Moral Diversity Predictor
Participants had to respond to the following options:
How morally diverse is your workplace? In other words, in terms of the moral composition of your work environment, how many people have similar (or different) moral priorities?
1 = Everybody shares the same moral priorities.
2 = Almost everybody shares the same moral priorities.
3 = Many people share the same moral priorities.
4 = Only a few people share the same moral priorities, everybody else’s
moral priorities differ from each other.
5 = Very few people share the same priorities.
Measures Block
Now, please answer the following questions about your workplace.
Cultural Looseness.
There are many social norms that people should abide by in this
group.
In this group, there are very clear expectations for how people should
act in most situations.
People agree upon what behaviors are appropriate versus inappropriate in
most situations in this group.
People in this group have a great deal of freedom in deciding how they
want to behave in most situations.
In this group, if someone acts in an inappropriate way, others will
strongly disapprove.
People in this group almost always comply with social norms.
Norm violation perceptions
Sometimes, employees in organizations can engage in problematic
behaviors (such as the ones below). We would look to know, in terms of
the behaviors below, how much you agree with them:
People…
act condescendingly to each other Pay little attention to
each other or show little interest in your opinion
Make demeaning or derogatory comments to each other
Address each other in unprofessional terms, either publicly or
privately Doubt each other’s judgement on a matter over which
they have responsibility Make unwanted attempts to draw each
other into a discussion of personal matters
(1 = strongly disagree, 2 = disagree, 3 = neither disagree or agree,
4 = agree, and 5 = strongly agree)
These behaviors…
are not acceptable in the team/organization.
not appropriate in the team/organization.
not be expected in the team/organization.
should not have occurred in the team/organization.
the shared behavioral standards of the team/organization
Positive gossip.
How frequently do the following behaviors occur in your
organization?
(1 = never, 2 = seldom, 3 = sometimes, 4 = often, and 5 = always)
Complimented a colleague’s actions while talking to others
Told others good things about a colleague
Defended a colleague’s actions while talking to others
Said something nice about a colleague while talking to others
Told others that you respect a colleague
Negative gossip
How frequently do the following behaviors occur in your
organization?
(1 = never, 2 = seldom, 3 = sometimes, 4 = often, and 5 = always)
Asked others if they have a negative impression of something that a
colleague has done
Questioned a colleague’s abilities while talking to others
Criticized a colleague while talking to others
Vented to others about something that a colleague has done
Told an unflattering story about a colleague while talking to others
Punishment
In my organization, I try to…
make sure other people act morally.
make sure people are held accountable when they do something
wrong.
ensure that people who do something wrong get punished for it.
enforce society’s rules.
make sure people who do wrong don’t get away with it.
Monitoring
In my organization, I try to…
(1 = Not at all, 4 = Sometimes, 7 = Very often)
stay alert for signs of corruption or wrongdoing
be vigilant for signs of injustice in society.
actively monitors others to see if they are following society’s
rules.
take it upon themselves to monitor others for signs of wrongdoing.
actively detects signs of wrongdoing.
Organizational Citizenship Behavior
In my organization, I try to…
(1 = Not at all, 4 = Sometimes, 7 = Very often)
seek opportunities to do good in the community
go out of my way to help those who need it
willingly give my time to help others
adjusts my schedule to accommodate others
go out of the way to make others feel better
shows genuine concern and courtesy toward others, even under the most
challenging situations
gives up time to help others who are experiencing various
problems.
seeks opportunities to help those who are struggling right now
Self-reported moral composition of participant’s workplace
##
## 1. Everybody same 2. Almost everybody same 3. Many people same
## 0.04188482 0.29842932 0.52356021
## 4. Only a few people same 5. Very few people same
## 0.11518325 0.02094241Graphs
Main Effects
Controls
I controlled for the following:
- Participant’s tenure in the organization. - Participant’s power in the
organization.
- The size of the organization.
- Participant’s gender.
- Participant’s age.
