Posted: February 28th, 2023

Applying Social Psychology to Education

answer one of the following questions 1 through 4, and then everyone answer 5.  When answering 1-4, please state the question first so that we all know which one you are answering.

1. What are some of the factors affecting student performance identified in the Dweck presentation? and identify 4 connections from Dweck’s presentation to concepts noted in the ASP chapter on education.

2. Discuss the role that the stereotype threat might play in the development of a social identity.  What are some suggestions from Dr. Steele on how the stereotype threat might be combated? 

3. In the article by Simoni and Drentea, why do you think that higher SES parents were more likely to medicate their  child with ADHD?  What explanation was given by the article? Also compare the concept of “academic self concept” from ASP to the concept “academic ethic.”

4. What are some occupations not sex segregated? (that is close to 50% male/50% female).  Does it hold true when you look into sub-specialties or specific jobs?  Is there an earnings difference?

5. Reflecting on your personal academic experience from grade school through high school, which material spoke to you/resonated the most  and why?  Please use discretion if you choose to discuss personal problems (e.g. failing in school etc).

Video 1:

Video 2:

Video 3:

Gender in schools and concept of sex segregation

But First, let’s review the Difference bEtwEEn sex and gender
Sex-pertains to biological characteristics of male and female

Gender-the cultural meaning we attach to male and female

Gender is considered to be on a continuum, and many argue that sex is also on a continuum

Thorne’s idea of Gender Borderwork
from a symbolic interactionist framework-that is that interaction creates social reality
Thorne wanted to know the process of how children create gender

Based on Thorne’s work from the book Gender Play (1993), an ethnography about how boys and girls create borders around gender to differentiate and create hierarchy.

Gender Borderwork
Girls and boys assign sex to one another, and then create gender through interaction and “borderwork.”
Borderwork is enacted in child’s play
The borderwork creates divisions, and perpetuates prevailing stereotypes
They create sex-segregated spaces on the playground and in the classroom

How we create gender
Thorne and Martin argue that schools are a major place in which gender is created.

Karin Martin. Becoming a Gendered Body

As adults we also create gender
One area is in the classroom

Boys and girls treated differently
Beliefs about which sex is “best” at different areas. eg. girls are bad at math

Gender is produced by the daily interactions and then is maintained by these interactions. The effect of gendering has implications on jobs and ultimately earnings.

Sex segregation
This leads to sex segregation
Sex segregation then has real outcomes in life that create inequality
Example, a waitress in a diner-typically a woman’s job

Occupational segregation
Almost all jobs are gender segregated
Social scientists measure the %female or %male in the occupation.
Preschool teachers are about 99%female
Mechanics probably about 97% male
Psychology students are about 60-80% female
Psychology assistant professors are about 60% female
Psychology Full professors are about 20% female

Sex Segregation in sociology
Among sociologists, family sociologists are about 78% female, mathematical sociologists- about 20% female
This is a really good blog about sex segregation in sociology, written by a well-known sociologist, Philip Cohen.
It shows sex segregation of professors, reviewers, authors, award winners etc.

Gender segregated sociology today

What are some occupations not segregated?
Pediatrician 75% female
Surgeon-41% female

Often, when we compare subspecialties, we find differences, such as peds vs. surgery. There are large income discrepancies.








Full Terms & Conditions of access and use can be found at

Download by: [University of Cincinnati Libraries] Date: 30 May 2017, At: 13:52

Sociological Focus

ISSN: 0038-0237 (Print) 2162-1128 (Online) Journal homepage:

ADHD, Socioeconomic Status, Medication Use, and


Zachary R. Simoni & Patricia Drentea

To cite this article: Zachary R. Simoni & Patricia Drentea (2016) ADHD, Socioeconomic
Status, Medication Use, and Academic Ethic, Sociological Focus, 49:2, 119-132, DOI:

To link to this article:

Published online: 26 Feb 2016.

Submit your article to this journal

Article views: 258

View related articles

View Crossmark data

ADHD, socioeconomic status, medication use, and academic ethic
Zachary R. Simoni and Patricia Drentea

University of Alabama—Birmingham

This study examines the relationship between socioeconomic status and
the likelihood of receiving medication for Attention Deficit Hyperactivity
Disorder (ADHD) and then addresses the embodiment of the “ideal stu-
dent,” using the National Survey of Child’s Health. We find that the con-
struction of the ideal student and parents’ higher income is correlated with
higher odds of medication use for children and adolescents with ADHD.
Furthermore, our results imply that structural inequalities in the current
healthcare system increase the odds of upper-class children and adoles-
cents receiving medication for ADHD. We find evidence that both severity
of ADHD and younger ages increase the odds of receiving medication. We
conclude with a discussion of the academic ethic, upper-class life, and
future suggestions for research.

The number of children and adolescents diagnosed with Attention Deficit Hyperactivity Disorder
(ADHD) has increased within the last decades (American Psychiatric Association 2000; Castle et al.
2007; Cuffe, Moore, and McKeown 2005; Faraone and Biederman 2005). Consequently, use of
stimulant medication in the United States has increased. As of 2005, 70 percent of children and
adolescents diagnosed with ADHD take psychotropic medication (Radigan et al. 2005). Mirroring
this finding, studies have investigated parental attitudes towards medication use and socioeconomic
status, concluding that parents with higher education are more willing to give their children
medication for ADHD (McLeod et al. 2007; McLeod et al. 2004).

Research involving ADHD has investigated etiological factors of ADHD, including genetics
(Faraone et al. 2005) and neurological explanations (Mostofsky et al. 2002), as well as studies on
the prevalence of ADHD diagnosis and medication rates (Olfson et al. 2003). However, very little
research has examined the role of the academic ethic in terms of receiving medication for
ADHD from a sociological perspective (Loe and Cuttino 2008). The academic ethic refers to a
set of core beliefs about what is deemed proper behavior in and outside of the classroom (Loe
and Cuttino 2008; Rau and Durand 2000). We hope to fill this gap in the literature. Using a
logistic regression analysis, we examine the predictors of medication usage in terms of SES,
insurance availability, and what we will discuss below as the norm to be an “ideal student” or the
academic ethic.

In doing so, we hypothesize (1) that children and adolescents whose parents have higher SES will
be more likely to receive medication for ADHD; (2) that children and parents develop a heightened
sense of the academic ethic and this increases the likelihood of receiving medication for ADHD; and
(3) that the academic ethic explains the effect of SES on receiving medication. We begin with a
review of pertinent literature including the relationship between

  • ADHD and social class
  • , and a
    discussion of medicalization and the academic ethic.

