Assessment of the labeling theory, in particular the effects it has on middle and high school
students.
Introduction
Labeling theory is one of the most important approaches to understanding deviant and criminal
behavior. Deviance is in the eye of the beholder, while the labeling theory is the view of
deviance according to which being labeled as a ‘deviant’ leads a person to engage in deviant
behavior. In today’s society worldwide, labeling as well as deviance goes hand in hand, they are
both common practices in our culture. Without labeling an individual or a group, deviance would
not exist, because deviance and the deviant emerge is out of a continuous process of interaction
among people. Like deviance, immorality is also in the eye of the beholder. Immorality is the
quality of not being in accord with standards of right or good conduct based on the society an
individual pertains to (Clarke 2008). The correlation between immorality as well as the labeling
theory is evident. The labeling theory is the view of deviant behavior according to which being
labeled as “deviant” leads a person to engage in deviant behavior, which in retrospect is
immortal (Clarke 2008). Labeling is predominant in our society, specifically in middle and high
schools. These middle and high school institutions are the ones that create an individual’s minds,
goals, future, as well as character (Clarke 2008).
Moreover, there are several aspects to take into consideration when analyzing the labeling theory
and its effects on middle and high school students. According to Lowe, May and Elrod (2008)
labeling is geared towards gender and race. While Case and Lingerfelt (2013) focus on the
aftermath of the labeling theory, and exactly what occurs after someone has been labeled in
society. On the other hand, Akiba et al (2003) explains the importance of the region one is from
in determining how labeling a middle or high school student affects their lifestyles. Watkins and
Aber (2009) concentrate their study on how the relationship of race, class, and gender correlate
with the school racial climate observations. Peguero (2011) focuses his study on the violence,
schools, and dropping out rates, specifically the racial and ethnic disparities in the education
consequence of student victimization. Arcia (2007) demonstrates how the suspension rates
between elementary and middle school students relate to their race and gender. All six articles
incorporate the affects of the aftermath of labeling a student, which is the dependent variable.
Furthermore, the independent variables that will be affecting the dependent variable are gender,
race, region, as well as education attainment.
Literature Review
Gender and Race
Lowe, May, and Elrod (2008) focus on the theoretical predictors of delinquency among a sample
of rural and small town youths in public schools in a mid-southern state. The researchers focus
on existing theories and look at them in depth in three different ways. Firstly, they look at
theoretical predictors derived from social disorganizations and control theories, learning theories,
and staring, culture conflict, as well as critical theories. Secondly, they examine the salience of
these theoretical predictors within and outside of these school environments. Last but not least,
they look at the correlation between the genders to identify if the same predictors are significant
in males as well as females. Lowe, May, and Elrod (2008) hypothesize that the influence of
theoretical predictors on delinquency differs by both the gender and social framework. This
particular study looks at samples based on 496 middle school students, grades six to eight, and
858 high school students, grades nine to twelve, giving this study a total of 1,354 students
(Lowe, May, and Elrod 2008). Moreover, the research methods used by the authors are surveys
as well as questionnaires where they took the point of views of males and females and compared
the two, and in retrospect came up with the conclusion that supported their initial hypothesis.
Moreover, Watkins and Aber (2009) concentrate their study on how the relationship of race,
class, and gender correlate with the school racial climate observations. The two authors examine
the race, socioeconomic class, and gender and explore the relationships among race, class,
gender, and middle school students’ perceptions of school racial climate. There is no definite
theory within this research; however, there are a few hypotheses. The first hypothesis is that
students’ views of racial climate varied depending on the type of race you are, the class you
pertained to, or the gender you are. This then is broken down into specific racial groups. For
example, female students and poor African Americans professed that the racial climate is more
undesirable compared to a white, non-poor, male correspondent. Moreover, the non-poor Whites
viewed a more positive racial climate compared to the non-poor African American students
(Watkins and Aber 2009).
Lowe et al. (2008) particular study looks at samples based on an individual basis, with a sample
size of 842 African American and White students. Furthermore, the data that is gathered portrays
that there are two fully endogenous dependent variables and several independent variable in this
study. The independent variables are delinquent peer influences, attachments to school, selfreport victimization, family strain-noxious stimuli, and seriousness of school problems. These
variables were operationalized by a series of questions within each factor. The two dependent
variables are predictors of out of school delinquency among male and female respondents as well
as in school delinquency among male and female respondents (Lowe et al. 2008). This particular
study looks at samples based on a group level and this is a longitudinal study. The sample
population was taken from both grade and high schools with 1,354 participants (Lowe et al.