- Participant’s race.
Moderation
Condition by Tightness on Punishing
Controls
summary(lm(punish~cond_short*tightness+tenure+ladder_participant+OrgSize+gender+age+race, mdp1_clean))##
## Call:
## lm(formula = punish ~ cond_short * tightness + tenure + ladder_participant +
## OrgSize + gender + age + race, data = mdp1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.00657 -1.14247 -0.00436 1.16660 3.07767
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -1.69544 4.19405 -0.404
## cond_short2. Almost everybody same 6.71487 4.34563 1.545
## cond_short3. Many people same 5.41044 4.15210 1.303
## cond_short4. Only a few people same 3.39718 4.36361 0.779
## cond_short5. Very few people same 16.52267 6.50446 2.540
## tightness 1.36534 0.68724 1.987
## tenure 0.04386 0.02068 2.121
## ladder_participant -0.08903 0.05441 -1.636
## OrgSize -0.05019 0.05894 -0.852
## gender -0.10933 0.21117 -0.518
## age -0.01885 0.01040 -1.813
## race1,2 0.41335 1.10696 0.373
## race1,3 0.86953 1.54101 0.564
## race1,3,5 2.75725 1.58068 1.744
## race2 0.65476 0.61067 1.072
## race2,3 -1.85544 1.55228 -1.195
## race3 0.21156 0.46296 0.457
## race4 0.28371 0.46216 0.614
## race7 0.35576 1.55621 0.229
## cond_short2. Almost everybody same:tightness -1.29582 0.74547 -1.738
## cond_short3. Many people same:tightness -1.15704 0.70444 -1.642
## cond_short4. Only a few people same:tightness -0.60017 0.78483 -0.765
## cond_short5. Very few people same:tightness -3.70096 1.47236 -2.514
## Pr(>|t|)
## (Intercept) 0.6865
## cond_short2. Almost everybody same 0.1242
## cond_short3. Many people same 0.1943
## cond_short4. Only a few people same 0.4374
## cond_short5. Very few people same 0.0120 *
## tightness 0.0486 *
## tenure 0.0354 *
## ladder_participant 0.1036
## OrgSize 0.3956
## gender 0.6053
## age 0.0716 .
## race1,2 0.7093
## race1,3 0.5733
## race1,3,5 0.0829 .
## race2 0.2852
## race2,3 0.2337
## race3 0.6483
## race4 0.5401
## race7 0.8195
## cond_short2. Almost everybody same:tightness 0.0840 .
## cond_short3. Many people same:tightness 0.1024
## cond_short4. Only a few people same:tightness 0.4455
## cond_short5. Very few people same:tightness 0.0129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.524 on 168 degrees of freedom
## Multiple R-squared: 0.2225, Adjusted R-squared: 0.1207
## F-statistic: 2.185 on 22 and 168 DF, p-value: 0.002882Simple Slopes and Graphs
## SIMPLE SLOPES ANALYSIS
##
## Slope of tightness when cond_short = 5. Very few people same:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -2.34 1.29 -1.81 0.07
##
## Slope of tightness when cond_short = 1. Everybody same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 1.78 0.67 2.65 0.01
##
## Slope of tightness when cond_short = 4. Only a few people same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.66 0.37 1.78 0.08
##
## Slope of tightness when cond_short = 2. Almost everybody same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.02 0.28 0.08 0.93
##
## Slope of tightness when cond_short = 3. Many people same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.28 0.16 1.71 0.09
## Condition by Organization Size on Tightness ### Controls
##
## Call:
## lm(formula = tightness ~ cond_short * OrgSize + tenure + ladder_participant +
## OrgSize + gender + age + race, data = mdp1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8587 -0.4292 0.0000 0.5430 2.2691
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.620257 0.564507 9.956