    CONTACT Zachary R. Simoni Department of Sociology, University of Alabama – Birmingham,
    460 Heritage Hall Building, 1401 University Boulevard, Birmingham, AL 35294-1152, USA.
    © 2016 North Central Sociological Association

    2016, VOL. 49, NO. 2, 119–132

    ADHD and social class

    Research in the United States on the connection between socioeconomic status and receiving an ADHD
    diagnosis is mixed. Prevalence estimates for ADHD vary significantly across studies but utilizing DSMV
    criteria, Willcutt (2012) found that 5.9–7.1 percent were diagnosed with ADHD. Some studies posit that
    lower family income and family poverty status are not significantly related to receiving an ADHD
    diagnosis (Bussing et al. 1998; Schneider and Eisenberg 2006). However, German studies have found a
    different relationship (Huss et al. 2008). Children with low SES have the highest prevalence of ADHD
    diagnosis (6.4 percent), children withmedium SES have the second highest (5 percent), and childrenwith
    high social status have the lowest (3.2 percent) (Huss et al. 2008). Also, researchers have concluded that
    higher SES children are more likely to receive medication for ADHD (Olfson et al. 2003).

    In terms of medication use, cross-county studies indicate a correlation between counties of
    affluence and stimulant usage (Bokhari, Mayes, and Scheffler 2005). Counties with higher stimulant
    consumption rates have higher private to public education ratios, implying a higher social class
    among those counties. Additionally, there is a strong link between higher socioeconomic status and
    access to treatment for just about every disease outcome. In short, higher SES is associated with a
    higher likelihood of receiving medication for ADHD.

    Sociologists agree that student performance is affected by the sociocultural attributes they acquire
    (Lareau and Weininger 2003). Material resources are unequally distributed and thus the amount of
    familial investment on education may vary by SES (Buchmann, DiPrete, and McDaniel 2008).
    Although one cannot deny the impact of material resources on educational attainment, even in
    the same school there are differences engendered by SES; students with high SES have a higher
    amount of cultural capital and know how to use it (Lareau and Weininger 2003). Further, high-SES
    children come equipped with interactional strategies which increase the quality of their education,
    and they are better at creating learning opportunities for themselves as their behaviors elicit different
    responses from teachers (Calarco 2011). For instance, they are more likely to raise their hand in class
    or ask for help if something does not make sense. In regard to this study, children with higher SES
    have more cultural capital and may be more likely to receive medication in light of ADHD

    Additionally, cultural capital is not simply about educational attainment. Knowledge about
    ADHD and how to engage health care professionals to receive diagnoses and medication are also
    forms of cultural capital. Research has demonstrated that those with more cultural capital are better
    able to discuss the issue with their physicians and receive better long-term treatment including
    mental health care (Bell 2009; Shim 2010). Thus, ADHD medication rates may be unequally
    distributed across SES strata in society as a result of cultural factors.

  • Medicalization of ADHD
  • Medicalization refers to the process in which social problems are defined in medical terms and
    medical intervention is deemed the appropriate solution (Conrad 1992). Medical sociologists chal-
    lenge the medical profession’s promotion of medical interventions and document the costs and
    benefits of medical interventions (McLeod et al. 2007). They do not argue—as many misconstrue—
    that the underlying biologic mechanisms are fallacious, but instead look at the discourse and
    processes of medicalization in society. They demonstrate the power imposed by medical community
    members as gatekeepers of medicine and judges of behavior.

    Conrad and Schneider (1980) highlight three steps in order for a behavior to become medicalized.
    First, there must be something about the behavior that makes it problematic to broad cultural values
    within society. Second, there is a process of medical discovery that results in the elaboration of
    biomedical jurisdiction; once medical interest increases, the medical category gains legitimacy and
    becomes reified. Third, the biomedicalization of behavior has to successfully contend with non-medical
    definitions of causation. ADHD provides the quintessential example of the medicalization process.


    In 1937, Bradley, an early psychiatrist studying children, found that amphetamine drugs had an
    altering effect on the behavior of children with minimal brain dysfunction and learning disorders.
    Bradley named this behavior the “Hyperactivity Syndrome,” which included symptoms such as hyper-
    activity, poor memory, and disinhibition (Conrad 1975). Further, once the medical community found a
    way to treat the behavior, the symptoms and diagnosis of the behavior became far more prevalent.

    After Bradley’s study became widely recognized, ADHD became far more prevalent. Starting in
    the mid-1960s, Ritalin was used to treat to the symptoms. Once a treatment option existed, the
    incidence rates of ADHD increased each year (Searight and McLaren 1998). In 1987, ADHD was
    added to the DSM III. At this point, it did not have any subgroups. Since then, ADHD has grown to
    include three subgroups: inattentiveness, hyperactivity, and impulsivity. Currently, ADHD is one of
    the most prevalent mental disorders among children in the United States (Conrad and Slodden
    2013). Further, Adult ADHD has also been added to the DSM IV, which has similar criteria as
    ADHD, but is diagnosed in adults.

  • Social class, ADHD, medicalization, and the academic ethic
  • Because ADHD medication helps students achieve academically, this study hypothesizes that children
    who have high SES will be more likely to conform to ideal student behavior, and therefore, are more
    likely to be medicated. Parents with higher SES have a transmission of values and expectations that lead
    them to expect greater academic achievement (Zhang et al. 2011). In other words, Zhang and colleagues
    note a reciprocal relationship in which expectations about students’ previous achievement influences
    parents’ expectations, which in turn affects students’ expectations over time. Additionally, Bandura and
    colleagues (1996) found that children whose parents had high academic aspirations for their children had
    higher feelings of academic self-efficacy compared to children whose parents did not.

    We suggest that one mechanism of social control in the educational social context may be the
    utilization of the academic ethic by children and adolescents. In short, the academic ethic refers to a
    set of core beliefs about what is deemed proper behavior in and outside of the classroom and how
    important school is to their lives (Loe and Cuttino 2008; Rau and Durand 2000). Although the study
    by Loe and Cuttino (2008) did not investigate children and adolescents, they established that the
    academic ethic referred to diligent attention to academic learning and thus helps to lay the
    foundation for our theoretical argument. As noted by Rau and Durand (2000), students rarely
    adhere to this ethic entirely, but instead manage their behaviors to conform to this idealized
    behavior. Further, while it is not clear whether a “normative” academic ethic exists, it is clear that
    the notion of an academic ethic is still relevant, and children and adolescents reify its existence by
    their behavior (Loe and Cuttino 2008; Rau and Durand 2000).

    The academic ethic is part of what is expected by the classroom teacher. The teacher’s
    perceptions construct the academic ethic as well. So, one of the goals of this study is to test
    whether this academic ethic is, in fact, relevant to children and adolescents. Students who have
    been diagnosed with ADHD may be in a position that hinders them from achieving the academic
    ethic; therefore, pharmaceutical treatment can be seen as integral to the student’s ability to meet
    these expectations.

    We are the first (to our knowledge) to conceptualize the academic ethic in a quantitative study,
    and this study aims to further expand the concept to children and adolescents. We must also note
    that the construction of the academic ethic may be closely connected to another concept, the ideal
    student; the ideal student is one who is polite, well behaved, eager to learn, and tries to get along with
    classmates (Harkness et al. 2007).

  • Hypotheses
  • This study hypothesizes (1) that children and adolescents whose parents have higher SES will be
    more likely to receive medication for ADHD; (2) that higher SES children and parents develop a


    heightened sense of the academic ethic, increasing the likelihood of receiving medication for ADHD;
    and (3) that the academic ethic explains the effect of SES on receiving medication. Figure 1 illustrates
    these hypotheses.

  • Method
  • In order to examine the relationship among SES, the academic ethic, and the likelihood of receiving
    medication for ADHD, we use logistic regression for this study.


    This study used secondary data collected by the National Survey of Children’s Health. The
    NSCH is a sample of children across the United States with approximately 1,800 per state (N =
    91,642) (Blumberg et al. 2012; NSCH 2007). For the purposes of this study, children and
    adolescents without an ADHD diagnosis were excluded from the analysis, resulting in a sample
    of 5,240 cases (approximately 6 percent of the population). Missing cases from the covariates
    were removed from the analysis as well, using listwise deletion. This includes responses of “don’t
    know” and refused responses. The total number of missing responses for all variables was 880,
    leaving a sample of 4,360.


    Dependent variables
    Medication for ADHD. This question asked whether or not the child has been medicated for
    ADHD. As noted above, the sample pertains only to children and adolescents who were diagnosed
    with ADHD according to the parents. That is, the parents were asked if the child had been diagnosed
    with ADHD by a doctor. The “medication for ADHD” variable is similar in that it asks the parent
    and measures children and adolescents who currently have ADHD and are taking medication for it.
    The question has two possible responses, “yes (taking medication) = 1,” and “no (not taking
    medication) = 0.”

    Academic ethic. This variable measures how much the child “cares about doing well in school” as
    designated by the parent. The survey item responses included, “never” = 1, “rarely” = 2, “sometimes”
    = 3, “usually” = 4, and, “always” = 5. For the purposes of our analysis we dichotomized the academic
    ethic so that those with low academic ethic (1-3) were scored 0 and high academic ethic (4-5) was
    scored 1. The variable was not proportional, thus nulling the option to use ordinal logistic

    Higher SES



    of Academic



    Odds of



    Figure 1. Hypotheses.

    1The categories used in the academic ethic variable are not proportional; therefore, a step from one category to another is not the
    same. This is further substantiated by calculating a “score test of proportional odds,” which tests this basic assumption of
    regression analysis. By dichotomizing the academic ethic variable, we are able to properly analyze the variable with all its
    covariates (Allison 1999).


    Independent variables: Socioeconomic status and demographic covariates
    Race. Race was originally divided into four categories: white (73.7 percent), black (17.3 percent),
    Hispanic (5.9 percent), and other (3.1 percent). However, due to the small cell size of Hispanic and
    other, we decided to dichotomize race as “white” = 1 and “non-white” = 0.2

    Age. Age is measured continuously with a range of 6 years of age to 18.

    Gender of child. Categories were coded such that “male” = 1 and “female” = 0.

    Income. This measured the total family income, including that of both parents. The variable is broken
    up into eight categories relative to the poverty level. Each group corresponds to a percent above or
    below the poverty level, ranging from 100 percent below the poverty level to 800 percent above the
    poverty level. “At or below 100 percent of poverty” = 1, “above 100 percent to at or below 133 percent
    poverty level” = 2, “above 133 percent to at or below 150 percent poverty level” = 3, “above 150 percent
    to at or below 185 percent poverty level” = 4, “above 185 percent to at or below 200 percent poverty
    level” = 5, “above 200 percent to at or below 300 percent poverty level” = 6, “above 300 percent to at or
    below 400 percent poverty level” = 7, and finally, “above 400 percent poverty level” = 8.

    Parents’ education. The question asked the parent what his or her education level was. The response
    categories were “less than high school” = 1, “high school” = 2, and “some college or higher” = 3.

    Insurance. The child’s health insurance status was collected with “insured” = 1 and “uninsured” = 0.
    This measure includes government funded insurance including Medicaid and Medicare.

    Mental health covariates

    We control for depression and anxiety because they may be associated with the academic ethic and
    medication use. In addition, depression and anxiety are both comorbid with ADHD (Becker et al.
    2014; Biederman et al. 1996; Schatz and Rostain 2006).

    Depression. The question asked the parent whether or not a doctor has diagnosed them with
    depression “yes, has depression” = 1, “no, does not have depression” = 0.

    Anxiety. The question asked the parent whether or not a doctor has diagnosed them with anxiety
    “yes, has anxiety” = 1; “no, does not have anxiety” = 0.

    ADHD severity. The question asked the parent how severe they thought their child’s ADHD
    symptoms were. This measure consists of a 3-point scale with “mild” = 1, “moderate” = 2, and
    “severe” = 3.

    Descriptive statistics. In order to examine the relationship between socioeconomic status and
    ADHD medication rates, this study compared the means and percentages of the variables in the
    analysis (e.g., race, age, gender, parents’ education, household income, academic ethic, anxiety and
    depression) across two categories (e.g., those with ADHD and taking medication; those with
    ADHD and not taking medication). Cross-tabulations and chi-squares were utilized to analyze
    nominal and ordinal variables and independent sample t-tests were utilized to analyze continuous
    variables (Table 1).

    2In the regression analyses, we did test a series of dummy variables for race with medication use. Since the results were
    substantively similar, we kept the race variable dichotomous for parsimony.


    Multivariate analysis. Next, we use cross-sectional multivariate logistic regression equations to
    predict medication usage. In order to examine whether demographic variables are correlated with
    ADHD medication use, the first model regresses the dependent variable medication on race, age and
    sex. The second model regresses medication, the previous variables, and adds SES variables, income,
    parents’ education and insurance. The third model adds ADHD severity, anxiety and depression. In
    order to decipher the effect of the academic ethic, the fourth model regresses medication, the
    previous variables, and then adds the academic ethic (Table 2). Table 3 uses a similar model structure
    but instead utilizes Academic ethic as a dependent variable in order to determine whether education
    or income is related to the academic ethic.

  • Results
  • Table 1 reports means for continuous variables and percentages for dichotomous variables on the
    variables of interest in the study, as well as the range. The sample of those diagnosed with ADHD is
    shown divided by taking versus not taking medication, and by total.