2008). After conducting this specific study with both surveys and questionnaires, the results
show that the findings were consistent with the existing research on theoretical predictors of
delinquency. The results show that the perspective maintained by social disorganization and
social control theorists that the stronger youth’s attachment to school, the less likely he/she will
participate in delinquency. Results also show that students with higher levels of victimization
were more likely to participate in delinquency (Lowe, et al. 2008). Watkins and Aber (2009)
particular study looks at samples based on an individual basis, with a sample size of 842 African
American and White students. The units of analysis are broken down further into 30% African
American, 70% white, 47% male, 53% female, 31% on free or reduced price lunch, and 69%
paying full price for lunch (Watkins and Aber 2009). The type of research method used by the
authors in this case is both surveys as well as experiments. Also, to ensure that possible bias is
eliminated and that the study sample was relatively representative in terms of demographics, they
randomly choose these specific students. Moreover, there are three different dependent variables.
These include: race, gender, and socioeconomic class. The dependent variable race is
operationalized by either black/African American or white were included in analyses and
students who identified as biracial or multiracial were excluded from analyses. The dependent
variable of socioeconomic class is operationalized by lunch status, specifically the means by
which students pay for school lunch and the students specified whether they paid full price for
lunch or received subsidies. The dependent variable gender is operationalized by what the
students marked them as, male or female. While the independent variable, is racial climate. This
specific independent variable is operationalized by the school climate survey which measures
middle school students’ perceptions of different aspects of school social climate with an
emphasis on racial climate. As tabulated by Watkins and Aber (2009) results show that African
Americans female students and poor African Americans professed that the racial climate is more
undesirable compared to a white, non-poor, male correspondent and that the non-poor Whites
viewed a more positive racial climate compared to the non-poor African American students.
These results prove that their original hypothesis is correct.
Case and Lingerfelt (2013) concentrate on the labeling theory in detail. The two authors examine
the deviant behaviors based on labeling an individual, specifically the affiliation among labeling
in social work and level of professional education, socioeconomic status of clients, and type of
presenting issue. Furthermore, the definite hypothesis in this article is that labeling rises with
education. Peguero (2011) focuses his study on the violence, schools, and dropping out rates,
specifically the racial and ethnic disparities in the education consequence of student
victimization. The researcher emphasizes the relationship between the exposure to violence and
victimization and the well-being and development of all youth. The general topic portrayed in
this article is the suspension rates between elementary and middle school students. There is no
direct theory in this research; however, the hypothesis is that across different racial groups,
different aspects like ethnicity, achievement, and suspension history play into effect and in turn
middle schools have a higher suspension rates compared to those students in elementary/K-8
schools (Arcia 2007). Case and Lingerfelt (2013) particular study looks at samples based on an
individual basis, with a sample size of 203. The units of this analysis are broken down into three
separate groups: 34 professional social workers, 125 students enrolled in a graduate school of
social work, and 44 undergraduates enrolled in an introductory social work course in a large state
university (Case and Lingerfelt 2013). The type of research method used by the authors in this
case is both questionaries’ as well as experiments. Also, to ensure that possible bias is eliminated
due to the order of presentation, the vignettes were methodically ordered so that each sample
group saw them in an altered arrangement. Moreover, there is a dependent variable and one
significant independent variable in this research. The dependent variable is the educational level
attained. The dependent variable, educational level, is operationalized by undergraduate students
enrolled in an introductory social work course in large state university, graduate students of
social work including sixty five first year students and sixty second year students, and
professional workers comprising of the highest educational level, were practitioners were
employed by family service, child guidance, and public welfare agencies. While the independent
variable, socioeconomic level is operationalized by high socioeconomic status and low
socioeconomic status. As tabulated by Case and Lingerfelt (2013) results show labeling scores
differed considerably across levels of education; however, no significant difference was found in
the core result of socioeconomic status of labelee or its interactional result with educational level
on extent of labeling. Also, it was hypothesized that individuals with a mental disorder would be
labeled more harshly than the other individuals with other types of problems. The results prove
the hypothesis to be correct, that the individuals with a mental disorder experience labeling to a
greater degree. Furthermore, the units of analysis for this particular study are sixth-grade
students enrolled in a regular education school. This sample size is further broken down into
students who are in the following academic year and are passed onto the seventh grade, leaving
the sample size to 26,137 students (Arcia 2007). Also, the dimension of this study is
longitudinal. This specific study uses results from state’s standardized achievement tests for both
years and the type of research method used by the authors in this case are descriptive statistics
