Attention check

First, let’s see who passed the attention check.

att_1 n
0 6
1 194

Demographics

Race

race N Perc
asian 8 4.12
black 39 20.10
hispanic 8 4.12
multiracial 11 5.67
white 127 65.46
NA 1 0.52

Gender

gender N Perc
man 100 51.55
woman 92 47.42
NA 2 1.03

Age

age_mean age_sd
36.32292 9.23741

Education

edu N Perc
GED 43 22.16
2yearColl 22 11.34
4yearColl 90 46.39
MA 28 14.43
PHD 9 4.64
NA 2 1.03

Income

Employment

employment N Perc
Full-time 150 77.32
Homemaker 2 1.03
Part-time 40 20.62
Part-time, Student 1 0.52
Unemployed 1 0.52

Measures

Message chosen

Which of the following messages do you wish to send to your employee?

dom: Your job in this task is to select the shapes that match the description. Make sure you look at them carefully. If you don’t complete the task and do it well, I may not be able to give you the full bonus.
nondom: Your job in this task is to select the shapes that match the description. Make sure you look at them carefully.

message_choice N Perc
dom 111 57.22
nondom 83 42.78

Predicted selection

Do you think your employee will choose to do the task and have a chance for a bonus of up to $1, depending on your choice? Or will they opt to skip the task and the chance for a bonus?

message_choice choice_dom N
dom do-task 110
dom skip-task 1
nondom do-task 60
nondom skip-task 23

Predicted Performance

If they choose to do the task after receiving your message, how well will they perform? Their performance can range from 0 points up to 50 points (for perfect performance). Split here by message selection.

Predicted Attitude

What will be the impact of this message on your employee’s attitude towards you? Split here by which message was selected.

Predicted nomination

Do you think your employee will recommend you as a participant in this “good manager” paid follow-up survey?

Punish decision

For each level of their performance below, please indicate what bonus you would like them to receive. You can select between 0 and 100 cents for each. 100 cents is the full $1.00.

Competitive Worldview

1 = Strongly Disagree to 7 = Strongly Agree

1. It’s a dog-eat-dog world where you have to be ruthless at times
2. Life is not governed by the “survival of the fittest.” We should let compassion and moral laws be our guide [R]
3. There is really no such thing as “right” and “wrong.” It all boils down to what you can get away with
4. One of the most useful skills a person should develop is how to look someone straight in the eye and lie convincingly
5. It is better to be loved than to be feared [R]
6. My knowledge and experience tell me that the social world we live in is basically a competitive “jungle” in which the fittest survive and succeed, in which power, wealth, and winning are everything, and might is right
7. Do unto others as you would have them do unto you, and never do anything unfair to someone else [R]
8. Basically people are objects to be quietly and coolly manipulated for one’s own benefit
9. Honesty is the best policy in all cases [R]
10. One should give others the benefit of the doubt. Most people are trustworthy if you have faith in them [R]

Cronbach’s alpha = 0.81

Correlations

is_dom: dummy-coded message chosen (0 = nondom; 1 = dom).
attitude: predicted impact of the message on employee’s attitude towards the manager.
pred_nom: predicted nomination for “good maanger” survey
comp: score the employee will get in the task if sent this message.

Analysis

Model 1

Linear regression: Competitive worldview as a predictor variable; expected relationship impact of dominant message as an outcome variable.

Without controls

(#tab:unnamed-chunk-19)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 3.73 [2.97, 4.50] 9.65 192 < .001
CWV 0.27 [0.03, 0.52] 2.20 192 .029

With controls

(#tab:unnamed-chunk-20)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 3.51 [2.11, 4.92] 4.93 179 < .001
CWV 0.20 [-0.05, 0.45] 1.56 179 .121
Age 0.00 [-0.02, 0.03] 0.36 179 .717
Gender man 0.26 [-0.20, 0.73] 1.13 179 .262
Race white -0.46 [-0.95, 0.02] -1.88 179 .062
Income num -0.09 [-0.20, 0.01] -1.71 179 .088
Edu num 0.24 [0.00, 0.47] 1.99 179 .048

Model 2

Linear regression: Competitive worldview as a predictor variable; binary choice of dominant message as an outcome variable.