## cond_short2. Almost everybody same -0.815372 0.533447 -1.528
## cond_short3. Many people same -1.346409 0.528948 -2.545
## cond_short4. Only a few people same -2.485960 0.590613 -4.209
## cond_short5. Very few people same -2.290864 0.865035 -2.648
## OrgSize 0.091823 0.153680 0.597
## tenure 0.013772 0.011586 1.189
## ladder_participant -0.027636 0.029663 -0.932
## gender 0.140595 0.117767 1.194
## age -0.002436 0.005807 -0.419
## race1,2 0.729552 0.622618 1.172
## race1,3 0.436295 0.858826 0.508
## race1,3,5 1.749106 0.867067 2.017
## race2 0.482517 0.336439 1.434
## race2,3 0.166563 0.866205 0.192
## race3 0.312223 0.258067 1.210
## race4 -0.183402 0.259586 -0.707
## race7 -0.212514 0.869117 -0.245
## cond_short2. Almost everybody same:OrgSize -0.032285 0.161928 -0.199
## cond_short3. Many people same:OrgSize 0.014087 0.161036 0.087
## cond_short4. Only a few people same:OrgSize 0.163341 0.172535 0.947
## cond_short5. Very few people same:OrgSize 0.124345 0.257430 0.483
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## cond_short2. Almost everybody same 0.12826
## cond_short3. Many people same 0.01181 *
## cond_short4. Only a few people same 4.16e-05 ***
## cond_short5. Very few people same 0.00886 **
## OrgSize 0.55098
## tenure 0.23621
## ladder_participant 0.35284
## gender 0.23421
## age 0.67541
## race1,2 0.24295
## race1,3 0.61211
## race1,3,5 0.04525 *
## race2 0.15337
## race2,3 0.84775
## race3 0.22802
## race4 0.48084
## race7 0.80713
## cond_short2. Almost everybody same:OrgSize 0.84221
## cond_short3. Many people same:OrgSize 0.93040
## cond_short4. Only a few people same:OrgSize 0.34514
## cond_short5. Very few people same:OrgSize 0.62970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8494 on 169 degrees of freedom
## Multiple R-squared: 0.2879, Adjusted R-squared: 0.1994
## F-statistic: 3.254 on 21 and 169 DF, p-value: 1.078e-05Simple Slopes and Graphs
## SIMPLE SLOPES ANALYSIS
##
## Slope of OrgSize when cond_short = 5. Very few people same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.24 0.21 1.16 0.25
##
## Slope of OrgSize when cond_short = 1. Everybody same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.10 0.15 0.64 0.52
##
## Slope of OrgSize when cond_short = 4. Only a few people same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.25 0.08 3.00 0.00
##
## Slope of OrgSize when cond_short = 2. Almost everybody same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.05 1.26 0.21
##
## Slope of OrgSize when cond_short = 3. Many people same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.09 0.04 2.08 0.04
Condition by Organization Size on Positive Gossip
Controls
##
## Call:
## lm(formula = pos_gos ~ cond_short * OrgSize + tenure + ladder_participant +
## OrgSize + gender + age + race, data = mdp1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.68252 -0.42769 0.09447 0.46789 1.68228
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.887074 0.532507 7.300
## cond_short2. Almost everybody same -0.007221 0.503207 -0.014
## cond_short3. Many people same -0.568985 0.498964 -1.140
## cond_short4. Only a few people same -1.573301 0.557134 -2.824
## cond_short5. Very few people same -0.425923 0.815999 -0.522
## OrgSize 0.057532 0.144969 0.397
## tenure 0.001357 0.010929 0.124
## ladder_participant -0.019967 0.027982 -0.714
## gender 0.298367 0.111091 2.686
## age -0.003307 0.005478 -0.604
## race1,2 0.571509 0.587324 0.973
## race1,3 -0.169673 0.810142 -0.209
## race1,3,5 1.089167 0.817916 1.332
## race2 -0.024530 0.317368 -0.077
## race2,3 0.400809 0.817102 0.491
## race3 0.010821 0.243438 0.044
## race4 -0.015116 0.244871 -0.062
## race7 -1.247985 0.819850 -1.522
## cond_short2. Almost everybody same:OrgSize -0.168932 0.152749 -1.106
## cond_short3. Many people same:OrgSize -0.041828 0.151907 -0.275
## cond_short4. Only a few people same:OrgSize 0.116523 0.162754 0.716
## cond_short5. Very few people same:OrgSize -0.165712 0.242837 -0.682
## Pr(>|t|)
## (Intercept) 1.08e-11 ***
## cond_short2. Almost everybody same 0.98857
## cond_short3. Many people same 0.25576
## cond_short4. Only a few people same 0.00531 **
## cond_short5. Very few people same 0.60238
## OrgSize 0.69198
## tenure 0.90136
## ladder_participant 0.47647
## gender 0.00796 **
## age 0.54689
## race1,2 0.33191
## race1,3 0.83436
## race1,3,5 0.18477
## race2 0.93848
## race2,3 0.62440
## race3 0.96460
## race4 0.95085
## race7 0.12982
## cond_short2. Almost everybody same:OrgSize 0.27032
## cond_short3. Many people same:OrgSize 0.78338
## cond_short4. Only a few people same:OrgSize 0.47501
## cond_short5. Very few people same:OrgSize 0.49592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8013 on 169 degrees of freedom
## Multiple R-squared: 0.1805, Adjusted R-squared: 0.07863
## F-statistic: 1.772 on 21 and 169 DF, p-value: 0.02504Simple Slopes and Graphs
## SIMPLE SLOPES ANALYSIS
##
## Slope of OrgSize when cond_short = 5. Very few people same:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.10 0.20 -0.52 0.61
##
## Slope of OrgSize when cond_short = 1. Everybody same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.14 0.53 0.60
##
## Slope of OrgSize when cond_short = 4. Only a few people same:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.16 0.08 2.08 0.04
##
## Slope of OrgSize when cond_short = 2. Almost everybody same:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.11 0.05 -2.12 0.04
##
## Slope of OrgSize when cond_short = 3. Many people same:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.01 0.04 -0.15 0.88
Condition by Participant’s Power on Tightness
Control
##
## Call:
## lm(formula = tightness ~ cond_short * ladder_participant + tenure +
## ladder_participant + OrgSize + gender + age + race, data = mdp1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8743 -0.4682 0.0000 0.5101 2.2440
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 6.097724 0.773116
## cond_short2. Almost everybody same -1.364924 0.837153
## cond_short3. Many people same -1.357095 0.795303
## cond_short4. Only a few people same -3.486416 0.928918
## cond_short5. Very few people same -3.570672 1.379722
## ladder_participant -0.084823 0.132613
## tenure 0.012258 0.011264
## OrgSize 0.108417 0.031045
## gender 0.104724 0.115842
## age -0.005538 0.005726
## race1,2 0.680713 0.604955
## race1,3 0.362133 0.847738
## race1,3,5 1.826286 0.856881
## race2 0.377041 0.336012
## race2,3 0.225931 0.864050
## race3 0.257951 0.254024
## race4 -0.320736 0.258773
## race7 -0.388260 0.859742
## cond_short2. Almost everybody same:ladder_participant 0.077417 0.145311
## cond_short3. Many people same:ladder_participant 0.011633 0.137927
## cond_short4. Only a few people same:ladder_participant 0.258191 0.155374
## cond_short5. Very few people same:ladder_participant 0.294850 0.236117
## t value Pr(>|t|)
## (Intercept) 7.887 3.66e-13 ***
## cond_short2. Almost everybody same -1.630 0.104872
## cond_short3. Many people same -1.706 0.089773 .
## cond_short4. Only a few people same -3.753 0.000240 ***
## cond_short5. Very few people same -2.588 0.010495 *
## ladder_participant -0.640 0.523282
## tenure 1.088 0.278044
## OrgSize 3.492 0.000611 ***
## gender 0.904 0.367269
## age -0.967 0.334854
## race1,2 1.125 0.262087
## race1,3 0.427 0.669795
## race1,3,5 2.131 0.034509 *
## race2 1.122 0.263410
## race2,3 0.261 0.794041
## race3 1.015 0.311339
## race4 -1.239 0.216899
## race7 -0.452 0.652135
## cond_short2. Almost everybody same:ladder_participant 0.533 0.594892
## cond_short3. Many people same:ladder_participant 0.084 0.932883
## cond_short4. Only a few people same:ladder_participant 1.662 0.098419 .
## cond_short5. Very few people same:ladder_participant 1.249 0.213485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8383 on 169 degrees of freedom
## Multiple R-squared: 0.3064, Adjusted R-squared: 0.2202
## F-statistic: 3.555 on 21 and 169 DF, p-value: 2.053e-06Simple Slopes and Graphs
Mediation (all with tightness as the mediator)
Note: I looked at DVs who were marginal after adding controls
DV: Norm violations
No controls: CI of indirect effect does NOT cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = n_vio ~ moraldiversity_corre + (tightness),
## data = mdp1_clean)
##
## The DV (Y) was n_vio . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on n_vio = 0.35 S.E. = 0.13 t = 2.69 df= 189 with p = 0.0079
## Direct effect (c') of moraldiversity_corre on n_vio removing tightness = 0.16 S.E. = 0.14 t = 1.16 df= 188 with p = 0.25
## Indirect effect (ab) of moraldiversity_corre on n_vio through tightness = 0.19
## Mean bootstrapped indirect effect = 0.19 with standard error = 0.06 Lower CI = 0.09 Upper CI = 0.31
## R = 0.31 R2 = 0.1 F = 9.97 on 2 and 188 DF p-value: 3.95e-06
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryWith controls: CI of indirect effect does NOT cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = n_vio ~ moraldiversity_corre + (tightness),
## data = mdp1_clean, z = c("tenure", "ladder_participant",
## "OrgSize", "gender", "age"))
##
## The DV (Y) was n_vio . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on n_vio = 0.35 S.E. = 0.13 t = 2.69 df= 189 with p = 0.0079
## Direct effect (c') of moraldiversity_corre on n_vio removing tightness = 0.16 S.E. = 0.14 t = 1.16 df= 188 with p = 0.25
## Indirect effect (ab) of moraldiversity_corre on n_vio through tightness = 0.19
## Mean bootstrapped indirect effect = 0.19 with standard error = 0.06 Lower CI = 0.08 Upper CI = 0.32
## R = 0.31 R2 = 0.1 F = 9.97 on 2 and 188 DF p-value: 3.95e-06
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryDV: Negative gossip
No controls: CI of indirect effect does cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = neg_gos ~ moraldiversity_corre + (tightness),
## data = mdp1_clean)
##
## The DV (Y) was neg_gos . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on neg_gos = 0.19 S.E. = 0.08 t = 2.38 df= 189 with p = 0.018
## Direct effect (c') of moraldiversity_corre on neg_gos removing tightness = 0.14 S.E. = 0.09 t = 1.62 df= 188 with p = 0.11
## Indirect effect (ab) of moraldiversity_corre on neg_gos through tightness = 0.05
## Mean bootstrapped indirect effect = 0.05 with standard error = 0.04 Lower CI = -0.02 Upper CI = 0.13
## R = 0.2 R2 = 0.04 F = 3.94 on 2 and 188 DF p-value: 0.00931
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryDV: Negative gossip - Crosses Zero
No controls: CI of indirect effect does cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = neg_gos ~ moraldiversity_corre + (tightness),
## data = mdp1_clean, z = c("tenure", "ladder_participant",
## "OrgSize", "gender", "age"))
##
## The DV (Y) was neg_gos . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on neg_gos = 0.19 S.E. = 0.08 t = 2.38 df= 189 with p = 0.018
## Direct effect (c') of moraldiversity_corre on neg_gos removing tightness = 0.14 S.E. = 0.09 t = 1.62 df= 188 with p = 0.11
## Indirect effect (ab) of moraldiversity_corre on neg_gos through tightness = 0.05
## Mean bootstrapped indirect effect = 0.05 with standard error = 0.04 Lower CI = -0.02 Upper CI = 0.13
## R = 0.2 R2 = 0.04 F = 3.94 on 2 and 188 DF p-value: 0.00931
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryDV: Punish
No controls: CI of indirect effect does NOT cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = punish ~ moraldiversity_corre + (tightness),
## data = mdp1_clean)
##
## The DV (Y) was punish . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on punish = -0.36 S.E. = 0.15 t = -2.46 df= 189 with p = 0.015
## Direct effect (c') of moraldiversity_corre on punish removing tightness = -0.23 S.E. = 0.16 t = -1.42 df= 188 with p = 0.16
## Indirect effect (ab) of moraldiversity_corre on punish through tightness = -0.14
## Mean bootstrapped indirect effect = -0.14 with standard error = 0.07 Lower CI = -0.29 Upper CI = 0
## R = 0.24 R2 = 0.06 F = 5.51 on 2 and 188 DF p-value: 0.00119
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryWith controls: CI of indirect effect does NOT cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = punish ~ moraldiversity_corre + (tightness),
## data = mdp1_clean, z = c("tenure", "ladder_participant",
## "OrgSize", "gender", "age"))
##
## The DV (Y) was punish . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on punish = -0.36 S.E. = 0.15 t = -2.46 df= 189 with p = 0.015
## Direct effect (c') of moraldiversity_corre on punish removing tightness = -0.23 S.E. = 0.16 t = -1.42 df= 188 with p = 0.16
## Indirect effect (ab) of moraldiversity_corre on punish through tightness = -0.14
## Mean bootstrapped indirect effect = -0.14 with standard error = 0.07 Lower CI = -0.29 Upper CI = -0.01
## R = 0.24 R2 = 0.06 F = 5.51 on 2 and 188 DF p-value: 0.00119
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryDV: OCBs
No controls: CI of indirect effect crosses zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = ocb ~ moraldiversity_corre + (tightness),
## data = mdp1_clean)
##
## The DV (Y) was ocb . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on ocb = -0.23 S.E. = 0.12 t = -1.9 df= 189 with p = 0.059
## Direct effect (c') of moraldiversity_corre on ocb removing tightness = -0.12 S.E. = 0.13 t = -0.93 df= 188 with p = 0.36
## Indirect effect (ab) of moraldiversity_corre on ocb through tightness = -0.11
## Mean bootstrapped indirect effect = -0.11 with standard error = 0.07 Lower CI = -0.26 Upper CI = 0.03
## R = 0.2 R2 = 0.04 F = 4.11 on 2 and 188 DF p-value: 0.0075
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryWith controls: CI of indirect effect crosses zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = ocb ~ moraldiversity_corre + (tightness),
## data = mdp1_clean, z = c("tenure", "ladder_participant",
## "OrgSize", "gender", "age"))
##
## The DV (Y) was ocb . The IV (X) was moraldiversity_corre . The mediating variable(s) = tightness .
##
## Total effect(c) of moraldiversity_corre on ocb = -0.23 S.E. = 0.12 t = -1.9 df= 189 with p = 0.059
## Direct effect (c') of moraldiversity_corre on ocb removing tightness = -0.12 S.E. = 0.13 t = -0.93 df= 188 with p = 0.36
## Indirect effect (ab) of moraldiversity_corre on ocb through tightness = -0.11
## Mean bootstrapped indirect effect = -0.11 with standard error = 0.07 Lower CI = -0.26 Upper CI = 0.03
## R = 0.2 R2 = 0.04 F = 4.11 on 2 and 188 DF p-value: 0.0075
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary