270854 | coefficient |
| 1|2 | -0.7831514 | 0.3276899 | -2.3899166 | scale |
| 2|3 | 0.0047825 | 0.3214328 | 0.0148786 | scale |
| 3|4 | 0.9710647 | 0.3212775 | 3.0225112 | scale |
| 4|5 | 2.1613841 | 0.3285025 | 6.5795063 | scale |
| edf | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|
| 13 | -1221.116 | 2468.231 | 2530.056 | 2442.231 | 846 | 859 |
8 Models to Publish
8.1 3-predictor (and final) model
Data:
ds_no_other_or_unknown Formula:
~
satisfaction_rank 1 + billet_current + officer_rate + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.7449416 | 0.2216697 | 12.3830259 | 0.0000000 |
| billet_currentAdministrative | -0.4449198 | 0.1985010 | -2.2413979 | 0.0252580 |
| billet_currentOCONUS MTF | -0.4035686 | 0.1380693 | -2.9229432 | 0.0035595 |
| billet_currentCONUS Operational | -0.3935332 | 0.1549743 | -2.5393446 | 0.0112834 |
| billet_currentOCONUS Operational | -1.1452059 | 0.2149465 | -5.3278645 | 0.0000001 |
| officer_rate | 0.2955084 | 0.0491857 | 6.0080167 | 0.0000000 |
| specialty_typesurgical | -0.2655425 | 0.1202900 | -2.2075190 | 0.0275446 |
| specialty_typefamily | -0.4094283 | 0.1188588 | -3.4446620 | 0.0005998 |
| specialty_typeoperational | -0.6634897 | 0.1855377 | -3.5760380 | 0.0003686 |
| specialty_typeresident | -0.1987343 | 0.2359798 | -0.8421669 | 0.3999317 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1506291 | 0.1416252 | 1.256672 | 16.72926 | 0 | 9 | -1410.092 | 2842.184 | 2894.498 | 1340.762 | 849 | 859 |
Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current + officer_rate Model
2: satisfaction_rank ~ 1 + billet_current + officer_rate +
specialty_type Res.Df RSS Df Sum of Sq F Pr(>F) 1 853 1373.5
2 849 1340.8 4 32.789 5.1907 0.0003911 
8.2 3-predictor model weight sampling weights
Data:
ds_no_other_or_unknown Formula:
~
satisfaction_rank 1 + billet_current + officer_rate + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.5672108 | 0.2427369 | 10.5761050 | 0.0000000 |
| billet_currentAdministrative | -0.4852311 | 0.2196834 | -2.2087744 | 0.0274568 |
| billet_currentOCONUS MTF | -0.2645507 | 0.1663384 | -1.5904370 | 0.1121086 |
| billet_currentCONUS Operational | -0.2267988 | 0.1489326 | -1.5228285 | 0.1281740 |
| billet_currentOCONUS Operational | -1.0832312 | 0.2116975 | -5.1168828 | 0.0000004 |
| officer_rate | 0.3307020 | 0.0525848 | 6.2889212 | 0.0000000 |
| specialty_typesurgical | -0.2780756 | 0.1284607 | -2.1646746 | 0.0306907 |
| specialty_typefamily | -0.4330843 | 0.1660709 | -2.6078272 | 0.0092719 |
| specialty_typeoperational | -0.7198543 | 0.1616156 | -4.4541130 | 0.0000096 |
| specialty_typeresident | -0.1449924 | 0.1527102 | -0.9494615 | 0.3426561 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1853017 | 0.1766653 | 2.545062 | 21.45595 | 0 | 9 | -1528.984 | 3079.968 | 3132.282 | 5499.261 | 849 | 859 |
8.3 Billet Intercepts
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
8.4 Specialty Intercepts
8.5 Nonsignificant Additions
The `officer_rate * specialty_type` interaction doesn't sig improve the fit of the modelAnalysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type
Model 2: satisfaction_rank ~ 1 + billet_current + officer_rate * specialty_type
Res.Df RSS Df Sum of Sq F Pr(>F)
1 849 1340.8
2 845 1336.7 4 4.0491 0.6399 0.6341`manning_proportion_cut3` doesn't sig improve the fit of the modelAnalysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type
Model 2: satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type +
manning_proportion_cut3
Res.Df RSS Df Sum of Sq F Pr(>F)
1 849 1340.8
2 847 1339.9 2 0.88131 0.2786 0.7569`bonus_pay_cut4` doesn't sig improve the fit of the modelAnalysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type
Model 2: satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type +
bonus_pay_cut4
Res.Df RSS Df Sum of Sq F Pr(>F)
1 849 1340.8
2 846 1336.5 3 4.2181 0.89 0.44589 Session Information
For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.
Environment
─ Session info ───────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 Patched (2023-03-29 r84127 ucrt)
os Windows 10 x64 (build 22621)
system x86_64, mingw32
ui RTerm
language (EN)
collate English_United States.utf8
ctype English_United States.utf8
tz America/Chicago
date 2023-04-06
pandoc 2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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──────────────────────────────────────────────────────────────────────────────────────────────────Report rendered by wibea at 2023-04-06, 18:04 -0500 in 7 seconds.