and archival. The dependent variable is race, specifically Latino, Black, or White. Arcia (2007)
operationalizes race by what the students reported. The independent variable is the demographic
and educational characteristics. Arcia (2007) operationalizes the independent variable by
participation in free or reduced lunch program, limited English proficiency, and exceptional
student education. In turn, Arcia (2007) conceptualizes her independent variables on another
level. For limited English proficiency and exceptional student education students in that
particular district are required to take an administered standardized competency based reading
and mathematics test as well as a standardized nationally normed reading and mathematics
achievement test. On the other hand, in order to conceptualize free or reduced lunch programs
Arcia (2007), each student is assigned to fill out a form regarding their lunch status, and takes
that into consideration for her research. As formulated by Arcia (2007) the results indicate that
suspension rates were found to be higher for Black students than for Latino students. Peguero
(2011) hypothesizes that Black/African Americans and Latino American students who are
victimized at school are at higher risk of dropping out. This particular study looks at samples
based on Waves 1 and 3 of a 2002 ELS which is a longitudinal survey administered by the
Research Triangle Institute (RTI) for the National Center for Education Statistics of the U.S.
Department of Education. This archival data contains information about not only the experiences
and backgrounds of students, but also their parents, and teachers, as well as the description of the
schools that student has attended. Furthermore, the original sample size for this study was 15,360
students; however, to eliminate certain biases like smaller ethnic groups (Native Americans and
Multiracial); the study included only 12,030 public school students in the base year from ELS
2002 sample (Peguero 2011). This in turn limited the sample size to those students who
recognized themselves only as Black/African American, Latino American, Asian American, and
White American. Moreover, the research methods used by the author are archival data as well as
experiments. Furthermore, the data that is gathered portrays that there is one significant
dependent variable, which is dropping out of school. Since, there are many debates regarding
how to measure and calculate dropout rates, Peguero (2011) operationalizes this dependent
variable by using a universal consensus of GED’s. He identifies GED’s are not being equivalent
because GED holders more closely resemble high school dropouts than high school graduates. In
addition to the dependent variable, there are two different independent variables. The two
independent variables are exposure to violence and victimization at school. Exposure to violence
and victimization at school is operationalized by three items someone threatened to hurt me at
school, someone hit me, and someone used strong-arm or forceful methods to get money or
things from me. The measure of victimization is dichotomized to specify whether or not the
student was visible to violence or mistreated at school (Peguero 2011). Results show that
approximately 13% of the sample dropped out of school, and roughly 33% of the sample
experienced at least one form of victimization at school. Peguero (2011) also concluded that
there is a significant role in racial and ethnic disparities among dropping out of school and
exposure to victimization at school. Specifically, the fact that the average Latino American and
Black/African American student drops out of school at a higher rate than White American
students. Also, that Asian Americans and Latino Americans have lower rates of victimization at
school, compared to White American students. All five articles demonstrate the significance race
and gender play in labeling middle and high school students.
Region
The general topic portrayed in this article is student victimization and the national and school
system effects on school violence in 37 nations. There is no direct theory in this research;
however, the hypothesis is that the national patterns of school violence are not strongly related to
general patterns of violence or lack of social integration in society (Akiba et al 2003).
Furthermore, the units of analysis for this particular study are groups of students from seventh
and eighth grade and the dimension is cross-sectional. Also this particular study used results
from TIMSS survey data in order to explore the amount of school violence among the 37 nations
in the study, ascertain whether the traditional national level predictors of crimes and delinquency
explain cross national variations in school violence, as well as to test whether factors related to
the educational system are associated with levels of school violence cross nationally (Akiba et al
2003). The type of research method used by the authors in this case is surveys, archival, as well
as questionnaires. Moreover, the independent variable is school violence. Akiba et al (2003)
operationalizes school violence as the level of victimization of violence among social groups by
asking these two questions: “How often did you think another student might hurt you in the last
month of school?” and “How often have some of your friends gotten hurt by other students?” On
the other hand the dependent variables are broken down into two categories the school
characteristics and the national characteristics. The school characteristics are school
effectiveness which is operationalized by national math achievement and achievement variation
and the high academic competition which is operationalized by high-stakes testing and shadow
educational system. While the national characteristics which are absolute and relative deprivation
are operationalized by GDP per capita and Gini coefficient of income inequality and age
distribution which is operationalized by percentage aged 15 to 29, and also social integration
which is operationalized by divorce rate and percentage linguistic minority (Akiba et al 2003).
As tabulated by Akiba et al (2003) the results show that school violence is widely prevalent
among the 37 nations studied, school violence rates are not related to general crimes rates in
these nations, school violence rates are related to some social indicators such as absolute
deprivation and age distribution but not to others such as income inequality or social integration,
school violence rates are related with school system variables and the effect of these variables is
independent of social variables, and last but not least when school system variables are
controlled for many social variables become non-significant. This article portrays the correlation
between region and the labeling theory of middle and high school students.
Research and Methods
Methods
I conducted a survey to assess how the labeling theory affects middle and high school students.
The particular survey consisted of ten demographic questions and fifteen survey questions
regarding being labeled and the negative effects of it. This in turn allowed me to better
understand the severity of labeling at a personal level. However, if I were to conduct this survey
in the perfect world, my population size would be 2,250 participants. My sample size then would
be broken down into about 750 high school students, 750 middle school students, and 750 staff
members that are in middle and high school. This would give me a better understanding of
everyone’s perspective, and eliminate bias because I will have a bigger sample pool. Also, I
would be conducting a probability sampling method, this will allow me to give every student or
staff member in the middle or high school an equal chance of being picked to participate, and
eliminate biases towards who is chosen. I would also use a stratified sample, which is a mini
reproduction of the population. This is a type of probability sample, and will again allow me to
eliminate as many biases as possible. Furthermore, I will be conducting an online survey with
one question being on each page and a NEXT button option. This in retrospect will allow the
survey taker to focus on each question without any confusion or distraction to another question.
The survey will also start off with ten demographic questions for general purposes, and then
continue on to the other twenty survey questions. Also, another important aspect to consider
while making a survey is the length of the survey. I made mine only 25 questions long, this
enables the survey taker to quickly but thoroughly go through the survey without it taking a lot
of their time. Please disregard the empty spaces on the bottom of each page because this is an
online survey.
1. Please indicate your highest level of education. (Check only one that applies)
0 Less than High School
0 High School Diploma
0 Some College
0 Associates/Technical Degree
0 Bachelors Degree
0 Masters Degree
0 Professional or Doctoral Degree
0 Prefer Not to Answer
2. What is your sex? (Check only one that applies)
0 Male
0 Female
0 Intersex
0 Prefer Not to Answer
3. What state do you currently live in?
(This will be a drop box with an option of all 50 states)
4. How long have you been living in this state? (Check only one that applies)
0 Less than 9 years
0 10-19 years
0 20-29 years
0 30-39 years
0 More than 40 years
0 Prefer Not to Answer
0 Don’t Know
5. What is your ethnicity? (Check only one that applies)
0 African American
0 Asian American
0 Hispanic or Latino Decent
0 Native American
0 Non-Hispanic Caucasian
0 Two or More Races
0 Prefer Not to Answer
6. What is your age? (Check only one that applies)
0 Under 12 years old
0 12-17 years old
0 18-24 years old
0 25-24 years old
0 35-44 years old
0 45-54 years old
0 55-64 years old
0 65-74 years old
0 75 years or older
0 Prefer Not to Answer
7. What is your marital status? (Check only one that applies)
0 Single
0 Married
0 Separated
0 Divorced
0 Widowed
0 Prefer Not to Answer
8. Which of the following best describes the area you live in? (Check only one that applies)
0 Urban
0 Suburban
0 Rural
0 Prefer Not to Answer
0 Don’t Know
9. How many children under 16 years old live in your household? (Check only one that
applies)
0 None
01
02
03
0 4 or More
0 Prefer Not to Answer
10. What is your current household income in United States dollars? (Check only one that
applies)
0 Under $10,000
0 $10,000 to $19,999
0 $20,000 to $29,999
0 $30,000 to $39,999
0 $40,000 to $49,999
0 $50,000 to $74,999
0 $75,000 to $99,999
0 $100,000 to $150,000
0 Over $150,000
0 Prefer Not to Answer
0 Don’t Know
11. Have you ever been physically abused? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
12. Have you ever been verbally abused? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
13. Have you ever been bullied by your peers? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
If you answered Yes for question number 13, go to question number 14.
If you answered No for question number 13, skip ahead to question number 15.
If you answered Prefer Not To Answer for question number13, go to question number 14.
14. Have you ever been called a name other than your own? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
If you answered Yes for question number 14, go to question number 15 and question number1 6.
If you answered No for question number 14, skip ahead to question number 17.
If you answered Prefer Not To Answer for question number 14, go to question number17.
15.) If you have been called a name other than your own, was it a bad name? (Check only
one that applies)
0 Yes
0 No
0 Prefer Not To Answer
16.) If you have been called a name other than your own, was it a good name? (Check only
one that applies)
0 Yes
0 No
0 Prefer Not To Answer
17. Have you ever bullied anyone? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
If you answered Yes for question number 17, go to question number 18.
If you answered No for question number 17, skip ahead to question number 19.
If you answered Prefer Not To Answer for question number 17, go to question number 19.
18. How many times have you bullied someone in the past 6 months? (Check only one that
applies)
0 1 to 3
0 4 to 6
0 7 to 9
0 10 to 12
0 13 to 15
0 More than 15
0 Don’t Know
19. Have you ever witnessed bullying? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
If you answered Yes for question number 19, go to question number 20.
If you answered No for question number 19, skip ahead to question number 21.
If you answered Prefer Not To Answer for question number 19, go to question number 21.
20. How many times have you witnessed bullying in the past 6 months? (Check only one that
applies)
0 1 to 3
0 4 to 6
0 7 to 9
0 10 to 12
0 13 to 15
0 More than 15
0 Don’t Know
21. Have you been placed into a specific group at your school? (Check only one that applies)
0 Yes
0 No
0 Prefer Not To Answer
0 Don’t Know
22. How many of your teachers have taken class time to discuss what to do if you see
bullying?
(Check only one that applies)
0 Zero Teachers
0 One Teacher
0 Two Teachers
0 Three Teachers
0 Four Teachers
0 Five Teachers
0 Six Teachers
0 Seven Teachers
0 More than Seven Teachers
23. How many of your teachers have taken class time to discuss ways to avoid bullying?
(Check only one that applies)
0 Zero Teachers
0 One Teacher
0 Two Teachers
0 Three Teachers
0 Four Teachers
0 Five Teachers
0 Six Teachers
0 Seven Teachers
0 More than Seven Teachers
24. How many of your teachers have taken class time to discuss how students feel when
they are bullied? (Check only one that applies)
0 Zero Teachers
0 One Teacher
0 Two Teachers
0 Three Teachers
0 Four Teachers
0 Five Teachers
0 Six Teachers
0 Seven Teachers
0 More than Seven Teachers
25. Do you think bullying during middle and high school years effects students after high
school? (Check only one that applies)
0 Yes
0 No
0 Prefer Not to Answer
0 Don’t Know
Works Cited
Akiba, Motoko, Gerald K. LeTendre, David P. Parker, and Brian Goesling. 2003. “Student
Victimization: National and School System Effects on School Violence in 37 Nations.”
American Educational Research Association 39(4):829-853.
Arcia, Emily. 2007. “A Comparison of Elementary/K-8 and Middle Schools' Suspension Rates.”
Urban Education 42(5):456-469.
Case, Lois P. and Neveryln B. Lingerfelt. 2013. “Name-calling: The Labeling Process in the
Social Work Interview.” Social Service Review 48(1):75-86.
Clarke, Edward. 2008. “Deviant Behavior.” Social Service Review 7:(1) 28-54.
Lowe, Nathan C., David C. May, and Preston Elrod. 2008. "Theoretical Predictors of
Delinquency among Public School Students in a Mid-Southern State." Youth Violence and
Juvenile Justice 6(4):343-362.
Peguero, Anthony A. 2011. “Violence, Schools, and Dropping Out: Racial and Ethnic Disparities
in the Educational Consequence of Student Victimization.” Journal of Interpersonal Violence
26(18):3753-3772.
Watkins, Natasha D. and Mark S. Aber. 2009. “Exploring the Relationships Among Race, Class,
Gender, and Middle School Students’ Perceptions of School Racial Climate.” Equity and
Excellence in Education 42(4):395-411.