Without controls

(#tab:unnamed-chunk-21)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.39 [0.15, 0.63] 3.22 192 .002
CWV 0.06 [-0.01, 0.14] 1.60 192 .112

With controls

(#tab:unnamed-chunk-22)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.36 [-0.08, 0.81] 1.63 179 .105
CWV 0.06 [-0.02, 0.14] 1.50 179 .135
Age 0.00 [-0.01, 0.01] -0.47 179 .635
Gender man 0.07 [-0.07, 0.22] 1.01 179 .314
Race white -0.08 [-0.23, 0.07] -1.02 179 .308
Income num 0.00 [-0.03, 0.04] 0.28 179 .778
Edu num 0.03 [-0.05, 0.10] 0.70 179 .487

Model 3

Linear regression: Expected relationship impact of dominant message as a predictor variable; binary choice of dominant message as an outcome variable; expected compliance impact of dominant message as a control variable.

(#tab:unnamed-chunk-23)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.57 [0.51, 0.63] 18.84 191 < .001
Scaleattitude dom 0.23 [0.17, 0.30] 7.27 191 < .001
Scalecomp dom 0.07 [0.01, 0.13] 2.25 191 .026

With controls

(#tab:unnamed-chunk-24)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.57 [0.23, 0.91] 3.34 178 .001
Scaleattitude dom 0.22 [0.16, 0.29] 6.53 178 < .001
Scalecomp dom 0.07 [0.00, 0.14] 2.06 178 .041
Age 0.00 [-0.01, 0.00] -0.73 178 .469
Gender man 0.04 [-0.08, 0.17] 0.66 178 .508
Race white -0.02 [-0.15, 0.12] -0.22 178 .825
Income num 0.01 [-0.02, 0.04] 0.87 178 .388
Edu num 0.01 [-0.06, 0.07] 0.23 178 .816

Model 4

Mediation model: Competitive worldview as a predictor variable; expected relationship impact of dominant message as a mediator; binary choice of dominant message as an outcome variable.

Without controls

a = 0.27 (p = 0.029)
b = 0.16 (p = 0)
direct = 0.06 (p = 0.112)
indirect = 0.02 (p = 0.577)

With controls

Call: psych::mediate(y = is_dom ~ CWV + (attitude_dom) + (comp_dom) + age + gender_man + race_white + income_num + edu_num, data = df_recd_elg)

Direct effect estimates (traditional regression) (c’) X + M on Y is_dom se t df Prob Intercept -0.57 0.25 -2.26 185 2.50e-02 CWV 0.04 0.04 1.15 185 2.50e-01 age 0.00 0.00 -0.79 185 4.30e-01 gender_man 0.03 0.06 0.51 185 6.10e-01 race_white 0.00 0.07 -0.02 185 9.84e-01 income_num 0.01 0.01 0.77 185 4.44e-01 edu_num 0.01 0.03 0.43 185 6.70e-01 attitude_dom 0.14 0.02 6.47 185 8.60e-10 comp_dom 0.01 0.00 2.47 185 1.44e-02

R = 0.54 R2 = 0.3 F = 9.68 on 8 and 185 DF p-value: 3.52e-11

Total effect estimates (c) (X on Y) is_dom se t df Prob Intercept 0.35 0.22 1.60 187 0.111 CWV 0.05 0.04 1.19 187 0.236 age 0.00 0.00 -0.54 187 0.590 gender_man 0.08 0.07 1.06 187 0.288 race_white -0.06 0.08 -0.81 187 0.420 income_num 0.00 0.02 -0.26 187 0.797 edu_num 0.05 0.04 1.30 187 0.195

‘a’ effect estimates (X on M) attitude_dom se t df Prob Intercept 3.49 0.70 5.01 187 1.25e-06 CWV 0.17 0.13 1.35 187 1.79e-01 age 0.00 0.01 0.34 187 7.33e-01 gender_man 0.26 0.23 1.16 187 2.48e-01 race_white -0.42 0.24 -1.75 187 8.22e-02 income_num -0.12 0.05 -2.22 187 2.79e-02 edu_num 0.29 0.11 2.53 187 1.23e-02 comp_dom se t df Prob Intercept 45.82 3.78 12.11 187 2.65e-25 CWV -1.73 0.68 -2.54 187 1.19e-02 age 0.00 0.07 -0.05 187 9.60e-01 gender_man 0.88 1.23 0.71 187 4.77e-01 race_white -0.24 1.31 -0.18 187 8.57e-01 income_num 0.06 0.28 0.23 187 8.19e-01 edu_num -0.66 0.62 -1.08 187 2.82e-01

‘b’ effect estimates (M on Y controlling for X) is_dom se t df Prob attitude_dom 0.14 0.02 6.47 185 8.60e-10 comp_dom 0.01 0.00 2.47 185 1.44e-02

‘ab’ effect estimates (through all mediators) is_dom boot sd lower upper CWV 0.01 0.01 0.02 -0.04 0.05 age 0.00 0.00 0.00 -0.04 0.05 gender_man 0.04 0.04 0.04 -0.04 0.05 race_white -0.06 -0.06 0.04 -0.04 0.05 income_num -0.02 -0.02 0.01 -0.04 0.05 edu_num 0.03 0.03 0.02 -0.04 0.05

‘ab’ effects estimates for each mediator for is_dom boot sd lower upper CWV 0.01 0.02 -0.04 0.05 age 0.00 0.00 0.00 0.00 gender_man 0.04 0.04 -0.03 0.12 race_white -0.06 0.04 -0.14 0.01 income_num -0.02 0.01 -0.03 0.00 edu_num 0.03 0.02 -0.01 0.08 attitude_domCWV 0.02 0.02 -0.01 0.06 attitude_domage 0.00 0.00 0.00 0.00 attitude_domgender_man 0.04 0.03 -0.03 0.10 attitude_domrace_white -0.06 0.03 -0.13 0.00 attitude_domincome_num -0.02 0.01 -0.03 0.00 attitude_domedu_num 0.04 0.02 0.01 0.08 comp_domCWV -0.02 0.01 -0.04 0.00 comp_domage 0.00 0.00 0.00 0.00 comp_domgender_man 0.01 0.01 -0.02 0.03 comp_domrace_white 0.00 0.01 -0.03 0.02 comp_domincome_num 0.00 0.00 -0.01 0.01 comp_domedu_num -0.01 0.01 -0.02 0.00

Exploratory Analysis

“Good manager” survey

All models from the analysis plan, but, instead of the continuous expected relationship impact measure, we will insert the binary expected recommendation to add the manager as a participant in the “good manager” follow-up.

Model 1

Linear regression: Competitive worldview as a predictor variable; expected nomination in follow-up for dominant message as outcome.

Without controls

(#tab:unnamed-chunk-28)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.44 [0.21, 0.67] 3.74 192 < .001
CWV 0.07 [-0.01, 0.14] 1.74 192 .084

With controls

(#tab:unnamed-chunk-29)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.22 [-0.21, 0.66] 1.03 179 .306
CWV 0.06 [-0.02, 0.14] 1.46 179 .145
Age 0.00 [-0.01, 0.01] 0.52 179 .603
Gender man 0.10 [-0.04, 0.24] 1.44 179 .153
Race white -0.02 [-0.17, 0.13] -0.26 179 .794
Income num -0.01 [-0.04, 0.03] -0.41 179 .684
Edu num 0.04 [-0.03, 0.11] 1.10 179 .273

Model 2

Linear regression: expected nomination in follow-up for dominant message as a predictor variable; binary choice of dominant message as an outcome variable; expected compliance impact of dominant message as a control variable.

(#tab:unnamed-chunk-30)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.13 [0.05, 0.22] 3.12 191 .002
Nompred dom 0.69 [0.59, 0.80] 12.96 191 < .001
Scalecomp dom 0.06 [0.01, 0.11] 2.29 191 .023

With controls

(#tab:unnamed-chunk-31)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.24 [-0.03, 0.51] 1.74 178 .084
Nompred dom 0.71 [0.60, 0.82] 13.13 178 < .001
Scalecomp dom 0.06 [0.01, 0.11] 2.19 178 .030
Age 0.00 [-0.01, 0.00] -1.21 178 .228
Gender man 0.00 [-0.10, 0.10] 0.06 178 .950
Race white -0.06 [-0.17, 0.04] -1.22 178 .224
Income num 0.01 [-0.02, 0.03] 0.53 178 .599
Edu num 0.01 [-0.04, 0.06] 0.33 178 .741

Model 4

Mediation model: Competitive worldview as a predictor variable; expected nomination in follow-up for dominant message as a mediator; binary choice of dominant message as an outcome variable.

Without controls

a = 0.07 (p = 0.084)
b = 0.72 (p = 0)
direct = 0.06 (p = 0.112)
indirect = 0.01 (p = 0.601)

With controls