Attention check

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

att_1 n
0 2
1 198

Demographics

Race

race N Perc
asian 16 8.08
black 41 20.71
hispanic 3 1.52
multiracial 6 3.03
white 128 64.65
NA 4 2.02

Gender

gender N Perc
1 0.51
man 91 45.96
woman 104 52.53
NA 2 1.01

Age

age_mean age_sd
36.87113 9.993204

Education

edu N Perc
noHS 1 0.51
GED 29 14.65
2yearColl 23 11.62
4yearColl 104 52.53
MA 30 15.15
PHD 8 4.04
NA 3 1.52

Income

Employment

employment N Perc
1 0.51
Full-time 161 81.31
Full-time, Student 1 0.51
Homemaker 1 0.51
Part-time 28 14.14
Part-time, Student 3 1.52
Part-time, Temporarily laid off 1 0.51
Permanently disabled 1 0.51
Student 1 0.51

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 109 55.05
nondom 89 44.95

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 103
dom skip-task 6
nondom do-task 61
nondom skip-task 28

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 = 0.83

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

term estimate conf.int statistic df p.value
Intercept 3.57 [2.76, 4.37] 8.74 196 < .001
CWV 0.26 [-0.01, 0.52] 1.91 196 .057

With controls

term estimate conf.int statistic df p.value
Intercept 3.92 [2.41, 5.44] 5.11 182 < .001
CWV 0.30 [0.03, 0.56] 2.20 182 .029
Age 0.02 [-0.01, 0.04] 1.47 182 .143
Gender man -0.17 [-0.68, 0.34] -0.66 182 .507
Race white -0.56 [-1.10, -0.03] -2.08 182 .039
Income num -0.09 [-0.20, 0.01] -1.74 182 .083
Edu num -0.06 [-0.34, 0.21] -0.46 182 .647

Model 2

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

Without controls

term estimate conf.int statistic df p.value
Intercept 0.42 [0.19, 0.64] 3.62 196 < .001
CWV 0.05 [-0.03, 0.12] 1.24 196 .218

With controls

term estimate conf.int statistic df p.value
Intercept 0.32 [-0.11, 0.75] 1.46 182 .145
CWV 0.04 [-0.03, 0.12] 1.09 182 .278
Age 0.00 [0.00, 0.01] 1.10 182 .271
Gender man 0.03 [-0.12, 0.17] 0.34 182 .734
Race white -0.11 [-0.26, 0.04] -1.42 182 .158
Income num -0.03 [-0.06, 0.00] -1.95 182 .052
Edu num 0.04 [-0.03, 0.12] 1.12 182 .264

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.

term estimate conf.int statistic df p.value
Intercept 0.55 [0.49, 0.61] 17.56 194 < .001
Scaleattitude dom 0.21 [0.14, 0.27] 6.21 194 < .001
Scalecomp dom 0.06 [0.00, 0.13] 1.85 194 .066

With controls

term estimate conf.int statistic df p.value
Intercept 0.35 [-0.01, 0.72] 1.90 180 .060
Scaleattitude dom 0.19 [0.11, 0.26] 5.18 180 < .001
Scalecomp dom 0.08 [0.01, 0.14] 2.16 180 .032
Age 0.00 [0.00, 0.01] 0.66 180 .513
Gender man 0.05 [-0.08, 0.18] 0.72 180 .475
Race white -0.05 [-0.19, 0.09] -0.67 180 .504
Income num -0.02 [-0.05, 0.01] -1.55 180 .122
Edu num 0.06 [-0.01, 0.14] 1.71 180 .089

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 = 0.26 (p = 0.057)
b = 0.13 (p = 0)
direct = 0.05 (p = 0.218)
indirect = 0.01 (p = 0.682)

With controls

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

term estimate conf.int statistic df p.value
Intercept 0.28 [0.06, 0.50] 2.47 196 .014
CWV 0.10 [0.02, 0.17] 2.62 196 .009

With controls

term estimate conf.int statistic df p.value
Intercept 0.23 [-0.19, 0.66] 1.07 182 .285
CWV 0.10 [0.02, 0.17] 2.52 182 .013
Age 0.00 [0.00, 0.01] 1.17 182 .245
Gender man -0.05 [-0.20, 0.09] -0.72 182 .474
Race white -0.11 [-0.26, 0.04] -1.50 182 .136
Income num -0.02 [-0.05, 0.01] -1.33 182 .184
Edu num 0.03 [-0.05, 0.11] 0.73 182 .468

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.

term estimate conf.int statistic df p.value
Intercept 0.27 [0.18, 0.36] 6.00 194 < .001
Nompred dom 0.51 [0.38, 0.63] 8.28 194 < .001
Scalecomp dom 0.08 [0.02, 0.14] 2.51 194 .013

With controls

term estimate conf.int statistic df p.value
Intercept 0.19 [-0.15, 0.53] 1.12 180 .264
Nompred dom 0.51 [0.38, 0.63] 7.86 180 < .001
Scalecomp dom 0.07 [0.01, 0.14] 2.33 180 .021
Age 0.00 [0.00, 0.01] 0.55 180 .580
Gender man 0.05 [-0.08, 0.17] 0.74 180 .459
Race white -0.06 [-0.19, 0.07] -0.90 180 .367
Income num -0.02 [-0.04, 0.01] -1.35 180 .180
Edu num 0.03 [-0.04, 0.10] 0.89 180 .374

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 = 0.1 (p = 0.009)
b = 0.53 (p = 0)
direct = 0.05 (p = 0.218)
indirect = -0.01 (p = 0.874)

With controls