    We found that among those children and adolescents taking medicine, 72.7 percent were white; of
    those not taking medicine, 63.6 percent were white. The mean age was higher for those children and

    Table 1. Means for taking versus not taking medicine for those diagnosed with ADHD.

    Diagnosed with ADHD (N = 4360)

    Variables Medication (N = 3012) No Medication (N = 1348) Sample (N = 4,360) Range

    White*** 72.7% 63.6% 73.7% 1-0
    Age*** 12.3 13.05 12.8 6-18
    Male*** 70.0% 67.1% 69% 1-0
    Education*** 2.5 2.4 2.5 1-3
    Income*** 5.9 5.5 5.4 1-8
    Insured*** 90.6% 85.5% 88.9% 1-0
    ADHD Severity*** 1.7 1.41 1.8 1-3
    Depression** 18.8% 12.6% 16.7% 1-0
    Anxiety 29.6% 25.0% 28.1% 1-0
    Academic Ethic* 3.95 3.7 3.8 1-5

    Note. Significance difference between taking medication and not taking medication groups.
    ADHD = Attention Deficit Hyperactivity Disorder.
    * p < .05, **p < .01, *** p < .001. Percentages reported for dichotomous variables means for continuous variables.

    Table 2. Logistic regression of medication usage (N = 4,360).

    Model 1 Model 2 Model 3 Model 4

    White 1.57*** 1.44*** 1.50 *** 1.83***
    Age .92*** .92*** .93*** .90***
    Male .91 .92 .98 1.19
    Parents’ Education
    High School 1.31 1.37 1.57

    Income 1.04*** 1.08*** 1.05**
    Insured 2.04*** 2.06*** 2.05***
    ADHD Severity
    Mild Reference Reference
    Moderate 2.23 2.09
    Severe 5.30*** 6.16***

    Depression 1.05 1.12
    Anxiety .83 .83
    Academic Ethic 1.29***
    Pseudo R2 .02 .04 .10 .12

    ADHD = Attention Deficit Hyperactivity Disorder.
    *p < .05, **p < .01., ***p < .001.


    adolescents who were not taking medication (13.05) as opposed to those who were taking medica-
    tion (12.34), indicating that younger children and adolescents were more likely to receive medica-
    tion. While 69 percent of the sample was male, of those taking medication, 70 percent were male, as
    compared to those not taking medication, in which 67.1 percent were male who were not taking
    medication. Children and adolescents taking medication had a higher mean household income
    (5.85) than those who were not taking medication (5.54). The relationship was significant at the
    .001 level. Also, parental education was significantly higher for children and adolescents taking
    medication (2.50), as opposed to those not taking medication (2.38). More children and adolescents
    who were insured received medication (90.6 percent) as opposed to those who were not insured
    (85.5 percent).

    Children and adolescents receiving medication for ADHD had higher ADHD severity (1.71)
    compared to those not receiving medication (1.41). In terms of mental health, children and
    adolescents receiving medication for ADHD were more likely to be depressed (18.8 percent vs.
    12.6 percent). The mean academic ethic was higher for those who were taking medication (3.95) as
    opposed to those who were not taking medication (3.74).

    Overall, Table 1 shows relationships in the expected directions. Those taking medicine were more
    likely to be white, younger, and male. Their parents had higher education and household income,
    and were more likely to be insured—all of these factors are indicative of higher socioeconomic status.
    Not surprisingly, those with more severe ADHD were more likely to take medicine; those who took
    medicine were more likely to be depressed. Finally, we find that those who scored higher on
    academic ethic were more likely to take medicine, which supports our reasoning that those who
    have more invested in being an “ideal” student would be more likely to take ADHD medication. We
    now turn to the multivariate analysis to show the relationship between the variables of interest, while
    incorporating the controls.

    We used progressive adjustment in binary logistic regression to examine the variables of
    interest with likelihood of medication use. Progressive adjustment helps to demonstrate the
    association between two variables and then the influence of a mediating variable. This is usually
    done by adding variables into a model in steps, depending on their theoretical relevance (Cole
    1980). We assessed each variable stepped-in independently, but we show results in theoretically
    relevant blocks for parsimony. For instance, we first examined the effect of race, sex, and age on
    medication use. In model 2 we added SES including parents’ income, parents’ education and

    Table 3. Logistic regression of academic ethic (N = 4,360).

    Model 1 OR Model 2 OR Model 3 OR Model 4 OR Model 5 OR

    Income 1.08*** 1.09*** 1.08*** 1.06*** 1.05***
    White 1.43*** 1.40*** 1.40 1.34***
    Age .91*** .91*** .91*** .92***
    Male .56*** .56*** .55*** .54***
    Parents’ Education
    High School .94 .91 .91

    Insured 1.21 1.23 1.30
    ADHD Severity
    Mild Reference Reference
    Moderate .80 .72
    Severe 52** .44**

    Depression .64 .64
    Anxiety .79 .79
    Medication 1.69***
    Pseudo R2 .05 .07 .08 .10

    ADHD = Attention Deficit Hyperactivity Disorder.
    *p < .05, **p < .01, ***p < .001.


    whether they were insured. Model 3 adds ADHD severity, depression and anxiety. Finally, model
    4 adjusts for the effect of the academic ethic. These findings are listed in Table 2.

    Model 1 regressed the dependent variable medication use on the independent variables of being
    white, age, and being male. For white children and adolescents, whites had a 57 percent greater odds
    of receiving medication than non-whites. For each additional year in age, the odds of receiving
    medication decreased by 8 percent.

    Model 2 added parents’ education, income, and whether or not they were insured. Education was
    not significant. However, income was a significant finding. With each increase in income, the odds
    of receiving medication increased by 4 percent. Being insured is associated with a 104 percent
    increase in the odds of receiving medication for ADHD.

    Model 3 added ADHD severity, depression, and anxiety. Not surprisingly, reporting severe
    ADHD (reference = mild) is associated with greater use of medication (OR = 5.30), or a 430 percent
    increase in the odds of using medication. By far, ADHD severity had the largest impact on increased
    odds of taking medication.

    Model 4 regressed the dependent variable medication use on the independent variables white,
    education, income, ADHD severity, age, uninsured, male, depression, anxiety, and added the
    academic ethic. The variables from model 3 do not change much substantively. White children
    and adolescents, having parents with higher income, and severe ADHD are still related to
    increased odds of receiving medication for ADHD (OR = 1.83, 1.05, 6.16). Being older is
    associated with a decrease in the odds of using medication (OR = .90). Surprisingly, being
    male was not significantly associated with medication usage. The academic ethic increased the
    likelihood of receiving medication by 29 percent. In this model, we expected to see a diminution
    of income demonstrating that the academic ethic explained income supporting our third hypoth-
    esis; however, this was not the case.

    In order to investigate whether the academic ethic was influenced by SES, we regressed income
    on the academic ethic and added all the other control variables to five separate models. We also
    used progressive adjustment in binary logistic regression to examine the variables of interest with
    likelihood of academic ethic. We investigated each variable stepped in independently, but we show
    results in theoretically relevant blocks for parsimony. We followed similar models as in the
    Table 2, but added medication at the end of the last model. Of primary interest in this table is
    the relationship between income and the academic ethic and parent education and the academic
    ethic. As noted in Table 3, income remains significant after all the controls have been added,
    indicating that income is related to the academic ethic (OR = 1.08, 1.09, 1.08, 1.06, and 1.05,
    respectively). We do not find a significant relationship between the academic ethic and parents’

  • Discussion
  • This study hypothesized (1) that children and adolescents whose parents have higher SES will
    be more likely to receive medication for ADHD; (2) that higher SES children and parents
    develop a heightened sense of the academic ethic and this increases the likelihood of receiving
    medication for ADHD; and (3) that the academic ethic explains the effect of SES on receiving

    We found partial support for hypothesis 1 and 2 as children and adolescents from a higher
    income background were more likely to receive medication for ADHD. Surprisingly, parents’
    education did not predict medication usage. In support of hypothesis 2, having a higher academic
    ethic increased the odds of receiving medication. In the final model, we did not find support for the
    third hypothesis as academic ethic did not reduce the effect of income. Although, as noted in
    Table 3, income is related to the academic ethic, we do not have substantial evidence that income
    mediates the relationship.


    We conclude with interpretations of the results in this study. Studies indicate that ADHD
    medication rates have been slowly increasing over the last three decades (Conrad 2007; Conrad
    and Schneider 1980). This study reflects the existing literature, implying that it is more likely for
    upper-class children and adolescents to receive medication. The empirical findings of this study
    suggest that younger children and adolescents with more severe symptoms of ADHD and higher
    parental income are more likely to receive medication for ADHD. Additionally, this study shows that
    a stronger construction of the academic ethic is associated with greater odds of receiving medication;
    therefore, when severe ADHD symptoms are observed, the odds of receiving medication increases.
    Older students are less likely to receive medication. This study also highlights the finding that
    insurance matters in terms of treatment for ADHD, since those with health insurance were more
    likely to receive medication for ADHD.

    The findings for race are important as well. Across all models, whites had an increased likelihood
    of receiving medication, as compared to non-whites. Once controlling for related variables with
    medication usage, whites were even more likely to receive medication. The finding that non-whites
    were less likely to take medicine may be indicative of a historical, and still current, distrust of
    medicine among African Americans and Hispanics, with real consequences for the acceptance of
    treatment strategies and health-seeking behavior (Thompson et al. 2004; Wasserman, Flannery, and
    Clair 2007). Our findings support other research showing African Americans had a decreased
    likelihood of using medication (Schnittker 2003).

    The way in which boyhood and normal adolescent behavior is constructed may vary across
    socioeconomic statuses. For instance, acting hyperactive in one’s social milieu may be frowned upon
    and hence elicit medication, whereas in another SES group it may tolerated. Hart, Grand, and Riley
    (2006) explain how some hyperactive behaviors may be tolerated as “boys will be boys,” while in
    other contexts, hyperactive and inattentive behavior are seen as a threat to the child and others.
    Thus, it is surprising as we did not find a sex difference in terms of medication usage, but not
    surprising that we found SES differences in medication usage.

    Having a higher SES may also increase the desire to maintain a high standard of living. As
    Durkheim (1951) notes, “The less one has the less one is tempted to extend the range of his needs
    indefinitely.” Following that logic, the more one has, the more one is willing to extend his or her
    needs indefinitely. Hence, if a child or parent has had a taste of excess by living in economic
    affluence granted by their social status, they will be more likely to strive for a continuation of that
    affluence. If a parent or a child feels that their desires are not likely to be met without medication,
    they might be more likely to receive medication for an affliction, which may hinder their success at
    achieving those goals. Parents may also fear the risk of their child drifting into a lower social class as
    a result of the ADHD symptoms.

    This study concluded that the severity of ADHD symptoms was a predictor of ADHD
    medication, implying that children and adolescents with more severe symptoms—as rated by
    their parents—are more likely to receive medication. Parents who self-identify severe symp-
    toms of ADHD for their children and adolescents are more likely to give them medication to
    solve the problem. Stigma, which has been found to impact the experience of ADHD (Koro-
    Ljungberg and Bussing 2009), may contribute to their decision as parents of children and
    adolescents with more severe ADHD symptoms; these parents may feel pressure from other
    parents or feel like a bad parent and thus be more willing to medicate their children and

    McLeod and colleagues (2004) found similar evidence in that parents were unwilling to give
    their children and adolescents medication for ADHD unless they were in the highest education
    bracket. Parents may have felt stigmatized by teachers and other parents, and thus be more
    willing to medicate their children and adolescents. They may have felt their child was not
    adequately equipped to conform to the standards of the educational system. To the parents,
    this inadequacy may reflect poorly on them for not providing the proper parenting (McLeod
    et al. 2004). We were surprised that though Table 1 showed that parents’ higher education was


    associated with medication usage, it was not significant in the regression analysis. Perhaps in
    terms of receiving medication, insurance, academic ethic, income, and severity of ADHD
    symptoms are such strong predictors, parents’ education was attenuated.

    There are overwhelming differences in healthcare services and access across SES groups in the
    United States. Medicaid allows for children living below or near the poverty line—depending on the
    state of residence—to receive health care funded by the state. Middle- and lower-middle-class
    persons are more likely to be uninsured or underinsured and may not receive proper mental health
    care, since it is often not covered by insurance companies. High SES persons are more likely to be
    insured, receive more mental health evaluations, and be able to afford expensive medication (Pande
    et al. 2011). Thus, it seems reasonable that children and adolescents with higher SES will be more
    likely to receive medication for ADHD.

    The passage and constitutional legitimacy of the health reform law (The Patient Protection and
    Affordable Care Act) which mandates that everyone buy health insurance or receive subsidies so that
    they can afford insurance may be a contributing factor in terms of continuing the patterns of the
    medicalization of ADHD elicited in this study. Thus, children and adolescents in the lower and
    middle SES groups who were previously uninsured will be more likely to have insurance and that
    may lead to diagnosis and medication for ADHD. This implies that the trend seen in the upper
    classes may be echoed in the lower and middle classes. It is yet to be demonstrated, however,
    whether the law will influence the construction of the academic ethic. Thus, this study suggests that
    disparities in access to medication will still be evident because of differing degrees of the academic

    We now turn to a discussion of the academic ethic. Previous literature notes the importance of
    cultural capital in terms of educational outcomes; namely, that cultural capital is an important
    predictor of educational success. In this vein, we found that the academic ethic led to an increased
    likelihood of children and adolescents receiving medication for ADHD. We presume that this is
    partly due to the fact that those children (or their parents) who want to perform well in school are
    more likely to use medication as a way to mitigate ADHD symptoms which may negatively influence
    their identity as a good student. In other words, ADHD medication is more likely to be taken when
    children have a desire to perform well in school. We found support for this proposition. However,
    we also hypothesized that part of the variation between SES and medication use was explained by the
    academic ethic and the results do not indicate this to be the case.

    Furthermore, we place the academic ethic within an educational context in order to fully
    analyze the ways in which this concept may influence medication use. In the last decade,
    education has increasingly relied on an accountability model (Elliott and Hout 2011; Wagner
    2013). That is, teachers are held accountable for their students’ academic success by measuring
    students’ progress through standardized testing (Smith and Kovacs 2011). This fact is further
    evinced by the implementation of public policies such as No Child Left Behind and the Common
    Core. Although this study is certainly not a discussion of public policy, it seems plausible that the
    academic ethic may be ever more important in the coming years due to the accountability
    movement in education.

    The academic ethic is conceptualized as the way in which children and adolescents manage
    their behaviors to conform to idealized behavior in a particular social setting. Socialization is a
    key component of the academic ethic and states that children learn which norms and behaviors
    are acceptable in different social contexts (Clausen et al. 1968). There are many powerful
    “agents of socialization” in modern society that aid in the socialization process including the
    family, the media, peers, and, most importantly, the education system. As a result of the move
    to the accountability model, children may become socialized to conform to certain types of
    behaviors that are more salient within an accountability context. In short, we suggest that the
    academic ethic may become further normalized and become present throughout all SES
    categories. This may help to explain why SES did not mediate the relationship between SES
    and medication use.


  • Limitations
  • One limitation in this study was the fact that parents self-reported the symptoms of their children
    and adolescents as told by a professional. The NSCH survey did not use specific DSM criteria in
    order to diagnosis the children and adolescents with ADHD. This may create a degree of
    subjectivity and skew the sample of children diagnosed with ADHD. However, for practical
    purposes, parents’ reports should reflect a diagnosis that originated from the DSM. Inclusion
    for this study was based on the question “has a doctor or other health care provider ever told you
    that your child had Attention Deficit Disorder or Attention Deficit Hyperactive Disorder, that is,
    ADD or ADHD?” This is a very clear question, and is also a strength, in that it is unlikely that
    parents who may perceive their children/adolescents as hyperactive would say their children/
    adolescents have ADHD.

    Another limitation in this study was the ambiguity in terms of medication use and receiving
    medication. Since the survey asked only whether the child had received medication for ADHD,
    and not whether they had been taking the medication, we do not know if they were, in fact,
    taking medication. As noted by Wilens et al. (2008) children and adolescents may divert, sell, or
    abuse ADHD medication, thus manipulating the possible benefits or harms of medication
    consequences (many girls use these medications for weight control) (DeSantis, Webb, and Noar
    2008). However, in this younger sample with an average age around 12 and more males than
    females, this is less likely.

    This study may be limited by the way in which the ideal student construction variable was
    operationalized. The variable asked the parent how much their child cared about school and—most
    importantly—it is presumed by this study that they felt this way before taking medication for
    ADHD. However, since this is a cross-sectional study, there is no way to discern whether or not it
    is, in fact, the medication that leads to caring about school or whether caring about school elicited
    the use of medication. Moreover, as this is cross-sectional data, we do not know the cause and effect
    relationships with medication usage and our variables of interest. However, it is unlikely that
    medicine usage also leads to higher parents’ income, higher ADHD severity, etc.

    Another important limitation is the cross-sectional nature of the analytic strategy. Although the
    authors would have wanted to investigate the current subject matter longitudinally, the data does not
    exist. As a result of this particular limitation, it is difficult for the authors to differentiate between
    whether those with a higher academic ethic are more likely to receive medication or whether those
    taking medication report a higher academic ethic as a result of medication use. Further studies are
    needed to fully tease out this causal relationship.

    Finally, 35 respondents answered they either do not know or refused on the ADHD severity
    variable and thus were coded as missing. The variable asked the parents how severe they thought
    their child’s ADHD was, so there may be exclusion bias, as parents may have been unwilling to
    admit that their child had severe symptoms. Thus, the sample may be slightly skewed towards
    ADHD cases that were less likely to report severe ADHD symptoms. Although this may be a
    limitation, it is reasonable to presume that the same findings will be present if the cases of ADHD
    were in fact more severe, and indeed may be stronger.

    In summary, ADHD is a serious and academically debilitating issue for many children and
    adolescents. We argue that medication rates are partially explained by important cultural attitudes
    and behaviors related to social class. Although we found that having insurance was an important
    factor in terms of receiving medication, cultural attitudes including the academic ethic still proved to
    be an important factor associated with medication use amongst children and adolescents. The
    findings of this study imply that the academic ethic is an important concept to consider that may
    explain higher rates of medication use amongst children and adolescents with higher SES. In light of
    these findings, we believe that further research needs to investigate the social structural components
    which lead to ADHD health disparities and further investigation of the academic ethic is needed in
    order to unravel health disparities with regards to ADHD and social class.


    About the authors

    Zachary R. Simoni is a doctoral candidate in the Department of Medical Sociology at the University of Alabama at
    Birmingham. His interests lie in medical sociology, sociology of mental health, and health disparities.

    Patricia Drentea is an associate professor of sociology at the University of Alabama-Birmingham. She is
    writing a book on families and aging. She conducts research on debt and mental health. She has recently
    published in Society and Mental Health, The Gerontologist, and the Journal of Family Issues. She may be
    reached at

  • Acknowledgment
  • The authors would like to express their gratitude to Dr. Hayley Hamilton at the Centre for Addiction and Mental
    Health, Toronto, Ontario.

  • References
  • Allison, Paul. 1999. Logistic Regression Using SAS®: Theory and Application. College Station, TX: SAS Publishing.
    American Psychiatric Association. 2000. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV-TR®.

    Washington, DC: American Psychiatric Publication.
    Bandura, Albert, Claudio Barbaranelli, Gian Vittorio Caprara, and Concetta Pastorelli. 1996. “Multifaceted Impact of

    Self-efficacy Beliefs on Academic Functioning.” Child Development 67(3):1206–1222.
    Becker, Stephen P., Joshua M. Langberg, Steven W. Evans, Erin Girio-Herrera, and Aaron J. Vaughn. 2014.

    “Differentiating Anxiety and Depression in Relation to the Social Functioning of Young Adolescents with
    ADHD.” Journal of Clinical Child & Adolescent Psychology (3):1–15.

    Bell, Ann V. 2009. “ʻIt’s Way Out of My League’: Low-income Women’s Experiences of Medicalized Infertility.”
    Gender & Society 23(5):688–709.

    Biederman, Joseph, Stephen Faraone, Eric Mick, Phoebe Moore, and Elise Lelon. 1996. “Child Behavior Checklist
    Findings Further Support Comorbidity between ADHD and Major Depression in a Referred Sample.” Journal of the
    American Academy of Child & Adolescent Psychiatry 35(6):734–742.

    Blumberg, Stephen J., E. B. Foster, A. M. Frasier, J. Satorius, B. J. Skalland, K. L. Nysse-Carris, H. M. Morrison, S. R.
    Chowdhury, and K. S. O’Connor. 2012. “Design and Operation of the National Survey of Children’s Health, 2007.”
    Vital and Health Statistics. Ser. 1, Programs and Collection Procedures (55):1.

    Bokhari, Farasat, Rick Mayes, and Richard M. Scheffler. 2005. “An Analysis of the Significant Variation in
    Psychostimulant Use across the U.S.” Pharmacoepidemiology and Drug Safety 14(4):267–275.

    Buchmann, Claudia, Thomas A. DiPrete, and Anne McDaniel. 2008. “Gender Inequalities in Education.” Annual
    Review of Sociology 34(1):319–337.

    Bussing, Regina, Nancy E. Schoenberg, Kenneth M. Rogers, Bonnie T. Zima, and Sherwin Angus. 1998. “Explanatory
    Models of ADHD: Do They Differ by Ethnicity, Child Gender, or Treatment Status?” Journal of Emotional and
    Behavioral Disorders 6(4):233–242.

    Calarco, Jessica McCrory. 2011. “ʻI Need Help!’ Social Class and Children’s Help-seeking in Elementary School.”
    American Sociological Review 76(6):862–882.

    Castle, Lon. Ronald E. Aubert, Robert R. Verbrugge, Mona Khalid, and Robert S. Epstein. 2007. “Trends in Medication
    Treatment for ADHD.” Journal of Attention Disorders 10(4):335–342.

    Clausen, John A., Orville G. Brim, Alex Inkeles, Ronald Lippitt, Eleanor E. Maccoby, and M. Brewster Smith. 1968.
    Socialization and Society. Boston, MA: Brown Little.

    Cole, Stephen. 1980. The Sociological Method: An Introduction to the Science of Sociology. New York: Rand McNally
    Publishing Company.

    Conrad, Peter. 1975. “The Discovery of Hyperkinesis: Notes on the Medicalization of Deviant Behavior.” Social
    Problems (4):12–21.

    Conrad, Peter. 1992. “Medicalization and Social Control.” Annual Review of Sociology 18(3):209–232.
    Conrad, Peter. 2007. The Medicalization of Society: On the Transformation of Human Conditions into Treatable

    Disorders. Baltimore: Johns Hopkins University Press.
    Conrad, Peter, and Joseph W. Schneider. 1980. “Looking at Levels of Medicalization: A Comment on Strong’s Critique

    of the Thesis of Medical Imperialism.” Social Science & Medicine. Part A: Medical Psychology & Medical Sociology

    Conrad, Peter, and Caitlin Slodden. 2013. “The Medicalization of Mental Disorder.” Pp. 61–73 in Handbook of the
    Sociology of Mental Health. 2nd ed, edited by C. Aneshensel. New York: Springer.

    Cuffe, Steven P., Charity G. Moore, and Robert E. McKeown. 2005. “Prevalence and Correlates of ADHD Symptoms
    in the National Health Interview Survey.” Journal of Attention Disorders 9(2):392–401.


    DeSantis, Alan D., Elizabeth M. Webb, and Seth M. Noar. 2008. “Illicit Use of Prescription ADHD Medications on a
    College Campus: A Multimethodological Approach.” Journal of American College Health 57(3):315–324.

    Durkheim, Emile. 1951. Suicide: A Study in Sociology. Translated by J.A. Spaulding and G. Simpson. New York: The
    Free Press.

    Elliott, Stuart W., and Michael Hout. 2011. Incentives and test-based accountability in education. Washington, DC:
    National Academies Press.

    Faraone, Stephen, and Joseph Biederman. 2005. “What is the Prevalence of Adult ADHD? Results of a Population
    Screen of 966 Adults.” Journal of Attention Disorders 9(2):384–391.

    Faraone, Stephen, Roy H. Perlis, Alysa E. Doyle, Jordan W. Smoller, Jennifer J. Goralnick, Meredith A. Holmgren, and
    Pamela Sklar. 2005. “Molecular Genetics of Attention-Deficit/Hyperactivity Disorder.” Biological Psychiatry 57

    Harkness, Sara, Marjolijn Blom, Alfredo Oliva, Ughetta Moscardino, Piotr Olaf Zylicz, Moises Rios Bermudez, Xin
    Feng, Agnieszka Carrasco-Zylicz, Giovanna Axia, and Charles M. Super. 2007. “Teachers’ Ethnotheories of the
    ‘Ideal Student’ in Five Western Cultures.” Comparative Education 43(1):113–135.

    Hart, Nicky, Noah Grand, and Kevin Riley. 2006. “Making the Grade: The Gender Gap, ADHD, and the
    Medicalization of Boyhood.” Medicalized Masculinities:132–154.

    Huss, M., H. Hölling, B. M. Kurth, and R. Schlack. 2008. “How Often Are German Children and Adolescents
    Diagnosed with ADHD? Prevalence Based on the Judgment of Health Care Professionals: Results of the German
    Health and Examination Survey (KiGGS).” European Child & Adolescent Psychiatry 17:52–58.

    Koro-Ljungberg, Mirka, and Regina Bussing. 2009. “The Management of Courtesy Stigma in the Lives of Families with
    Teenagers with ADHD.” Journal of Family Issues 3(9):1175–1200.

    Lareau, Annette, and Elliot B. Weininger. 2003. “Cultural Capital in Educational Research: A Critical Assessment.”
    Theory and Society 32(5/6):567–606.

    Loe, Meika, and Leigh Cuttino. 2008. “Grappling with the Medicated Self: The Case of ADHD College Students.”
    Symbolic Interaction 31(3):303–323.

    McLeod, Jane D., Danielle L. Fettes, Peter S. Jensen, Bernice A. Pescosolido, and Jack K. Martin. 2007. “Public
    Knowledge, Beliefs, and Treatment Preferences Concerning Attention-Deficit Hyperactivity Disorder.” Psychiatric
    Services 58(5):626–631.

    McLeod, J. D., B. A. Pescosolido, D. T. Takeuchi, and T. F. White. 2004. “Public Attitudes Toward the Use of
    Psychiatric Medications for Children.” Journal of Health and Social Behavior 45(1):53–67.

    Mostofsky, Stewart H., Karen L. Cooper, Wendy R. Kates, Martha B. Denckla, and Walter E. Kaufmann. 2002.
    “Smaller Prefrontal and Premotor Volumes in Boys with Attention-Deficit/Hyperactivity Disorder.” Biological
    psychiatry 52(8):785–794.

    NSCH. 2007. “Child and Adolescent Health Initiative, Data Resource Center on Child and Adolescent Health.”
    Retrieved April 23, 2007 (

    Olfson, Mark, Marc J. Gameroff, Steven C. Marcus, and Peter S. Jensen. 2003. “National Trends in the Treatment of
    Attention Deficit Hyperactivity Disorder.” American Journal of Psychiatry 160(6):1071–1077.

    Pande, Aakanksha H., Dennis Ross-Degnan, Alan M. Zaslavsky, and Joshua A. Salomon. 2011. “Effects of Healthcare
    Reforms on Coverage, Access, and Disparities: Quasi-Experimental Analysis of Evidence from Massachusetts.”
    American Journal of Preventive Medicine 41(1):1–8.

    Radigan, Marleen, Peter Lannon, Patrick Roohan, and Foster Gesten. 2005. “Medication Patterns for Attention-
    Deficit/Hyperactivity Disorder and Comorbid Psychiatric Conditions in a Low-Income Population.” Journal of
    Child & Adolescent Psychopharmacology 15(1):44–56.

    Rau, William, and Ann Durand. 2000. “The Academic Ethic and College Grades: Does Hard Work Help Students to
    ‘Make the Grade’?” Sociology of Education 73(1):19–38.

    Schatz, David Beck, and Anthony L. Rostain. 2006. “ADHD With Comorbid Anxiety A Review of the Current
    Literature.” Journal of Attention Disorders 10(2):141–149.

    Schneider, Helen, and Daniel Eisenberg. 2006. “Who Receives a Diagnosis of Attention-Deficit/ Hyperactivity
    Disorder in the United States Elementary School Population?” Pediatrics 117(4):601–609.

    Schnittker, Jason. 2003. “Misgivings of Medicine?: African Americans’ Skepticism of Psychiatric Medication.” Journal
    of Health and Social Behavior 3(4):506–524.

    Searight, H. Russell, and A. Lesley McLaren. 1998. “Attention-Deficit Hyperactivity Disorder: The Medicalization of
    Misbehavior.” Journal of Clinical Psychology in Medical Settings 5(4):467–495.

    Shim, Janet K. 2010. “Cultural Health Capital a Theoretical Approach to Understanding Health Care Interactions and
    the Dynamics of Unequal Treatment.” Journal of Health and Social Behavior 51(1):1–15.

    Smith, Jason M., and Philip E. Kovacs. 2011. “The Impact of Standards-Based Reform on Teachers: The Case of ‘No
    Child Left Behind.’” Teachers and Teaching 17(2):201–225.

    Thompson, Hayley S., Heiddis B. Valdimarsdottir, Gary Winkel, Lina Jandorf, and William Redd. 2004. “The Group-
    Based Medical Mistrust Scale: Psychometric Properties and Association with Breast Cancer Screening.” Preventive
    Medicine 38(2):209–218.

    Wagner, Robert B. 2013. Accountability in education: A philosophical inquiry. London, UK: Routledge.


    Wasserman, J., M. A. Flannery, and J. M. Clair. 2007. “Raising the Ivory Tower: the Production of Knowledge and
    Distrust of Medicine Among African Americans.” Journal of Medical Ethics 33(3):177–180.

    Wilens, Timothy E., Lenard A. Adler, Jill Adams, Stephanie Sgambati, John Rotrosen, Robert Sawtelle, Linsey
    Utzinger, and Steven Fusillo. 2008. “Misuse and Diversion of Stimulants Prescribed for ADHD: A Systematic
    Review of the Literature.” Journal of the American Academy of Child and Adolescent Psychiatry 47(1): 21–31.

    Willcutt, Erik G. 2012. “he Prevalence of DSM-IV Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Review.”
    Neurotherapeutics 9(3):490–499.

    Zhang, Yanyan, Eileen Haddad, Bernadeth Torres, and Chuansheng Chen. 2011. “The Reciprocal Relationships
    Among Parents’ Expectations, Adolescents’ Expectations, and Adolescents’ Achievement: A Two-Wave
    Longitudinal Analysis of the NELS Data.” Journal of Youth and Adolescence 40(4):479–489.


    • Abstract
    • ADHD and social class

      Medicalization of ADHD

      Social class, ADHD, medicalization, and the academic ethic





      Dependent variables

      Independent variables: Socioeconomic status and demographic covariates

      Mental health covariates





    • Notes on contributors
    • Acknowledgment


    Chapter 9

    Applying Social Psychology to Education

    Chapter covered a lot of material and vocabulary. Only some covered here.

    When we think about performance, we have to consider attitudes and behaviors. The theory of planned behavior provides a useful framework in that it takes into account one’s attitudes, norms, and perceived control.

    Theory of Planned Behavior

    Academic self concept is also important
    Perceptions held by students about themselves/their academic ability

    it affects motivation and performance

    Internalized feelings about their own poor performance leads a student to create barriers to successful performance prior to (or at the same time) as an achievement task

    e.g. not studying before a test because “it wouldn’t matter anyway”

    Delaying the completion of a task or intended course of action

    Self determination theory
    Degree to which one sees themselves as autonomous
    Having sense of control
    Belonging in a social environment
    Being able to self-regulate

    Social Comparison Theory
    We judge our performance and abilities in comparison to others in our environment
    Objective standard vs. social standard

    We make comparisons to others to understand who we are

    Self-fulfilling prophecy
    Having expectations about another person that influences how you perceive and behave towards that person
    This interaction then effects how the person interacts with you
    Thus completing a repeating pattern

    Stereotype threat
    Anxiety experienced by students when faced with expectations consistent with stereotypes about their group

    In academia, women and underrepresented minorities sometimes talk about the imposter syndrome, i.e. that someone will figure out you are a fake. (though you have a Ph.D. just like everyone else).








    Expert paper writers are just a few clicks away

    Place an order in 3 easy steps. Takes less than 5 mins.

    Calculate the price of your order

    You will get a personal manager and a discount.
    We'll send you the first draft for approval by at
    Total price: