1. Cyberbullying in a College Setting: Applying General Strain Theory
Matheson Sanchez
Mentor: Gang Lee, PhD
Prepared for the 2015 CJAG Annual Meeting

2. Cyberbullying
“When the internet, cell phones, or other devices are used to send or post text or images intended to hurt or embarrass another person” (National Crime Prevention Council)
Constantly evolving
Possible severe outcomes

3. General Strain Theory (GST)
Agnew (1992)
Strain in the form of lack of positive stimuli, loss of positive stimuli, or presence of negative stimuli increases chance of criminal or delinquent coping
Update in 2001
Certain types of strain are more likely to lead to criminal or delinquent coping than others

4. Trends in Cyberbullying and GST
Juveniles
College Students
Previous bullying victimization is tied to future cyberbullying behavior
Strain is a significant predictor of cyberbullying behavior
“Aging in” of youths during the middle school years
“Aging out” during the high school years
Previous bullying victimization is tied to future cyberbullying behavior
Males associate higher levels of strain with being cyberbullied than do females

5. Anonymity and Cyberbullying
Affords cyberbullies a unique advantage
Protection from authorities
Protection from victim retaliation
Is tied to higher levels of cyberbullying behavior
Is tied to greater perceived severity of both traditional and cyberbullying incidences

6. Hypotheses
H1: College students who experience higher levels of academic strain are not more likely to engage in cyberbullying activities than students who experience lower levels of academic strain.
H2: College students who experience higher levels of financial strain are more likely to engage in cyberbullying behavior than students who experience lower levels of financial strain.
H3: College students who experience higher levels of environmental strain are more likely to engage in cyberbullying behavior than students who experience lower levels of environmental strain.
H4: The amount of personal information disclosed online has a moderating effect on the relationship between academic, financial, and environmental strain and cyberbullying behavior in college students.

7. Methodology Multi stage cluster sample
Partial sample of 9 courses at Kennesaw State University
In-person questionnaires
N = 277

8. Table 1 Summary of Study Variables (N=277)%
Race (N=255)
White
158
62.0
African-American 62
24.3
Hispanic 20
7.8
Asian 15
5.9
Gender (N=275)
Male
116
42.2
Female
159
57.8
Age (N=273) 18 55
20.1 19 35
12.8 52
19.0 21 43
15.8 22 36
13.2
23 or above
19.1
College (N=277)
Humanities 51
18.4
Science and Math 45
16.2
Education 23
8.3
Health and Human Services 76
27.4
Arts
0.0
Business 28
10.1
Engineering 31
11.2
Computing and Software
Architecture
Disclosed Information (N=270)
Low
155
57.4
High
115
42.6
Minimum
Maximum
Mean
Std. dev.
Cyberbullying Behavior (out of 12) (N=272)
3.0
10.0
4.0
1.543
Academic Strain (out of 16) (N=272)
15.0
7.1
2.222
Financial Strain (out of 12) (N=276)
12.0
8.5
2.325
Environmental Strain (out of 16) (N=272)
16.0
7.2
1.850

9. Table 2 Bivariate Correlation of Study Variables1 2 3 4 5 6 7 8 9 10
1. Cyberbullying Behavior
1.00
2. Academic Strain
.117
3. Financial Strain
.125*
.144*
4. Environmental Strain
-.039
.304**
-.011
5. Disclosed Information
.296**
.070
.192**
.067
6. Gender
-.215**
-.156*
.062
.119
.036
7. Age
-.064
.053
.018
-.118
8. White
.078
-.004
-.119
-.005
.028
-.144*
9. African-American
-.092
.019
.102
-.054
-.017
.074
.140*
-.723**
10. Hispanic
.008
-.024
.003
.073
.031
-.067
.044
-.372**
-.165**
11. Asian
.001
.055
.026
-.063
-.049
-.009
-.319**
-.142*
-.073
**p < .01
*p < .05

10. High Disclosed Information .665 .206 3.230 .001 Gender (Female) -.780
Table 3 OLS Regression of Study Variables with Cyberbullying Behavior Dependent Variable
Variable B
Std. Error
Beta t p
Constant
3.800
.895
4.246
.000
Academic Strain
.025
.049
.036
.518
.605
Financial Strain
.088
.045
.129
1.924
.056
Environmental Strain
-.035
.059
-.041
-.597
.551
High Disclosed Information
.665
.206
3.230
.001
Gender (Female)
-.780
.243
-.242
-3.205
.002
Age
-.001
.038
-.002
-.024
.981
Race (Ref = White)
African-American
-.314
.246
-.085
-1.279
.202
Hispanic
-.078
.397
-.013
-.196
.845
Asian
-.255
.433
-.038
-.589
.557
College (Ref = Humanities)
Science and Mathematics
.006
.338
.016
.987
Education
.424
-.031
.975
Health and Human Services
-.383
.311
-.110
-1.232
.219
Business
.030
.444
.067
.947
Engineering
-.062
.047
-.102
-1.301
.195
Computing and Software
-.440
-.076
-.990
.323
R2=.153, df = 15, p = .001

11. Low Disclosed Information High Disclosed Information Variable B Beta t
Table 4 OLS Regression of Study Variables with Cyberbullying Behavior Dependent Variable by Amount of Personal Information Disclosed
Low Disclosed Information
High Disclosed Information
Variable B
Beta t
Constant
3.914
3.833
3.543
2.038
Academic Strain
.016
.027
.301
.009
.011
.085
Financial Strain
.055
.090
1.038
.160
.216
1.872
Environmental Strain
.030
.039
.440
-.056
-.060
.623
Gender
-.784
-.290**
-2.648
-.891
-.242*
-2.048
Age
-.013
-.031
-.313
.031
.048
.376
Race (white=ref)
African American
-.363
-.118
-1.321
-1.90
-.045
-.397
Hispanic
-.847
-.156
-1.779
.823
.132
1.206
Asian
-.165
-.030
-.354
-.242
-.274
College (humanities=ref)
Science and Mathematics
.195
.053
.514
-.325
-.065
-.524
Education
-.032
-.007
-.067
-.280
-.040
-.345
Health and Human Services
-.291
-.097
-.815
-.552
-.142
-.936
Business
-.400
-.096
-.843
1.111
1.201
Engineering
-.063
-.119
-1.128
-.146
-1.062
Computing and Software
.068
.015
.147
-2.055
-.252
-2.051
Low Disclosed Information: R2=.152, df = 14, p = .089
High Disclosed Information: R2=.202, df = 14, p = .146
**p < .01
*p < .05

12. Discussion and Conclusions
None of the three measured types of strain were significant predictors of cyberbullying behavior
Once full dataset is available for analysis, it is anticipated that financial strain will be a significant predictor
The amount of personal information disclosed online did not appear to moderate the relationship between strain and cyberbullying behavior
Gender is the only variable to remain a significant predictor of cyberbullying behavior through every step of the analysis
Males more likely to engage in cyberbullying activities