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SCS 502 Final Project Guidelines and Rubric
Overview
The final project for this course is the creation of a credibility analysis of a research publication. The project is divided into three milestones, which will be
submitted at various points throughout the course to scaffold learning and ensure quality final submissions. These milestones will be submitted in Modules Two, Four, and Six. The final product will be submitted in Module Nine.
In this assignment, you will demonstrate your mastery of the following course outcomes:
● Analyze research designs used in the social sciences for how they produce valid and credible research
● Select research publications relevant to specific topics following appropriate standards and guidelines in the social sciences ● Assess the appropriateness of the claims made in published studies relevant to specific topics in the social sciences
● Interpret evidence in peer-reviewed research that support personal claims regarding a specific social sciences topic
Prompt
For this assessment, you will analyze and review a series of research publications for credibility, how the experiments have been designed, and how the authors’ results sections inform or do not inform their conclusions. You will choose a topic area which has three preselected research articles associated with it to perform your analysis; later in the assessment, you will be asked to locate an additional article that relates to this topic area. Following your analysis of all of the articles, you will reflect on them holistically and make claims about how they contribute to the understanding of the topic, and what future research into the topic area could explore.
Specifically, the following critical elements must be addressed:
I. Overview: For this part of the assessment, you will summarize the important information in the preselected studies that you have chosen in order to guide your analysis later in the assessment. This will include how the research was designed, as well as the results and conclusions that were reached.
A. Describe the research design of each of the preselected studies that you have chosen. Be sure to include the specific methodology as well as the hypothesis, the variables and details on the selection of participants.
B. Describe the results of each of the studies that you chose and how the results of the studies support or do not support the research hypothesis.
C. Describe the conclusions that the authors in each of the chosen studies reached based on their results.
II. Research Analysis: For this part of the assessment, you will begin your analysis of the studies that you have selected using the information that you have previously collected. Your analysis will focus on the research design of the studies, and how the conclusions are or are not supported by the results of the studies. Following your analysis of the preselected studies, you will research and select an additional study which pertains to the topic area.

 A. Compare the research methodologies used in your chosen studies and explain why these methodologies were used over other methodologies. In other words, in your comparison you could include why methodologies are informed by the hypothesis created by the author of the study. Be sure to support your response with examples and support from the chosen studies.
B. Explain why the research designs of your chosen studies ensure that the study is or is not valid, reliable, and credible. Be sure to support your response with examples and support from the chosen studies.
C. Explain how the results of your chosen studies appropriately support the conclusions that were reached. If the results do not support the conclusions reached, briefly describe the more appropriate conclusions given the results presented. In other words, you could consider the results section and how the authors used that information to reach their conclusions, even if their claims are inaccurate.
D. Select an additional research article that aligns with the appropriate standards and guidelines used in the social sciences which would help inform future research into the topic area of the chosen studies. In other words, based on what the chosen studies have demonstrated about the topic area, what present research might inform future research in this area?
E. Explain how the article you chose follows the appropriate standards and guidelines of the field. Some questions to consider: Is the article peer reviewed? Is the article relevant to the topic? Is the article still currently relevant?
F. Explain how the research article you located would help inform future research into the topic area of your chosen studies. Be sure to support your response with examples and support from the chosen studies.
III. Reflection: For this part of the assessment, you will analyze the studies for how they contribute to their topic area. Be sure to provide examples and support from the studies presented in your research analysis and incorporate your personal claims and thoughts into your responses.
A. Describe how the evidence in the chosen studies contributes to the collective understanding of the topic.
B. Explain any new conclusions that you could reach about the topic area based on the evidence provided in your chosen studies. In other words, in
your interpretation of the evidence provided in the chosen studies, what conclusions can you reach that go beyond the research?
C. Predict what future research in the topic area of the chosen studies could explore.
Milestones
Milestone One: Reading & Summarizing
In Module Two, you will choose a set of preselected studies. Using these studies, you will summarize the important information in order to guide your analysis later in the course. This will include an overview of the methods used as well as the results and conclusions presented by the authors. This milestone will be graded with the Milestone One Rubric.
Milestone Two: Analyzing
In Module Four, you will begin your analysis of the previously selected studies using the information you presented in Milestone One. Your analysis will focus on a critique of the research design of the studies as well as the relationship between the design, the results generated from that design, and the conclusions drawn from those results. This milestone will be graded with the Milestone Two Rubric.
  
 Milestone Three: Expanding Upon the Topic
In Module Six, you will research and select an additional study which pertains to the topic area you selected in the earlier milestone assignments. You will also evaluate the three studies you selected for their contribution to their topic area. Finally, you will integrate the information presented in the published studies and the information learned in this class to draw your own conclusions about the topic area using appropriate evidence to support your claims. This milestone will be graded with the Milestone Three Rubric.
Final Project Submission: Research Publication Credibility Analysis
In Module Nine, you will submit your final project. It should be a complete, polished artifact containing all of the critical elements of the final product. It should reflect the incorporation of feedback gained throughout the course. This submission will be graded with the Final Project Rubric (below).
      Milestone
Deliverable
Deliverables Module Due
Two Four Six Nine
Grading
Graded separately; Milestone One Rubric
Graded separately; Milestone Two Rubric Graded separately; Milestone Three Rubric Graded separately; Final Project Rubric (below)
    One Reading & Summarizing Two Analyzing
        Three
Expanding Upon the Topic
Final Submission: Research Publication Credibility Analysis
        
 Final Project Rubric
Guidelines for Submission: Your research analysis must be double spaced, with 12-point, Times New Roman font and research used is appropriately cited in APA
format.
Critical Elements Exemplary Proficient Needs Improvement Not Evident Value
                    Overview: Research Design [SCS-502-01]
Describes the research design of each of the selected studies and includes in response the hypothesis statement, independent and dependent variables, and participant selection (100%)
Describes the research design of each of the selected studies but description is cursory or is missing key elements (70%)
Does not describe the research design of the selected studies (0%)
8
              Overview: Results
[SCS-502-03]
Describes the results of each of the chosen studies and how the results support or do not support the research hypothesis (100%)
Describes the results of each of the chosen studies and how the results support or do not support the research hypothesis but description is cursory or contains inaccuracies (70%)
Does not describe the results of each of the chosen studies and how the statistics support or do not support the research hypothesis (0%)
8
             Overview: Conclusions [SCS-502-03]
Meets “Proficient” criteria and description demonstrates keen insight into the thought process used by the authors of the studies (100%)
Describes the conclusions that the authors in each of the studies reached based on their results (90%)
Describes the conclusions that the authors in each of the studies reached based on their results but description is cursory or contains inaccuracies (70%)
Does not describe the conclusions that the authors in each of the studies reached based on their results (0%)
8
             Research Analysis: Research Methodologies [SCS-502-01]
Meets “Proficient” criteria and explains the appropriateness of the research design based on the hypotheses of the chosen studies (100%)
Compares the research methodologies used in the chosen studies and explains why those methodologies were used, supporting response with examples and support from the research (90%)
Compare the research methodologies used in the chosen studies and explains why those methodologies were used but explanation is cursory, contains inaccuracies, or does not support response with examples and support from the research (70%)
Does not compare the research methodologies used in the chosen studies and explain why those methodologies were used (0%)
8
       
    Research Analysis: Valid, Reliable, and Credible [SCS-502-01]
   Meets “Proficient” criteria and evidence referenced from the chosen studies makes cogent connections between how the methodology of a study ensures that it is or is not valid, reliable, and credible (100%)
   Explains why the methodologies of the chosen studies ensure that the study is or is not valid, reliable, and credible, supporting response with examples and support from the research (90%)
  Explains why the methodologies of the chosen studies ensure that the study is or is not valid, reliable, and credible but explanation is cursory, contains inaccuracies, or does not support response with examples and support from the research (70%)
   Does not explain why the methodologies of the chosen studies ensure that the study is or is not valid, reliable, and credible
(0%)
    8
   Research Analysis: Support [SCS-502-03]
 Meets “Proficient” criteria and explanation demonstrates keen insight into the thought process used by the authors of the studies (100%)
 Explains how the results of the chosen studies appropriately support the conclusions that were reached or if the results do not support the conclusions reached, explains more appropriate conclusions given the results presented (90%)
Explains how the results of the chosen studies appropriately support the conclusions that were reached or if the results do not support the conclusions reached, explains more appropriate conclusions given the results presented but explanation is cursory or contains inaccuracies (70%)
 Does not explain how the results of the chosen studies appropriately support the conclusions that were reached (0%)
  8
   Research Analysis: Additional Research [SCS-502-02]
       Selects an additional research article that aligns with the appropriate standards and guidelines of the field which would help inform future research into the topic area of the chosen studies (100%)
  Selects an additional research article which would help inform future research into the topic area of the chosen studies but selection contains inaccuracies or does not follow the guidelines of the field (70%)
   Does not select an additional research article which would help inform future research into the topic area of the chosen studies (0%)
    8
   Research Analysis: Appropriate Standards and Guidelines [SCS-502-02]
 Meets “Proficient” criteria and explanation demonstrates a sophisticated ability to determine how research follows the appropriate standards and guidelines of the field (100%)
 Explains how the article chosen follows the appropriate standards and guidelines of the field (90%)
Explains how the article chosen follows the appropriate standards and guidelines of the field but explanation is cursory or contains inaccuracies (70%)
 Does not explain how the article chosen follows the appropriate standards and guidelines of the field (0%)
  8
   Research Analysis: Future Research [SCS-502-02]
   Meets “Proficient” criteria and explanation demonstrates keen insight into what the future research in the topic area could explore (100%)
   Explains how the research article you located would help inform future research into the topic area of your chosen studies (90%)
  Explains how the research article you located would help inform future research into the topic area of your chosen studies but explanation is cursory or contains inaccuracies (70%)
   Does not explain how the research article you located would help inform future research into the topic area of your chosen studies (0%)
    8
 
    Reflection: Collective Understanding [SCS-502-04]
  Meets “Proficient” criteria and description demonstrates keen insight into how the chosen studies collectively contribute to the shared understanding of the topic area (100%)
    Describes how the evidence in the chosen studies contributes to the collective understanding of the topic, supporting response with examples and support from the research and personal claims (90%)
  Describes how the evidence in the chosen studies contributes to the collective understanding of the topic but description is cursory, contains inaccuracies, or does not support response with examples and support from the research and personal claims (70%)
   Does not describe how the evidence in the chosen studies contributes to the collective understanding of the topic (0%)
    8
   Reflection: Conclusions [SCS-502-04]
 Meets “Proficient” criteria and explanation provides unique and insightful conclusions regarding the topic area that are appropriately supported by the research (100%)
   Explains the new conclusions that could be reached about the topic area based on the evidence provided in your chosen studies supporting response with examples and support from the research and personal claims (90%)
 Explains the new conclusions that could be reached about the topic area based on the evidence provided in your chosen studies but explanation is cursory, contains inaccuracies, or does not support response with examples and support from the research and personal claims (70%)
  Does not explain the new conclusions that could be reached about the topic area based on the evidence provided in your chosen studies (0%)
   8
   Reflection: Predict
[SCS-502-04]
Meets “Proficient” criteria and prediction demonstrates keen insight into what the future research in the topic area could explore (100%)
  Predicts what could be explored by future research in the topic area of the chosen studies, supporting response with examples and support from the research and personal claims (90%)
Explains the new conclusions that could be reached about the topic area based on the evidence provided in your chosen studies but explanation is cursory, contains inaccuracies, or does not support response with examples and support from the research and personal claims (70%)
 Does not explain the new conclusions that could be reached about the topic area based on the evidence provided in your chosen studies (0%)
  8
   Articulation of Response
  Submission is free of errors related to citations, grammar, spelling, syntax, and organization and is presented in a professional and easy-to-read format (100%)
    Submission has no major errors related to citations, grammar, spelling, syntax, or organization (90%)
  Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas (70%)
   Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas (0%)
    4
 Total 100%
1

Reading and Summarizing

Tamera Young
Department of psychology, Southern New Hampshire University
SCS-502-Q2378: Foundations of Research Methods
Professor Susan Depue
December 13, 2020

In the article Influences of motivational contexts on prescription drug misuse and related drug problems the researchers are looking to determine if young adults abuse prescription drugs because of motivational contexts. They are looking to find if things like relaxing, staying alert while socializing or negative effects create a need/want to abuse prescription drugs. (Martins et al., 2014)
In this study the hypothesis is if young adults score high scores on the motivational context they will be influenced to utilize prescription drugs as well as endure drug related problems and symptoms of dependency. Having greater scores can also have an indirect influence on prescription drug use and drug related problems and symptoms of dependency.
The independent variables are motivational context and the dependent variables are the frequency of prescription drug misuse an prescription drug related problems and symptoms of dependency.
Hypothesis 1: IV= motivational context DV= Frequency of prescription drug misuse
Hypothesis 2: IV=motivational context DV= Prescription drug related problems and symptoms of dependency.
The participants that were used in this study were 400 young adults with a mean age of 24.5 recruited via time-space sampling. The researchers utilized time space-sampling in wide range of venues that house nightlife scenes in New York City. Using a random digit generator, a random number was drawn corresponding to a particular venue on a particular day.
In the initial assessment participants completed the informed consent process, and then completed the survey via ACASI or in-house assessments and via Qualtirics for online
assessments. Once completed, participants were compensated $50 in cash, check or Amazon gift card, which ever they preferred.
According to the authors young adults in the nightlife are more likely to abuse prescription drugs due to a negative situation which can be a key driver of drug problems. Addressing negative situations with young adults may assist with helping to keep them from relapsing or starting to use drugs at all.
The night life is not the only place where young adults can access prescription drugs, researchers in this next article Nonmedical prescription drug use among US young adults by educational attainment the researchers are looking to find if education has any connection to young adults use of prescription drugs. (Martins et al., 2014)
Prescription drugs used among young adults based upon their educational attainment can affect their use of drugs positively or negatively. The researchers would also want to learn if there is a connection with NMUPO or nonmedical stimulant use involving racial/ethnic and male and female young adults.
Hypothesis 1: IV= Educational Level DV= Prescription drug use
Hypothesis 2: IV=Racial/Ethnic & Male or female DV= NMUPO or nonmedical stimulant use.
The independent variable in this study are education level and racial/ethnic and male or female. The dependent variables are prescription drug use and NMUPO or nonmedical stimulant use.
In this study, 36, 781 young adults between the ages of 18 and 22 were involved. They were chosen from the National Survey on Drug Use and Health public use files. They were also
chosen by the adjusted main effects for current educational attainment, along with its interaction with gender and race/ethnicity was considered.
Survey items were administered by computer-assisted personal interviewing (CAPI) conducted by an interviewer and audio computer-assisted self-interviewing (ACASI). Respondents were offered a $30 incentive payment or participation in the survey. All respondents provided information about their drug experiences and their socio demographic characteristics.
The results found that young adults will benefit from prevention and intervention programs that should target nonmedical prescription drug use beyond college campuses. The hypothesis was supported in this article.
According to the authors young adult nonmedical prescription drug use is not only an issue on college/school campuses. This appears to be an all-around issue with young adults.
In Ethnic/Racial Differences in Peer and Parent influence on Adolescent Prescription Drug Misuse the researchers are looking to study the misuse of prescription drugs based off of young adults ethnic/racial background and also how their parental and peer disapproval affects their decisions to indulge in prescription drug use. (Conn & Marks, 2014)
In this article the hypothesis is the misuse of prescription drugs based off of adolescent ethnic/racial background along with peer and parental disapproval may have a correlation to young adults likelihood of using prescription drugs.
Hypothesis 1: IV= Ethnic/Racial DV= Adolescent prescription drug use
Hypothesis 2: IV=Parental or peer DV= Adolescent prescription drug use
The independent variables are ethnic/racial and parental or peer. The dependent variables are Adolescent prescription drug use.
The samples used in this study was Adolescents aged 12 -17 years old in 50 states were chosen for this study. They self-identified as African American, Hispanic or White American. Half the study sample were males.
Researchers used computer-assisted interviewing methodology combines with computer-assisted interviewing personal interviewing (CAPI) and audio computer assisted self-interviewing (ACASI). For more sensitive questions pertaining to drug use, respondents listened to prerecorded questions through headphones and entered responses directly into the computer without interviewer observation or assistance. The sample is composed of adolescents who selected mono racial categories: non-Hispanic, black/African American, non-Hispanic white and Hispanic.
The study shows that substance use disapproval by important socialization agents such as family and peers may significantly influence NMUPD. The findings also state that parents continue to remain a vital part of adolescent’s decision making. The hypothesis was supported in this study.
According to the author Parental and peer disapproval of prescription drug misuse has an effect on young adults’ decisions to use drugs or not. Due to the researchers having a small number of minority samples the white participants had a greater number than the others which made it difficult to examine the numbers.

References

Conn, B. M., & Marks, A. K. (2014). Ethnic/Racial Differences in Peer and Parent Influence on Adolescent Prescription Drug Misuse. Journal of Developmental & Behavioral Pediatrics, 35(4), 257-265. doi:10.1097/dbp.0000000000000058
Kelly, B. C., Rendina, H. J., Vuolo, M., Wells, B. E., & Parsons, J. T. (2015). Influences of motivational contexts on prescription drug misuse and related drug problems. Journal of Substance Abuse Treatment, 48(1), 49-55. doi:10.1016/j.jsat.2014.07.005
Martins, S. S., Kim, J. H., Chen, L., Levin, D., Keyes, K. M., Cerdá, M., & Storr, C. L. (2014). Nonmedical prescription drug use among US young adults by educational attainment. Social Psychiatry and Psychiatric Epidemiology, 50(5), 713-724. doi:10.1007/s00127-014-0980-3

Original Article

Ethnic/Racial Differences in Peer and Parent Influence on
Adolescent Prescription Drug Misuse
Bridgid M. Conn, MA, Amy K. Marks, PhD

ABSTRACT: Purpose: To describe ethnic/racial group differences in prescription drug misuse within a na-
tionally representative sample of US adolescents. Also to identify potential sociocultural influences on this
health risk behavior. Methods: A secondary data analysis was conducted on the public-use data of the 2010
National Survey of Drug Use and Health using the records of 12- to 17-year-old African American, Hispanic,
and white participants (N 5 18,614). Logistic and Poisson regression analyses focused on examining the
predictive role of perceived parental and peer substance use disapproval on adolescents’ prescription drug
misuse and how these social influences varied by ethnic group. Results: Within this sample, 10.4% of ado-
lescents endorsed misusing 1 or more subtypes of prescription drugs. The results showed significant ethnic
group differences in rates of prescription drug misuse such that white adolescents reported the highest rates
and African American adolescents reported the lowest rates of prescription drug misuse. Rates of misuse for
Hispanic adolescents fell in between. Importantly, perceived parental and peer substance use disapproval
decreased the likelihood of prescription drug misuse most significantly among white adolescents compared
to Hispanic and African American adolescents. Furthermore, influence of disapproval differed by the type of
substance, indicating ethnic group differences in disapproval, such as views of alcohol versus marijuana use.
Conclusions: These findings provide new ethnic group–specific information about the role that the attitudes
of peers and parents on substance use may play in whether adolescents misuse prescription drugs. Future
studies should explore possible parent/peer-related socialization mechanisms, which may account for these
ethnic group differences.

(J Dev Behav Pediatr 35:257–265, 2014) Index terms: adolescents, substance abuse, prescription drugs, health risk, ethnic minority health.

Currently, misuse and abuse of prescription drugs is
one of the fastest growing drug epidemics in the United
States, particularly among adolescents.1 Misuse is com-
monly defined as both the use of prescription medi-
cations without a doctor’s prescription and use simply
for the (p.5)“experience or feeling the drug caused.2 ” In
2006, the number of US youth older than 12 years who
initiated nonmedical use of prescription opioids within
the span of a year was greater than the estimated number
of youth who initiated either illicit cannabis or cocaine
use during that same period.2 Significant increases in the
nonmedical use of prescription drugs (NMUPD) have led
to labeling today’s American adolescents as “Generation

Rx.3” Despite slight decreases in the NMUPD among
adults, rates among adolescents seem to remain stable,
warranting attention regarding the reasons US adoles-
cents remain active in NMUPD.4

From the Department of Psychology, Suffolk University, Boston, MA.

Received November 2013; accepted January 2014.

This study is based on secondary data analyses conducted as part of a doctoral
dissertation.

Presented as a poster in the 2013 Society for Child Development Conference in
Seattle, Washington, April 2013.

A. K. Marks received grants from the Jacobs Foundation. B. M. Conn received
funding from the American Psychological Association to attend the annual APA
conference and Psychology Summer Institute. B. M. Conn is employed in the Primary
Children’s Hospital and received payment for lectures from the Suffolk University.

Disclosure: The authors declare no conflict of interest.

Address for reprints: Amy K. Marks, PhD, Department of Psychology, Suffolk
University, 41 Temple St, Boston, MA 02114; e-mail: [email protected]

Copyright © 2014 Lippincott Williams & Wilkins

Vol. 35, No. 4, May 2014 www.jdbp.org | 257

In an effort to begin to understand which group of
adolescents may be at the greatest risk for prescription
drug misuse, significant differences in the rates of
NMUPD between white and ethnic minority adolescents
have been documented consistently in the recent litera-
ture.5,6 Specifically, white adolescents reported higher
rates of NMUPD than their non-white peers.7 In their
review, Young et al7 also noted several peer (e.g., peer
attitudes) and parental factors (e.g., parental bonding/
disapproval of drug use) associated with adolescent
NMUPD; however, a few studies have attempted to ex-
plore how such factors may explain these racial/ethnic
disparities. The current study is informed by theoretical
perspectives, which move beyond the traditional cross-
cultural “risk” models, which document between-group
differences without examining relevant mechanisms or
processes that may explain these discrepancies. In fact,
several seminal articles8,9 have highlighted such issues in
clinical sciences, in particular, the use of race/ethnicity
as an “explanatory construct” in and of itself. Among
their proposed guidelines for cultural research, Helms
et al9 call for researchers to move away from using race
as an “explanation” and to shift focus to culture-specific

ITdept
Sticky Note

factors (e.g., values) to explain the risk behaviors in the
development of any health issues rather than perpetuating
group-level comparisons.

In a review of theoretical models on culture and hu-
man development, Garcia Coll et al8 stated that the study
of culture is vital in the confirmation, creation, and ex-
pansion of theories of human development. Historically,
research in the United States has taken a universal or etic
approach to studying the impact of culture on normal
and abnormal development, leading to the propagation
of “deficiency models,” in other words, using race or
ethnicity to explain health disparities between white and
ethnic minority youth. However, Garcia Coll et al8 make
a strong case for examining culture-based environmental
influences as processes rather than a backdrop for de-
velopmental psychopathology. Thus, studies within
one culture or between cultures can provide vital
new information regarding the strength of cultural fac-
tors on the increased or decreased likelihood of the
development of the phenomena of interest. Taking
a within-culture, or emic, perspective may more effec-
tively document how particular cultural values, beliefs,
or practices specifically impact the cause and course of
developmental psychopathology.8 Thus, the current
study takes a culture-specific approach to understanding
NMUPD, extending previous research. More specifically,
we aim to examine one aspect of cultural socialization,
parent and peer attitudes, on adolescent NMUPD.
Methodologically, we use a within-group (or parallel
analysis)10 approach to allow for meaningful cross-
cultural differences in socialization patterns to emerge.

In adolescence, the attitudes and behaviors of peers
and parents have profound impacts on youths’ own
attitudes and behaviors related to drug use. In the pres-
ent literature, there is less information on peer disap-
proval compared to approval/acceptability. Disapproval
as a construct does not represent the opposite end of the
spectrum from approval and is, in fact, qualitatively dif-
ferent. However, some studies have used disapproval as
a way to capture a potential protective factor for ado-
lescents. For example, among a diverse sample of 8th,
10th, and 12th graders, perceived peer substance use
disapproval, particularly at the school level, decreased
the likelihood of cigarette, alcohol, and marijuana use.11

In addition, a study of predominantly African American
adolescents found that caregiver and peer disapproval
was negatively associated with early substance use initi-
ation.12 Other research with white, black, and Hispanic
adolescents highlights the influence of parental disap-
proval on adolescent substance use.13,14 A recent study
with an ethnically diverse sample of adolescents found
that the attitudes of close friends differentially predicted
the risk of cigarette, alcohol, and marijuana use. The
authors recommended further studies to better un-
derstand cultural processes that may influence adoles-
cent substance use behaviors.15

To date, ethnic group examinations of these in-
fluential social factors have been limited to studies of

tobacco, marijuana, and alcohol use among adolescents.
For example, peer substance use has been related to
cigarettes, marijuana, and alcohol use among US-born
Hispanic and African American adolescents.16,17 Ennett
et al18 found that African American and Hispanic ado-
lescents who reported having conversations with their
parents about the rules and discipline related to tobacco
use were less likely to start or increase the use of ciga-
rettes compared to white adolescents. However, the in-
fluence of peer and parent substance use attitudes on
adolescents’ NMUPD is not currently well understood.
Moreover, with the differing influence of these attitudes
on nonprescription drugs substances, such as tobacco
and alcohol, across adolescent racial/ethnic groups, little
research has been done in the way of examining how
these social influences translate to prescription drug
misuse.

Using data from the 2010 National Survey on Drug
Use and Health, we investigated the influence of per-
ceived parental and peer disapproval of various sub-
stance use on NMUPD among African American,
Hispanic, and white adolescents. Our primary goal was
to answer 3 questions: (1) Are there differences in rates
of NMUPD among ethnic/racial groups in this sample?
(2) Does perceived peer and parental substance use
disapproval influence adolescents’ NMUPD? (3) If so,
what are the observed differences in peer and parental
influence patterns across these ethnic groups?

METHODS
Procedures

The National Survey on Drug Use and Health
(NSDUH) is a national survey that uses multistage area
probability sampling methods to select a representative
sample of the US civilian, noninstitutionalized population
aged 12 years or older in all 50 states (N 5 57,873). For
the purposes of improving the precision of drug use
estimates for key subgroups, adolescents aged 12 to
17 years were oversampled. The 2010 sample (N 5
18,614) included 12- to 17-year-old participants who
self-identified as African American (n 5 2,486; 14.8%),
Hispanic (n 5 3,273; 19.4%), or white American (n 5
11,093; 65.8%). Approximately half of the study sample
was male (50.8%). The data collection method used in
NSDUH involves in-person interviews, incorporating
procedures that are likely to increase respondents’
cooperation and willingness to report honestly about
illicit behavior. More specifically, researchers used
computer-assisted interviewing to increase the likelihood
of valid respondent reports of drug use behaviors.19

Computer-assisted interviewing methodology combines
computer-assisted personal interviewing (CAPI) and
audio computer-assisted self-interviewing (ACASI).
However, ACASI is designed to provide the respondent
with a highly private and confidential means of respond-
ing to questions, and it is used for questions of a sensitive
nature, such as substance use.20 For more sensitive
questions pertaining to drug use, respondents listened to

258 Ethnic Differences in Prescription Drug Misuse Journal of Developmental & Behavioral Pediatrics

prerecorded questions through headphones and entered
responses directly into the computer without interviewer
observation or assistance. In addition, all identifying in-
formation was kept separate from survey responses and
respondents switched from ACASI to CAPI mode for
interviewers when they completed the questions.

Measures
Comparisons of using ACASI within NSDUH have

shown that it reduces reporting bias.21 Therefore, vari-
ables used in this study are considered to be based on
valid self-reports. A reliability study was conducted to
assess the reliability of responses to the 2010 NSDUH
questionnaire, and the results provide support for con-
tinued use of the survey items.22 For racial/ethnic
grouping, adolescents were asked to select a monoracial
category (e.g., non-Hispanic white) or a multiracial cat-
egory, non-Hispanic more than 1 race. Of note, 4% of
adolescents in this sample selected the multiracial cate-
gory, identifying with more than one ethnic/racial label.
Biracial African American adolescents who may have also
identified as Hispanic were not included in the multira-
cial group. In the present study, our sample is composed
of adolescents who selected monoracial categories: non-
Hispanic black/African American, non-Hispanic white,
and Hispanic.

Nonmedical Use of Prescription Drugs
Self-reported lifetime nonmedical use of stimulants,

sedatives, tranquilizers, and pain relievers were coded
either “0” (i.e., no reported use) or “1” (i.e., yes reported
use). Initial analyses revealed significant associations
between nonmedical use of prescription drugs (NMUPD)
and demographic variables (e.g., age, ethnic/racial
group). However, the number of responses per pre-
scription drug subtype was insufficient to run full model
logistic regressions for each ethnic/racial group by sub-
type independently; therefore, responses for all 4 drug
subtypes were summed to create a composite score and
recoded to a dichotomous variable (i.e., 0 5 no NMUPD;
1 5 reported lifetime misuse of at least 1 type of pre-
scription drug) for use in the full logistic regression models.

Perceived Parent Disapproval of Substance Use
The following independent items were used to mea-

sure perceived parental substance use disapproval: “How
do you think your parents would feel about you smoking
1 or more packs of cigarettes per day?” “How do you
think your parents would feel about you trying marijuana
or hashish once or twice?” “How do you think your
parents would feel about you using marijuana or hashish
once a month or more?” “How do you think your parents
would feel about you having 1 or 2 drinks of an alcoholic
beverage nearly every day?” These items were based
on a 3-point scale: 1 5 neither approve nor disapprove;
2 5 somewhat disapprove; 3 5 strongly disapprove. For
the purposes of analysis, responses were recoded to
a dichotomous variable: somewhat disapprove and
strongly disapprove5 1; neither approve nor disapprove
5 2. Reported perception of parental substance use

disapproval was contingent upon respondents’ indicated
presence of at least 1 parent but does not assume con-
stant cohabitation.

Perceived Peer Disapproval of Substance Use
The following independent items were used to mea-

sure perceived peer substance use disapproval: “How do
you think your close friends would feel about you
smoking 1 or more packs of cigarettes a day?” “How do
you think your close friends would feel about you trying
marijuana or hashish once or twice?” “How do you think
your close friends would feel about you using marijuana
or hashish once a month or more?” “How do you think
your close friends would feel about you having 1 or 2
drinks of an alcoholic beverage nearly every day?” These
items were based on a 3-point scale: 15 neither approve
nor disapprove; 2 5 somewhat disapprove; 3 5 strongly
disapprove. Similarly, responses were recoded to a di-
chotomous variable: somewhat disapprove and strongly
disapprove 5 1; neither approve nor disapprove 5 2.

Covariates
Variables identified in previous research on ethnic

disparities in health behaviors and that exhibited signif-
icant racial/ethnic differences in the current sample
were treated as covariates.23 These included socio-
demographic characteristics, such as gender (e.g., 1 5
male), age (e.g., 1 5 12–13 years old; 2 5 14–15 years
old), and total family income (e.g., 1 5 ,$10,000; 2 5
$10,000–$19,999; 3 5 $20,000–$29,999). There was
a significantly higher proportion of white adolescents
whose reported family level income above $75,000
(x2(4, N 5 16,852) 5 2642.56; p , .01) and whose
reported poverty status was $200% above the federal
poverty threshold (x2(6, N 5 16,852) 5 2610; p , .01)
as compared to Hispanic and African American adoles-
cents. In the NSDUH interview, adolescents were asked if
they would like a parent (or other adult household mem-
ber) to answer questions about income and insurance.
Approximately 87% of adolescents in NSDUH 2010 opted
to have an adult answer these items rather than answer
themselves24; thus, this variable is generally considered to
be an accurate assessment of household income.

Analyses
We observed demographic distributions and preva-

lence rates of NMUPD for Hispanic, African American,
and white adolescents. Next, bivariate associations were
examined with x2 tests for categorical variables (e.g.,
gender). Finally, we conducted a series of Poisson and
logistic regressions for each ethnic group to predict the
likelihood of NMUPD based on perceived parental and
peer substance use disapproval. Each item (described
above) was entered hierarchically into the regression
model with socioeconomic and gender variables entered
in the first step and perceived substance use disapproval
items entered in the second step. Parent and peer mod-
els were analyzed separately. As we ran analyses sepa-
rately by ethnic/racial group, unweighted data were

Vol. 35, No. 4, May 2014 © 2014 Lippincott Williams & Wilkins 259

used. Results are presented using odds ratios as the
measure of effect size.

RESULTS
Descriptive Statistics

Among the total adolescents in the 2010 National Sur-
vey on Drug Use and Health (NSDUH) sample, 10.4%
endorsed nonmedical use of 1 or more prescription drug
subtypes (i.e., pain relievers, stimulants, sedatives, and
tranquilizers). Across these subtypes, adolescents gener-
ally reported greater nonmedical stimulant use than non-
medical sedative, tranquilizer, and pain killer use
combined. Chi-square analyses of nonmedical use of pre-
scription drugs (NMUPD) revealed significant differences
across ethnic groups specifically for stimulant (x2(2, N 5
360) 5 12.01; p , .01) and tranquilizer prescription drug
misuse (x2(2, N 5 495) 5 44.89; p , .01). White ado-
lescents reported significantly higher rates of misuse for
stimulants (2.4%) and tranquilizers (3.4%) compared to
either African American or Hispanic adolescents. Con-
versely, African American adolescents reported the lowest
rates of stimulant (1.3%) and tranquilizer (0.9%) misuse of
all 3 groups. Reported misuse of stimulants (2.1%) and
tranquilizers (2.9%) among Hispanic adolescents fell be-
tween the rates of misuse for white and African American
adolescents. Table 1 shows correlations between NMUPD
by subtype and demographic characteristics in the
NSDUH adolescent sample.

Regression Analyses
Contrary to previous findings, as income increased,

the total likelihood of NMUPD decreased. Adolescents
who reported higher family income were significantly
less likely to endorse misuse of pain relievers, tranquil-
izers, and sedatives. Stimulant misuse was not related
to adolescents’ reported family income. In terms of
income level, adolescents did not significantly differ by

ethnic/racial group for the majority of income levels. The
percentage of white adolescents increased with each
income bracket and was significantly greater than Afri-
can American and Hispanic adolescents for households
earning $75,000 or greater (p , .01). Older adolescents
were significantly more likely to report prescription drug
misuse compared to younger adolescents. For example,
older white adolescents were more than twice as likely
to report tranquilizer misuse compared to their younger
counterparts. Finally, girls were more likely to report
prescription drug misuse than boys, regardless of age.
Table 2 provides odds ratios (ORs) of adolescents’
NMUPD based on perceived close friends’ and parents’
substance use disapproval by substance type according
to adolescents’ gender and age.

Our analyses revealed distinct ethnic differences in
the nonmedical use of specific types of prescription
drugs. Consistent with previous research, white adoles-
cents were significantly more likely to report NMUPD,
specifically of both pain relievers (OR 5 1.1; confidence
interval [CI], 1.01–1.27) and tranquilizers (OR 5 2.2; CI,
1.78–2.76). In other words, white adolescents were
more than twice as likely to report nonmedical tran-
quilizer use compared to non-white adolescents. Con-
versely, African American adolescents were almost 500%
less likely to report nonmedical use of tranquilizers than
their white and Hispanic peers. Additionally, although
white adolescents were 70% more likely to misuse pain
relievers than non-white adolescents, African American
adolescents were 18% less likely to misuse pain relievers
than their non-African American counterparts.

Perceived Parental Disapproval of Substance Use
Table 3 shows the odds of NMUPD based on per-

ceived parental disapproval by substance subtype across
the ethnic/racial groups. White adolescents who en-
dorsed stronger perceived parental disapproval of

Table 1. Spearman’s Rho Correlation Coefficients of Nonmedical Prescription Drug Use (and by Type), Poverty Level, and Income Indicators, and
Demographic Characteristics (i.e., Age, Gender, Ethnicity)

Variables 1 2 3 4 5 6 7 8 9 10 11 12

1 Nonmedical pain reliever use —

2 Nonmedical tranquilizer use .433* —

3 Nonmedical stimulant use .310* .311* —

4 Nonmedical sedative use .179* .189* .191* —

5 Total family income 2.048* 2.039* 2.012 2.036* —

6 Poverty level 2.038* 2.032* 2.007 2.031* .846* —

7 Age .138* .120* .086* .017** .002 .014 —

8 Gender .030* .029* .024* .012 2.017** 2.016** 2.009 —

9 Any nonmedical prescription drug
misuse

.924* .514* .437* .246* 2.049* 2.044* .146* .036* —

10 Hispanic ethnicity .010 2.001 2.003 .010 2.237* 2.266* 2.010 .008 .013 —

11 White ethnicity 2.002 .038* .022* 2.006 .386* .394* .000 2.012 2.005 2.681* —

12 Black ethnicity 2.008 2.050* 2.025* 2.004 2.251* 2.230* .011 .007 2.008 2.204* 2.577*

*p , .01, 2 tailed. **p , .05, 2 tailed.

260 Ethnic Differences in Prescription Drug Misuse Journal of Developmental & Behavioral Pediatrics

Table 2. Age and Gender Differences in Logistic Regression Analyses Predicting Prescription Drug Misuse Based on Perception of Parents’ and Close
Friends’ Substance Use Disapproval Among White, Hispanic, and African American Adolescents

smoking 1 or more packs of cigarettes a day were more
than 250% less likely to report NMUPD than their white
peers who perceived less disapproval or neither ap-
proval nor disapproval. Moreover, white adolescents
who perceived strong parental disapproval of trying
marijuana (OR 5 2.02; CI, 1.36–2.97) and using mari-
juana monthly (OR 5 1.73; CI, 1.12–2.70) were also less
likely to report NMUPD compared to their peers. His-
panic adolescents who reported stronger perceived
parent disapproval of monthly marijuana use were 400%
less likely to endorse NMUPD compared to Hispanic
adolescents who perceived lesser parental disapproval or
that parents neither approved nor disapproved. In addi-
tion, Hispanic adolescents who perceived stronger pa-
rental disapproval of drinking alcohol daily were 200%
less likely to endorse NMUPD than those who did not.
Finally, African American adolescents who perceived
stronger parental disapproval of drinking alcohol daily
were 285% less likely to report NMUPD than those who
perceived lesser parental disapproval or that parents
neither approved nor disapproved.

b SE Wald Odds Ratio (95% Confidence Interval)

White

Parental disapproval

Age .66 .04 226.49** 1.94 (1.78–2.12)

Gender 2.28 .07 17.96** 0.76 (0.67–0.86)

Close friends’ disapproval

Age .47 .05 98.21** 1.60 (1.46–1.75)

Gender 2.44 .07 42.50** 0.64 (0.56–0.73)

Hispanic

Parental disapproval

Age .56 .07 56.70** 1.75 (1.51–2.02)

Gender 2.13 .12 1.32 0.88 (0.70–1.10)

Close friends’ disapproval

Age .47 .08 37.14** 1.60 (1.37–1.85)

Gender 2.22 .12 3.65 0.80 (0.64–1.01)

African American

Parental disapproval

Age .41 .09 21.65** 1.51 (1.27–1.79)

Gender 2.40 .14 7.95** 0.67 (0.51–0.89)

Close friends’ disapproval

Age .34 .09 14.32** 1.40 (1.18–1.67)

Gender 2.45 .14 9.84** 0.64 (0.49–0.85)

**p , .01.

Vol. 35, No. 4, May 2014 © 2014 Lippincott Williams & Wilkins 261

Close Friends’ Disapproval of Substance Use
As presented in Table 3, white adolescents who per-

ceived close friends’ strong disapproval of monthly
marijuana use were more than 230% less likely to report
NMUPD compared to their peers who did not perceive

such strong disapproval. Additionally, white adolescents
who perceived that close friends strongly disapproved of
trying marijuana (OR 5 1.86; CI, 1.40–2.47), smoking 1
or more packs of cigarettes a day (OR 5 1.33; CI, 1.08–
1.63), or drinking alcohol daily (OR 5 1.25; CI, 1.02–
1.53) were less likely to endorse NMUPD than those who
did not. For Hispanic and African American adolescents,
most of these perceived peer disapproval items did not
significantly predict NMUPD. However, Hispanic ado-
lescents who perceived disapproval (either some disap-
proval or strong disapproval) of smoking marijuana
monthly from their close friends were less likely to re-
port NMUPD (OR 5 1.81; CI, 1.08–3.03) than those who
reported neither approval nor disapproval. For both
ethnic groups, age and gender appeared to account for
the majority of variance compared to perceived peer
substance use disapproval in reported NMUPD.

DISCUSSION
Until recently, research on nonmedical use of pre-

scription drugs (NMUPD) misuse among adolescents was
limited to cross-cultural descriptions of prevalence. The
present study confirmed the presence of these ethnic
group differences and novel findings that suggest that
perceived peer and parental substance use disapproval
uniquely influence adolescent NMUPD. In sum, results
were consistent with previous research7 such that white

Table 3. Logistic Regression Analyses Predicting Prescription Drug Misuse Based on Perception of Parents’ and Close Friends’ Substance Use
Disapproval Among White, Hispanic, and African American Adolescents

adolescents demonstrated the highest rates of NMUPD
compared to African American and Hispanic adolescents.
As previous studies of adolescent alcohol, tobacco, and
illicit drug use have similarly demonstrated,2,25,26 in the
present study, African American adolescents demon-
strated the lowest rates of NMUPD, whereas rates of
NMUPD among Hispanic adolescents fell between those
of the other groups. We observed that higher income
was related to lower adolescent NMUPD misuse and that
African American adolescents were significantly less
likely than both white and Hispanic adolescents to mis-
use prescription drugs. Thus, these results seem to sug-
gest that while income is an important factor in the

increased risk for NMUPD, it does not fully account for
the observed differences among African American, His-
panic, and white adolescents. Previous studies posit that
lower rates of NMUPD among Hispanic and African
American adolescents may be related to their decreased
likelihood of being prescribed medications in the first
place.26 Other research suggests that messages from the
immediate environment (e.g., home), such as accept-
ability of drug use, may significantly influence adolescent
substance use behaviors.27

b SE Wald Odds Ratio (95% Confidence Interval)

White

Parental disapproval

Smoking 11 packs of cigarettes/day .93 .15 38.99* 2.52 (1.89–3.37)

Trying marijuana .70 .20 12.00* 2.02 (1.36–3.00)

Smoking marijuana monthly .55 .28 5.84* 1.73 (1.11–2.70)

Drinking alcohol daily .31 .18 2.91 1.36 (0.96–1.94)

Close friends’ disapproval

Smoking 11 packs of cigarettes/day .28 .12 7.33* 1.33 (1.08–1.63)

Trying marijuana .62 .15 18.14* 1.86 (1.40–2.50)

Smoking marijuana monthly .83 .15 31.24* 2.30 (1.71–3.07)

Drinking alcohol daily .22 .10 4.55** 1.25 (1.02–1.53)

Hispanic

Parental disapproval

Smoking 11 packs of cigarettes/day 2.23 .34 0.45 0.80 (0.41–1.55)

Trying marijuana 2.52 .54 1.01 0.59 (0.21–1.67)

Smoking marijuana monthly 1.40 .51 7.53* 4.05 (1.49–10.99)

Drinking alcohol daily .73 .32 5.31** 2.01 (1.12–3.89)

Close friends’ disapproval

Smoking 11 packs of cigarettes/day .02 .19 0.01 1.02 (0.71–1.48)

Trying marijuana .43 .26 2.64 1.54 (0.92–2.58)

Smoking marijuana monthly .59 .26 5.04** 1.81 (1.08–3.03)

Drinking alcohol daily .15 .18 0.67 1.17 (0.81–1.66)

African American

Parental disapproval

Smoking 11 packs of cigarettes/day .04 .40 0.10 1.04 (0.48–2.27)

Trying marijuana .26 .55 0.23 1.30 (0.44–3.84)

Smoking marijuana monthly 2.50 .58 0.74 0.61 (0.20–1.89)

Drinking alcohol daily 1.05 .47 4.91** 2.85 (1.13–7.19)

Close friends’ disapproval

Smoking 11 packs of cigarettes/day .38 .23 2.69 1.47 (0.93–2.32)

Trying marijuana 2.00 .32 0.00 0.98 (0.54–1.86)

Smoking marijuana monthly .48 .32 2.19 1.61 (0.86–3.05)

Drinking alcohol daily .07 .25 0.07 1.07 (0.66–1.73)

*p , .01, **p , .05.

262 Ethnic Differences in Prescription Drug Misuse Journal of …

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

DOI 10.1007/s00127-014-0980-3

ORIGINAL PAPER

Nonmedical prescription drug use among US young adults
by educational attainment

Silvia S. Martins • June H. Kim • Lian-Yu Chen •

Deysia Levin • Katherine M. Keyes •

Magdalena Cerdá • Carla L. Storr

Received: 19 May 2014 / Accepted: 10 November 2014 / Published online: 27 November 2014

© Springer-Verlag Berlin Heidelberg 2014

Abstract

Purpose Little is known about nonmedical use of pre-

scription drugs among non-college-attending young adults

in the United States.

Methods Data were drawn from 36,781 young adults (ages

18–22 years) from the 2008–2010 National Survey on Drug

Use and Health public use files. The adjusted main effects for

current educational attainment, along with its interaction

with gender and race/ethnicity, were considered.

Results Compared to those attending college, non-col-

lege-attending young adults with at least and less than a HS

degree had a higher prevalence of past-year nonmedical

use of prescription opioids [NMUPO 13.1 and 13.2 %,

respectively, vs. 11.3 %, adjusted odds ratios (aORs) 1.21

(1.11–1.33) and 1.25 (1.12–1.40)], yet lower prevalence of

prescription stimulant use. Among users, regardless of drug

type, non-college-attending youth were more likely to have

past-year disorder secondary to use [e.g., NMUPO 17.4 and

19.1 %, respectively, vs. 11.7 %, aORs 1.55 (1.22–1.98)

and 1.75 (1.35–2.28)]. Educational attainment interacted

with gender and race: (1) among nonmedical users of

prescription opioids, females who completed high school

but were not enrolled in college had a significantly greater

risk of opioid disorder (compared to female college stu-

dents) than the same comparison for men; and (2) the risk

for nonmedical use of prescription opioids was negligible

across educational attainment groups for Hispanics, which

was significantly different than the increased risk shown for

non-Hispanic whites.

Conclusions There is a need for young adult prevention

and intervention programs to target nonmedical prescrip-

tion drug use beyond college campuses.

Keywords Nonmedical prescription drug use · Drug use
disorders · Educational attainment · Young adults · Gender
differences

S. S. Martins (&) · J. H. Kim · D. Levin ·
K. M. Keyes · M. Cerdá
Department of Epidemiology, Mailman School of Public Health,

722 West 168th Street, Rm. 509, New York, NY 10032, USA

e-mail: [email protected]

L.-Y. Chen

Taipei City Psychiatric Center, Taipei City Hospital, Taipei,

Taiwan

C. L. Storr

Department of Family and Community Health, University of

Maryland School of Nursing, Baltimore, USA

123

Introduction

Nonmedical prescription drug use—use without a pre-

scription or use with a prescription but in a manner other

than how prescribed—is the fastest growing drug problem

in the US [1], driven primarily by nonmedical use of pre-

scription opioids (NMUPO) among younger cohorts [2].

While a large proportion of young adults (age 18–22) are

prescribed opiates (PO) and stimulants for legitimate health

conditions [3–7], NMUPO is second only to marijuana as

the most prevalent form of illegal drug use among young

adults, and a third of persons with opiate disorders sec-

ondary to PO use in 2011 were young adults [8]. Non-

medical use of prescription stimulants is also of concern

among young adults [5–7, 9]. Moreover, this age group is

particularly vulnerable to the development of adverse

substance using patterns, due in part to the process of

identity formation that emerges at this developmental

stage, and a greater level of independence compared to

adolescence [10].

A limitation of many studies on nonmedical prescription

drug use (particularly opiates) among young adults is that

their samples are limited to select segments of the young

adult population. For example, a few studies have exam-

ined NMUPO in community samples of high-risk young

adults (i.e., injection drug users) in urban settings, but none

of these studies have compared estimates to young adults in

the general population [11–16]. Problems related to sub-

stance use on college campuses have also been a central

focus of research on alcohol [17, 18], nonmedical stimulant

use [5–7, 19–22], and NMPO [7, 23, 24] use among college

students. However, many young adults are not seeking a

college education [25]. It has been estimated that among

those completing their secondary education, about 70 %

enroll in further education: 42 % enroll in 4-year institu-

tions and 28 % at 2-year institutions right after graduating

high school [26]. College-based studies also exclude sig-

nificant proportions of minority young adults [27]. The

National Center for Education Statistics indicates that high

school dropout rates are particularly high for non-Hispanic

(NH) black and Hispanic students, as well as for those who

are the first in their family to attend college, and those who

have limited English proficiency [28]. Nationally, about

75 % of all students graduate from high school on time

with a regular diploma, but barely half of non-Hispanic

black and Hispanic students earn diplomas with their peers

[29]. Thus, a substantial proportion of young adults fall

outside the purview of college-based studies and there is a

need to further compare the prevalence of nonmedical

prescription use of opiates and stimulants and disorders

secondary to their use by race/ethnicity between young

adults who attend college versus those who do not attend

college. Notably, most prevention programs to reduce

substance use among young adults are designed for college

settings; a comparison between college- and non-college-

attending young adults would illuminate specific issues that

need to be adapted for prevention programs targeting non-

college-attending youth [30].

It is also important to investigate whether there are any

racial/ethnic and male–female differences in NMUPO or

nonmedical stimulant use within subgroups of young adults

with similar educational attainment levels. Lifetime and

past-year drug use disorders have been consistently asso-

ciated with lower educational attainment and minority

status [31–35]. Studies suggest that individuals with less

years of formal education are at high risk of becoming drug

dependent [32, 34] and of experiencing persistent depen-

dence, in contrast to those with more years of formal

education [34]. Among college students, whites are more

likely than the students of other race/ethnicities to be

nonmedical stimulant and prescription opioid users [5, 7,

23, 24, 36, 37]. The evidence on potential gender differ-

ences in nonmedical prescription drug use among young

adults has been mixed—some studies find no difference,

others have found a higher prevalence in males, and others

in females [7, 8, 23, 24, 36–39]. Very few studies have

investigated male–female differences in prescription stim-

ulant and prescription opioid disorders secondary to non-

medical use in young adults [40, 41]. Thus, examining how

college attendance modifies gender differences might

inform the mixed results on the association between gender

and nonmedical prescription drug use. This study aims to

examine racial/ethnic and male–female differences in

nonmedical prescription use of opiates and stimulants as

well as on disorders secondary to their use among young

adults by different educational attainment.

The goals of this study are to explore whether non-

medical prescription drug use (specifically, opioids and

stimulants) and disorders secondary to the drug use varies

by education and examine race/ethnic and male–female

differences within educational subgroups using data

obtained from nationally representative samples of

18–22 year olds residing in the US. Specifically, we sought

to: (1) compare the 12-month prevalence of nonmedical

use of prescription opioids and stimulants as well as the

prevalence of opioid and stimulant disorder secondary to

nonmedical use among non-college-attending young adults

versus their college-attending peers adjusting for demo-

graphics and past-year serious psychological distress, and

(2) test for risk differences of nonmedical use and disorder

among males and females separately and racial/ethnic

groups stratified by educational attainment in this popula-

tion. Our models also adjust for the presence of psycho-

logical distress because POs have been found to be used

nonmedically to self-medicate negative emotions among

young adults [42] and several studies have shown that

NMUPO can be related to psychological distress in general

population samples [43–48].

Materials and methods

Study sample and measures

We analyzed data from 36,781 young adults between the

ages of 18 and 22 from the 2008 (n = 57,739), 2009

(n = 55,772), and 2010 (n = 57,873) NSDUH public use

files; three consecutive NSDUH years were combined to

increase the sample size. The NSDUH is an annual cross-

sectional survey sponsored by the Substance Abuse and

Mental Health Administration (SAMHSA) and is designed

to provide estimates of the prevalence of drug use and

disorders in the household population of the US among

those 12 years old and older [49]. Annually the survey

selects an independent multistage area probability sample

for each of the 50 states and the District of Columbia.

714 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

African-Americans, Hispanics, and young people were

over-sampled to increase the precision of estimates for

these groups. The response rate for household screening

was 88 % and the weighted response rate was 74.8 % for

completed interviews across 3 years [50]. Survey items

were administered by computer-assisted personal inter-

viewing (CAPI) conducted by an interviewer and audio

computer-assisted self-interviewing (ACASI). Use of

ACASI was designed to provide respondents with a highly

private and confidential means of responding to questions

and to increase the level of honest reporting of drug use and

other sensitive behaviors [51]. Respondents were offered a

$30 incentive payment for participation in the survey.

Detailed information about the sampling and survey

methodology of NSDUH are found elsewhere [8, 35, 49].

All respondents provided information about their drug

experiences and their sociodemographic characteristics.

The NSDUH questionnaire instrument has sensitivity val-

ues ranging from 0.8 to 0.97 for most substances, and

specificity values of 0.7–0.95 [35, 52].

Outcome variables: nonmedical prescription opioid

and stimulant use and disorders secondary to use

NMUPO was defined as any self-reported use of pre-

scription pain relievers that were not prescribed for the

respondent or that the respondent took only for the expe-

rience or feeling they caused [49]. To reduce false-positive

responses, all respondents were given the following

instructions: ‘‘These questions are about prescription pain

reliever use. We are not interested in your use of over-the-

counter pain relievers that can be bought in stores without a

doctor’s prescription.’’ Past-year NMUPO was defined

based on the response to the following question: ‘how long

has it been since you last used any prescription pain

reliever that was not prescribed for you or that you took

only for the experience or feeling it caused.’ If the

respondent answered positively, they were classified as a

lifetime NMUPO user. Then, if the response indicated that

nonmedical use occurred during the preceding 12 months,

the respondent was classified as a past-year NMUPO user.

The survey used discrete questions and a card with pictures

of many types of prescription opioids. The respondents

were asked which ones he/she had used, as well as the

frequency of use.

Similarly, the NSDUH used a screening question that

assessed whether the respondent had ever used a pre-

scription stimulant that was not prescribed, or taken one for

the experience or feeling it caused. If the response was

positive, the respondent was given a card with pictures of

many types of prescription stimulant sand was asked which

ones he/she had used, as well as the frequency of use. Then,

if the response indicated that nonmedical use occurred

during the preceding 12 months, the respondent was clas-

sified as a past-year prescription stimulant user.

Respondents with NMUPO in the past-year were asked a

set of 17 structured questions designed to operationalize

DSM-IV criteria [53] for past-year opioid abuse and

dependence secondary to NMUPO (referred together as

OD secondary to NMUPO in this manuscript). Similar

questions were asked to operationalize DSM-IV criteria

[50] for past-year stimulant abuse and dependence sec-

ondary to prescription stimulant use (referred together as

prescription stimulant disorder in this manuscript).

Primary exposure variable: educational attainment

Current educational attainment was operationalized in the

NSDUH as: (1) current college student, (2) high school

graduate/GED [general education certification], (3) did not

complete high school (this information was only asked for

18- to 22-year-old respondents). There was no information

on whether respondents were attending 2-year (community

colleges) or 4-year colleges.

Demographic covariates

Demographic variables selected for this study included

gender, race/ethnicity (non-Hispanic white, non-Hispanic

African American, Native American/Hawaiian/Pacific

Islander, Asian, more than one race, Hispanic), and whe-

ther they resided in a large metro, small metro or non-

metropolitan statistical area. We recognize that for some

racial/ethnic groups sample sizes of respondents (particu-

larly for disorders) will be small, but simply combining

these groups into an ‘‘Other’’ group would prohibit us from

exploring potential prevalence disparities that might exist.

Past-year serious psychological distress

Serious psychological distress (SPD) was measured using

the Kessler 6 (K6) screening instrument for nonspecific

psychological distress. The K6 scales were designed to

maximize the ability to discriminate cases of SPD from

non-cases [54]. The tool consists of six items, each with a

0–4 point rating scale that screens for general distress in the

past year. It has excellent internal consistency and reli-

ability (Cronbach’s alpha = 0.89). In all years, respon-

dents were classified as past-year SPD if the totaled

summed score was 13 or greater [54].

Statistical analyses

Data were weighted to reflect the complex design and

multiple years of the NSDUH sample and were analyzed

using Stata 11.0 [55] and SUDANN [59] software

Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 715

123

(specifically used for interaction-testing). We used Taylor

series estimation methods to obtain proper standard error

estimates for the cross-tabulations and logistic regressions.

All percentages reported are weighted by study weights.

Because we analyzed data from three NSDUH years

combined, weights were divided by three (number of years

of data combined) as recommended by SAMHSA [8].

Exploratory data analyses did not show any statistically

significant differences in the NSDUH samples across years,

justifying combining the data from multiple years. After

basic contingency tables were created, we ran logistic

models that included covariates (adjusted for demographics

and past-year serious psychological distress) to compare

the prevalence of past-year NMUPO, past-year nonmedical

prescription stimulant use, past-year OD secondary to

NMUPO, and past-year stimulant dependence secondary to

nonmedical prescription stimulant use among college stu-

dents aged 18–22 vs. their non-college student counter-

parts. Then, we tested for interactive effects between

educational group status with both gender and race. Inter-

action was assessed on the additive scale by testing the

interaction contrast (IC), which represents the difference in

risk differences [56]. Adjusted ICs were calculated using

the PRED_EFF command in SUDAAN [57].

Results

Estimated past-year prevalence by educational

attainment (Table 1)

The prevalence estimates of past-year NMUPO among 18-

to 22-year-old college students, those with high school

diploma/GED and those with less than high school edu-

cation were 11.3, 13.1 and 13.2 %, respectively. Those

with less than high school [aOR 1.25 (1.12–1.40)] and

those who completed high school/GED [aOR 1.21

(1.11–1.33)] were more likely than college students to be

past-year NMUPO (Table 1). Women were less likely than

men to be past-year NMUPO [aOR 0.74 (0.68–0.81)]; NH

blacks, NH Asians and Hispanics were less likely to use PO

nonmedically in the past-year compared to NH whites; and

respondents who reported past-year serious psychological

distress were more likely than those without distress to

report past-year NMUPO [aOR 2.09 (1.89–2.31)]. There

were no differences in the prevalence of NMUPO or in the

prevalence of OD secondary to NMUPO by county type.

In contrast, the prevalence estimates for past-year non-

medical stimulant use among 18- to 22-year-old college

students, those with a high school diploma/GED, and those

with less than high school education were 4.8, 3.1, and

3.0 %, respectively. Those with less than high school [aOR

0.66 (0.54–0.80)] and those who completed high school/

GED [aOR 0.65 (0.55–0.77)] were less likely to have been

past-year nonmedical stimulant users compared to their

college-attending peers (Table 1). As with prescription

opioids, females, NH blacks, NH Asians, and Hispanics

were less likely to report past-year nonmedical stimulant

use, and respondents who reported past-year serious psy-

chological distress were more likely than those without

distress to report past-year nonmedical stimulant use.

Among young adults aged 18–22 year old with past-

year NMUPO, those with lower educational attainment

[less than high school education: 19.1 %, aOR 1.75

(1.35–2.28), completed high school/GED: 17.4 %, aOR

1.55 (1.22–1.98)] were more likely to have past-year OD

compared to college students (Table 1). NH blacks were

less likely than NH whites to have OD [aOR

0.60(0.41–0.89)], but there were no racial/ethnic differ-

ences in the prevalence of OD between NH whites and

those of other racial/ethnic groups. NMUPO users who

reported past-year psychological distress were more likely

to have OD than those with no psychological distress [aOR

3.05 (2.40–3.88), Table 1].

Among young adults with past-year nonmedical stimu-

lant use, a similar pattern with educational attainment was

seen. Past-year stimulant use disorder was more likely

among those with lower educational attainment [less than

high school education: 17.9 %, aOR 2.39 (1.35–4.12),

completed high school/GED: 14.0 %, aOR 1.75 (1.02-

3.02)] compared to their college-attending peers (Table 1).

However, among past-year nonmedical stimulant users,

NH Asians were more likely than whites to have developed

past-year stimulant use disorder [aOR 3.29 (1.17–9.24)],

and those living in a nonmetro county type were less likely

to develop past-year stimulant use disorder [aOR 0.54

(0.32–0.92)], compared to living in a large metro area.

Risk differences: educational attainment by gender

(Table 2)

For both males and females, having less than a high school

degree was associated with a greater risk of NMUPO use

compared to their college-attending counterparts (RD

3.4 %, p 0.001 for males, RD 1.5 %, p = 0.072 for
females). While a greater risk difference was observed for

males, this association was not significantly different from

that of females (IC 2.0 %, p = 0.069). Further, the rela-

tionship between educational attainment and past-year OD

among PO users differed by gender. While the difference

between male college students and males with a high

school diploma/GED for past-year OD secondary to

NMUPO was negligible (RD 1.7 %, p = 0.445), females

with a high school diploma/GED had a much greater risk

compared to their college-attending counterparts (RD

8.2 %, p 0.001). In our test of additive interaction, the

716 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

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Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724 717

123

risk of OD secondary to NMUPO associated with educa-

tional attainment did significantly differ by gender (IC

-6.5 %, p = 0.050). For past-year nonmedical stimulant

use, females with less than a high school degree were

significantly less likely to report past-year nonmedical

stimulant use than females with college education (RD

-2.2 %, p 0.001). There was less of an educational risk
difference among males (RD -0.8 %, p = 0.080).

Table 2 Risk differences for
nonmedical prescription opioid

and stimulant use and

nonmedical prescription opioid

and stimulant use disorders for

males and females by

educational attainment, and test

for differential effects

(difference in risk differences)

among young adults aged

18–22: National Survey on

Drug Use and Health,

2008-2010 data

Males

RD % (SE) p value

Reference

Females

RD % (SE) p value

IC % (SE) p value

Past-year nonmedical prescription opioid use

Educational attainment

College Reference Reference

High School Diploma/GED 1.6 (0.6 %) 0.011 2.4 (0.9 %) 0.007

-0.8 (1.1 %) 0.463

Less than HS degree 3.4 (0.8 %) .001 1.5 (0.8 %) 0.072
2.0 (1.1 %) 0.069

Past-year nonmedical prescription stimulant use

Educational attainment

College Reference Reference

High School Diploma/GED -1.5 (0.4 %) .001 -1.7 (0.5 %) .001
0.3 (0.6 %) 0.674

Less than HS Degree -0.8 (0.5 %) 0.080 -2.2 (0.5 %) .001
1.4 (0.7 %) 0.050

Past-year prescription opioid use disorder

Educational attainment

College Reference Reference

High School Diploma/GED 1.7 (2.2 %) 0.445 8.2 (2.2 %) .001
-6.5 (3.3 %) 0.050

Less than HS degree 6.6 (2.5 %) 0.009 7.3 (2.4 %) 0.003

-0.6 (3.5 %) 0.856

Past-year prescription stimulant use disorder

Educational attainment

College Reference Reference

High School Diploma/GED 1.6 (2.7 %) 0.549 8.3 (3.5 %) 0.020

-6.7 (3.6 %) 0.071

Less than HS Degree 5.1 (3.8 %) 0.181 12.3 (5.6 %) 0.034

-7.2 (7.2 %) 0.325

718 Soc Psychiatry Psychiatr Epidemiol (2015) 50:713–724

123

RD—adjusted for race/

ethnicity, annual family income,

county type, serious

psychological distress, and

survey year

IC—interaction contrast (in

italics)—represents difference

between risk differences for

females vs. males

Risk differences: educational attainment by race

(Table 3)

The relationship between educational attainment and

NMUPO was modified by race. Among NH whites, those

with lower educational attainment had a significantly

higher risk of NMUPO compared to college students

(completed high school/GED, RD: 2.7 %, p 0.001; less
than a high school (HS) degree, RD: 3.0 %, p 0.001).

However, among Hispanics, NH blacks, NH more than one

race and Asians, there were no significant differences

between either of the lower educational attainment groups

and college students for risk of NMUPO (see Table 3). The

risk differences for Hispanic groups were significantly

different from those observed for their NH white counter-

parts (IC -3.2 %, p = 0.031, IC -3.0 %, p = 0.040, for

completed HS/GED and less than a HS degree, respec-

tively). Further, while the risk of NMUPO associated with

educational attainment was much more pronounced among

Native Americans/Pacific Islanders and Asians, these risk

differences were not significantly different from those

observed for their NH white counterparts. For past-year

OD among NMUPO, the risk associated with educational

attainment did not significantly differ by race.

For past-year nonmedical stimulant use, the risk differ-

ences between NH whites attending college …

Journal of Substance Abuse Treatment 48 (2015) 49–55

Contents lists available at ScienceDirect

Journal of Substance Abuse Treatment
Influences of motivational contexts on prescription drug misuse and

related drug problems

Brian C. Kelly, Ph.D. a,b,⁎, H. Jonathon Rendina, Ph.D. b,d, Mike Vuolo, Ph.D. a,
Brooke E. Wells, Ph.D. b,c,d, Jeffrey T. Parsons, Ph.D. b,c,d

a Department of Sociology, Purdue University, 700 W State Street, West Lafayette, IN 47907, USA
b Center for HIV Educational Studies & Training, 142 West 36th Street, 9th Floor, New York, NY 10018, USA
c Department of Psychology, Hunter College of the City University of New York, 695 Park Avenue, New York, NY 10065, USA
d The Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY 10016, USA

a r t i c l e i n f o a b s t r a c t
⁎ Corresponding author at: Purdue University Departm
St. West Lafayette, IN 47907.

E-mail address: [email protected] (B.C. Kelly).

http://dx.doi.org/10.1016/j.jsat.2014.07.005
0740-5472/© 2014 Elsevier Inc. All rights reserved.
Article history:
Received 28 February 2014
Received in revised form 11 July 2014
Accepted 14 July 2014

Keywords:
Prescription drug misuse
Young adults
Motivational contexts
Dependence
Drug problems
Prescription drug misuse has emerged as a significant problem among young adults. While the effects of
motivational contexts have been demonstrated for illicit drugs, the role of motivational contexts in
prescription drug misuse remains understudied. Using data from 400 young adults recruited via time–space
sampling, we examined the role of motivational contexts in the frequency of misuse of three prescription drug
types as well as drug-related problems and symptoms of dependency. Both negative and positive motivations
to use drugs are associated with increases in prescription drug misuse frequency. Only negative motivations
are associated directly with drug problems and drug dependence, as well as indirectly via prescription pain
killer misuse. Addressing positive and negative motivational contexts of prescription drug misuse may not
only provide a means to reduce misuse and implement harm reduction measures, but may also inform the
content of treatment plans for young adults with prescription drug misuse problems.
ent of Sociology 700 W State
© 2014 Elsevier Inc. All rights reserved.
1. Introduction

Prescription drug misuse has emerged as a significant problem
during the 21st century; this trend has been particularly prevalent
among young adults (Kelly et al., 2013; McCabe, Teter, & Boyd, 2006).
In 2012, over 4.7 million American young adults reported the misuse
of prescription drugs during the past year (Substance Abuse and
Mental Health Services Administration [SAMHSA], 2013). Further­
more, the lifetime prevalence of prescription drug misuse among
young adults is greater than that for most illegal drugs; only
marijuana continues to be more widely used than prescription
drugs among young people (SAMHSA, 2013). Further, while the
overall prescription drug trend has plateaued in the United States,
misuse remains a significant problem among American young adults,
and recently it has become a more significant global drug trend
(United Nations Office on Drugs and Crime, 2011).

Prescription drug misuse has not only emerged as a significant drug
trend, but has created substantial problems for the health care sector
and drug treatment facilities. Studies suggest that a range of negative
health effects are associated with prescription drug misuse, including
cognitive impairment, mental health problems, overdose, and organ
damage (Caplan, Epstein, Quinn, Stevens, & Stern, 2007; Teter, Falone,
Cranford, Boyd, & McCabe, 2010). Prescription drug misuse burdens the
health care system as well. Between 2004 and 2008, the number of
emergency room visits involving the misuse of prescription drugs
increased 81%; for prescription pain killers specifically, the increase was
111%, or more than double the number of visits (SAMHSA, 2011). The
misuse of prescription drugs accounted for a large proportion of all
drug-related emergency room visits (SAMHSA, 2011). Increased rates of
prescription drug misuse have also contributed heavily to the treatment
burden in the United States in recent years. Prescription drug misuse is
among the most common problems for young people enrolled in drug
treatment (Gonzales, Brecht, Mooney, & Rawson, 2011). There are also
major economic impacts; prescription opioid abuse alone costs the
United States tens of billions of dollars (Birnbaum et al., 2011). Thus, the
problems associated with prescription drug misuse are significant,
making research into the motivations associated with misuse impera­
tive to guide prevention and intervention programs.

1.1. The role of motivational contexts in drug use

There are a variety of motivations underlying substance use. Young
people, in particular, have been shown to express a wide range of
motivations for substance use, including relaxation, intoxication, staying
alert while socializing, and alleviating negative affect (Boys, Marsden, &
Strang, 2001), and these wide ranging motivations among young people
extend to prescription drug misuse (Boyd, McCabe, Cranford, & Young,
2006; McCabe, Boyd, & Teter, 2009). Such motivational contexts have

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50 B.C. Kelly et al. / Journal of Substance Abuse Treatment 48 (2015) 49–55
proven to be important influences of patterns of drug use in a variety of
ways (Hartwell, Back, McRae-Clark, Shaftman, & Brady, 2012; Starks,
Golub, Kelly, & Parsons, 2010). For example, the desire to use drugs to
deal with conflicts with others is associated with greater frequency of
drug use (Halkitis, Parsons, & Wilton, 2003). The growth of an
individual’s drug use trajectory over time is associated with the
motivation of using drugs to have pleasant times with others (Palamar,
Mukherjee, & Halkitis, 2008). Additionally, unpleasant emotions have
been identified as a motivational context related to polydrug use among
young adults (Kelly & Parsons, 2008). Scholars have also shown that
motivations are important in the reduction or cessation of substance
use. For example, feeling motivated to use drugs due to social pressures
has been associated with heroin relapse (El Sheikh & Bashir, 2004).
Collectively, numerous studies demonstrate the role of a range of
motivational contexts in patterns of substance use, particularly that
certain motivations are tied to increasing frequency of substance use.
Yet, the role of motivational contexts in abuse and dependence related
to prescription drug misuse remains understudied.

Motivational contexts also have implications for both identifying
the potential for problem use as well as the consideration of drug
treatment options (Turner, Annis, & Sklar, 1997). If particular
motivations can be tied to problem patterns of drug use, researchers
and practitioners can identify high risk situations for drug users, and
intervention or harm reduction efforts can focus on these motiva­
tional contexts as part of the approach. Similarly, if these motivations
can be linked with symptoms of drug dependence, they will facilitate
tailored interventions as well as the identification of treatment
modalities that best serve clients with certain motivational profiles.
Furthermore, motivational contexts are particularly important when
it comes to relapse of substance abuse (Marlatt & Friedman, 1981),
and thus will enable the identification of key relapse prevention
measures among individuals with past substance abuse problems.

1.2. Current study

Given the significance of prescription drug misuse and the
treatment and care burden it has generated, we aim to understand
how motivational contexts influence patterns of prescription drug
misuse and related problems. Specifically, we examine the influence
of motivational contexts on the frequency of prescription drug misuse,
drug related problems, and symptoms of dependence among young
adults active in nightlife scenes. We will consider that greater scores
on motivational contexts will directly influence greater frequency of
misuse of all prescription drug types as well as prescription drug
related problems and symptoms of dependency. We also posit that
greater scores on these motivational contexts indirectly influence
prescription drug related problems and symptoms of dependency via
the frequency of prescription drug misuse. The identification of these
pathways allows us to determine the motivational contexts that most
influence prescription drug misuse and its associated problems. Such
an assessment may facilitate prevention, treatment, and harm
reduction efforts.

2. Methods

2.1. Sampling and procedures

To generate the sample for this study, we primarily utilized time–
space sampling in a wide range of venues that house nightlife scenes
in New York City (NYC). Time–space sampling was originally
developed to capture hard-to-reach populations (MacKellar, Valleroy,
Karon, Lemp, & Janssen, 1996; Muhib et al., 2001; Stueve, O’Donnell,
Duran, San Doval, & Blome, 2001), but is also constructive for
generating samples of venue-based populations (Parsons, Grov, &
Kelly, 2008). As young adults active in nightlife scenes can be
considered a venue-based population, we used venues as our basic
unit of sampling in order to systematically generate a sample of
socially active young adults. We captured a range of variability among
these young adults through randomizing (1) the venues attended and
(2) the days and times we sampled individuals from them.

We randomized “time” and “space” using a sampling frame of
venues and times of operation. To construct the sampling frame,
ethnographic fieldwork conducted over the previous 12 months
enabled the assessment of “socially viable” venues for each day of
the week. A venue was deemed socially viable if a threshold of young
adult patron traffic existed at the venue on that given day of the week.
We generated lists of socially viable venues for each day of the week
across several key youth cultures—e.g. electronic dance music (EDM),
gay clubs, lesbian parties, and indie rock scenes. The venues included
bars, clubs, lounges, warehouses, loft spaces, and performance venues.
Recruitment occurred year round with teams of recruiters. For all days
of the week, all viable venues were listed and assigned a number.
Using a random digit generator, a random number was drawn
corresponding to a particular venue on a particular day. Ultimately,
this process yielded our schedule of venues for each month.

Once at the venue, project staff attempted to screen as many
individuals as possible, aiming to survey all young adults at the venue.
Staff approached a patron, identified themselves, described the
screening survey, and requested verbal consent for participation in
the anonymous brief survey conducted on an iPod Touch® that was
designed using iFormBuilder™ software. For those who provided
consent to participate, the beginning of the brief surveys were
administered by trained staff (age and NYC residency) and respon­
dents self-reported all other information (race, sexual orientation,
gender, and substance use) directly onto the iPod Touch®. Individuals
received no compensation for completing the screening survey. Field
staff members were trained not to administer surveys to individuals
who were visibly impaired by intoxication to ensure the capacity to
consent. Response rates to the screening survey (75.0%) were high
given the difficult conditions of surveying young adults in nightclub
settings and the lack of compensation for participating in the
screening survey.

Upon completion of the survey, the software determined whether
the individual was eligible for the study (9.4% of all those screened in
venues were eligible). If participants were eligible, they were given a
brief description of the study and asked to provide contact
information if they were interested in participating. Later in the
timeline of study enrollment, staff also provided eligible participants
the opportunity to verify their age and identity at the point of
recruitment so that the study survey could be completed online. A
majority of those deemed eligible (77.4%) provided contact informa­
tion for further study participation.

Near the end of the project, venue recruitment was supplemented
by scene-targeted recruitment via online groups associated with
nightlife scenes of interest. The research team first developed a list of
groups that were relevant to each of the scenes of interest for the
project. Group members who were between the ages of 18 and 29 and
resided in the NYC metropolitan area saw an advertisement for the
study; if they clicked on the advertisement, they were directed to a
Qualtrics® survey that screened for study eligibility and, if eligible,
collected their contact information. Less than 5% of the sample was
recruited via this supplemental method.

Regardless of recruitment method (venue-based or online),
research staff contacted participants by phone and e-mail to provide
more information about the study, confirm eligibility, and schedule
the initial assessment (or send them a link to the online survey if they
showed proof of age in the field). Eligibility criteria were as follows:
(1) aged 18–29; (2) report the misuse of prescription drugs at least
three times in the past 6 months; and (3) report the misuse of
prescription drugs at least once in the past 3 months. The subjects
could report any of three classes—many reported the misuse of
multiple classes. A threshold of 3 recent occasions of misuse excluded

51 B.C. Kelly et al. / Journal of Substance Abuse Treatment 48 (2015) 49–55
individuals who simply experimented with a single occasion of
misuse while also avoided enrolling only heavy users. In the initial
assessment, participants completed the informed consent process,
then completed the survey (via ACASI for in-house assessments and
via Qualtrics® for online assessments). Once completed, participants
were compensated $50 in cash, check, or Amazon.com gift card
(depending on their preference). All procedures were reviewed and
approved by the universities’ Institutional Review Boards.

2.2. Measures

2.2.1. Demographics
Participants self-reported their age, gender, sexual identity (gay,

straight, bisexual, queer, or questioning), race/ethnicity (White,
Latino, Black, Asian/Pacific Islander, or mixed), highest education
completed (some high school, high school diploma, some college,
currently enrolled in college, 4-year college degree, or graduate
school), parental socio-economic status (poor, working class, middle
class, upper middle class, and wealthy), and employment status (full­
time work, part-time work, part-time work/student, unemployed
student, or unemployed-other).

2.2.2. Motivational contexts
We use the Inventory of Drug Taking Situations (IDTS) to assess

motivational contexts in which individuals misuse prescription drugs
(Annis, Turner, & Sklar, 1996). The scale was modified to specifically
focus on situations of prescription drug misuse. Subjects rated on a 5­
point Likert-type scale from “never” to “always” how often they had
misused prescription drugs in response to specific situations. For
example, “In the past 3 months, I have used prescription drugs when I
felt overwhelmed and wanted to escape.” Or “…when I wanted to
celebrate.” The IDTS has eight subscales (unpleasant emotions,
physical discomfort, conflict with others, social pressures to use,
pleasant times with others, pleasant emotions, testing personal
control, and urges/temptations). These subscales load onto three
more general higher-order factors of negative situations (unpleasant
emotions, physical discomfort, and conflict with others), positive
situations (pleasant emotions, and pleasant times with others), and
tempting situations (urges/temptations, social pressures to use, and
tempting personal control). A confirmatory factor analysis revealed
that this structure adequately fit the adapted scale (RMSEA = 0.06,
CFI = 0.86, SRMR = 0.08). Scores for the three higher-order sub­
scales were formed by averaging across the items from their
corresponding lower-order factors, and each showed evidence of
strong internal consistency (negative situations α = 0.96, positive
situations α = 0.90, tempting situations α = 0.90).

2.2.3. Prescription drug misuse
We used the following operational definition of prescription drug

misuse, which was provided to subjects: “…using prescription drugs
obtained from a non-medical source, using more than the prescribed
dose, or using prescription drugs for a non-medical or recreational
purpose. Non-medical use may occur whether you do or do not have a
prescription for that drug.” Respondents reported their frequency of
misuse (measured in days) during the previous 3 months of each of
three distinct prescription drug types (pain killers, sedatives, and
stimulants). Subjects were provided examples of each prescription
drug class to facilitate clarity on the types of substances.

2.2.4. Drug problems
We also assessed both symptoms of dependence and problems

associated with drug use. The Composite International Diagnostic
Interview (CIDI) Substance Abuse Module was tailored to assess
symptoms of drug dependence related to prescription drug use
(Cottler & Keating, 1990). This 8-item measure is an internationally
recognized measure used to assess symptoms of prescription drug
dependence. The Short Inventory of Problems with Alcohol and Drugs
(SIP-AD) was also tailored to assess problems associated with the
misuse of prescription drugs. The SIP-AD is a 15-item inventory of
problems associated with substance use (Blanchard, Morgenstern,
Morgan, Labouvie, & Bux, 2003).

2.3. Statistical analyses

We began by examining basic frequencies of sample characteris­
tics, followed by demographic comparisons of the three higher order
IDTS subscales (negative situations, positive situations, and tempting
situations) using analysis of variance with least significant difference
(LSD) post-hoc tests for those results with significant main effects. We
also provide similar analyses for frequency of prescription drug
misuse, drug problems, and dependence as outcomes; we performed
Kruskal–Wallis and Mann–Whitney tests for the prescription drug
frequencies and ANOVA with LSD post-hoc tests for the CIDI and SIP­
AD. Following this, we utilized Mplus version 7.11 to conduct path
analyses examining the structural associations among the IDTS
subscales, frequency of misuse of each prescription drug type (pain
killers, sedatives, and stimulants), and severity of substance use
problems (the CIDI and SIP-AD). In doing so, we specified the three
frequency of prescription drug misuse variables as negative binomial-
distributed count variables using the Mplus default of robust
maximum likelihood (MLR) estimation with Monte Carlo integration
based on 500 dimensions of integration (Muthén & Muthén, 1998­
2012). Upon fitting the initially hypothesized model, we tested for
mediation by allowing direct paths from the IDTS subscales to the CIDI
and SIP-AD scores to be freely estimated. We retained all significant
effects between IDTS and outcome variables (while dropping non-
significant paths above marginal significance at p =0.06) and ran a
final model testing for the significance of the indirect paths using the
Model Constraint feature in Mplus. As a result of using MLR
estimation, traditional fit indices (e.g., RMSEA, CFI/TLI, SRMR, chi-
square statistic) and standardized model results were unavailable for
the path models and are not reported within the results. Incident rate
ratios (IRR) are reported for the negative binomial results (i.e., substance
use frequency variables) in the text.

3. Results

The recruitment yielded 400 participants for the analyses
contained in this paper, with a mean age of 24.5 years. As seen in
Table 1, approximately one-third of members of the sample were
racial/ethnic minorities. The sample was split relatively evenly with
regard to gender and sexual orientation. Slightly more than half of the
sample was single. More than half of the subjects had a 4-year college
degree and nearly one-quarter grew up in a poor or working class
family. We found significant differences between racial/ethnic groups
in the positive and tempting situations subscales; post-hoc analyses
revealed that Latino participants were higher than White (p b .01)
and other (p b .001) participants on the positive situations subscale
and Black and Latino participants were significantly higher than
White (p b .01 in both cases) and other (p b .05 in both cases)
participants on the tempting situations subscale. We found significant
gender/sexual orientation differences in the positive and tempting
situations subscales, with heterosexual men scoring higher on both
subscales than gay/bisexual/queer (GBQ) men (p b .01 for both
subscales) and heterosexual women (p b .001 for both subscales)
and LBQ women scoring higher than heterosexual women (p b .05 for
both subscales). Finally, we found educational differences on the
negative and tempting situations subscales; those with a 4-year
college degree scored lower on the negative situations subscale than
those with only some college or a 2-year degree (p b .001), and those
with a 4-year college degree scored lower on the tempting situations
subscale than those with a high school diploma or less (p b .01), some

http:Amazon.com

52 B.C. Kelly et al. / Journal of Substance Abuse Treatment 48 (2015) 49–55

Table 1
Demographic characteristics of the sample and demographic differences in IDTS subscales.

N = 400 Negative situations Positive situations Tempting situations

Demographic characteristics n % M SD M SD M SD

Race/Ethnicity F(3, 396) = 1.82 F(3, 396) = 4.15⁎⁎ F(3, 396) = 4.62⁎⁎

White 269 67.3 1.88 0.76 2.41a 0.84 1.67a 0.59
Latino 32 8.0 2.18 0.96 2.90b 1.03 1.98b 0.71
Black 20 5.0 2.10 0.75 2.68a,b 0.88 2.07b 0.84
Other 79 19.8 1.87 0.81 2.31a 0.94 1.67a 0.68

Gender/sexual orientation F(3, 396) = 2.19 F(3, 396) = 6.24⁎⁎⁎ F(3, 396) = 5.94⁎⁎

LGBQ male 110 27.5 1.85 0.79 2.34a,b 0.90 1.64a,b 0.60
Straight male 107 26.8 1.89 0.84 2.71c 0.90 1.90c 0.72
LGBQ female 82 20.5 2.11 0.82 2.49a,c 0.88 1.76a,c 0.67
Straight female 101 25.3 1.86 0.67 2.22b 0.79 1.55b 0.53

Relationship status F(1, 398) = 1.08 F(1, 398) = 0.77 F(1, 398) = 0.30
Single 222 55.5 1.95 0.78 2.41 0.88 1.73 0.64
Partnered 178 44.5 1.87 0.79 2.49 0.90 1.69 0.64

Education F(3, 396) = 4.27⁎⁎ F(3, 396) = 1.32 F(3, 396) = 4.55⁎⁎

High school diploma/GED or less 26 6.5 2.08a,b 0.89 2.69 1.05 1.97a 0.74
Some college or associate’s 64 16.0 2.18a 0.98 2.52 0.95 1.84a 0.69
Currently enrolled in college 83 20.8 1.94a,b 0.83 2.49 0.98 1.80a 0.73
4-Year degree or higher 227 56.8 1.81b 0.68 2.38 0.81 1.62b 0.57

Parental socioeconomic status F(2, 394) = 0.26 F(2, 394) = 0.18 F(2, 394) = 2.53
Poor or working class 92 23.0 1.96 0.76 2.47 0.90 1.84 0.76
Middle class 154 38.5 1.92 0.78 2.46 0.90 1.71 0.61
Upper middle class or rich 151 37.8 1.89 0.82 2.41 0.87 1.65 0.59
Not reported 3 0.8 – – – – – –

Note: Means with differing superscripts within columns differed significantly (p b .05) in LSD-adjusted post hoc analyses (those with the same superscript do not differ at p b .05).
⁎ p b .05

⁎⁎ p b .01.

⁎⁎⁎ p b .001.

college or a 2-year degree (p b .05), and those currently enrolled in
college (p b .05).

As shown in Table 2, the median number of occasions of
prescription drug misuse for pain killers, sedatives, and stimulants
during the past 90 days was 3.0, 4.0, and 3.0 days, respectively. Mean
SIP-AD score was 5.03 and mean CIDI score was 2.25. Significant
racial/ethnic differences in pain killer use were found, with Latinos
reporting higher frequency than White and other/multiracial partic-
Table 2
Demographic differences in substance use outcomes.

Pain killer frequency Sedative frequenc

Median IQR Median IQ

Total Sample (N = 400) 3.0 0, 10 4.0 0,
Race/ethnicity H(3) = 15.01** H(3) = 3.83
White 3.0a 0, 9 4.0 1,
Latino 10.0b 4, 29 3.0 0,
Black 5.5a,b 2, 14 6.0 3,
Other 2.0a 0, 10 3.0 0,

Gender/sexual orientation H(3) = 6.59 H(3) = 6.96
LGBQ male 3.0 0, 10 4.0 1,
Straight male 3.0 0, 15 2.0 0,
LGBQ female 4.0 1, 10 5.0 2,
Straight female 2.0 0, 6 3.0 0,

Relationship status U(398) = 19,997.5 U(398) = 21,199
Single 3.0 0, 10 3.0 0,
Partnered 3.0 0, 10 4.0 1,

Education H(3) = 22.78*** H(3) = 8.71*
High school diploma/GED or less 15.0a 3, 53 15.0a 1,
Some college or associate’s 5.0a,b 2, 15 5.0a 2,
Currently enrolled in college 3.0b,c 0, 10 2.0a 0,
4-Year degree or higher 2.0c 0, 7 4.0a 0,

Parental socioeconomic status H(2) = 1.80 H(2) = 0.96
Poor or working class 3.0 0, 10 4.0 1,
Middle class 2.0 0, 9 4.0 0,
Upper middle class or rich 4.0 0, 10 3.0 0,

Note: Means and medians with differing superscripts within columns differed significantly
analyses (those with the same superscript do not differ at p b .05). Interquartile ranges we
ipants (p b .01). No other differences were found for race/ethnicity.
Educational differences existed for pain killer and sedative use
frequency, CIDI score, and SIP-AD score. Those with high school
education or less reported higher pain killer use than those currently
in college or those with a 4-year degree, and those with some college
or an associate’s degree reported higher use than those with a 4-year
degree (p b .001). Although there was a significant main effect of
education on sedative use, no significant differences emerged in the
y Stimulant frequency SIP-AD CIDI

R Median IQR M SD M SD

12 3.0 0, 15 5.03 6.65 2.25 2.24
H(3) = 1.26 F(3, 396) = 1.87 F(3, 396) = 1.84

10 3.0 0, 15 4.71 6.47 2.24 2.25
23 2.5 0, 9 7.41 8.67 2.94 2.14
24 6.0 0, 23 6.35 6.21 2.60 2.23
10 5.0 0, 20 4.78 6.34 1.90 2.18

H(3) = 3.05 F(3, 3.96) = 1.15 F(3, 3.96) = 0.55
11 3.0 0, 12 5.35 7.19 2.25 2.42
20 4.0 0, 20 5.53 6.60 2.46 2.21
16 3.0 0, 11 5.21 7.44 2.18 2.19
10 5.0 0, 20 3.98 5.26 2.07 2.09
.5 U(398) = 20,337.0 F(1, 398) = 0.09 F(1, 398) = 0.14
10 3.0 0, 15 5.11 6.63 2.29 2.18
20 5.0 0, 20 4.92 6.70 2.21 2.31

H(3) = 3.80 F(3, 396) = 7.01⁎⁎⁎ F(3, 396) = 6.23⁎⁎⁎

22 1.0 0, 18 8.50a 9.88 2.81 a 2.25
20 5.0 0, 15 7.19 a 7.77 3.08 a 2.55
6 5.0 0, 15 5.26 b 6.76 2.47 a 2.32
10 3.0 0, 15 3.93 b 5.48 1.86 b 2.03

H(2) = 0.85 F(2, 394) = 0.29 F(2, 394) = 0.46
15 3.0 0, 15 5.51 6.10 2.42 2.36
10 3.0 0, 15 4.88 6.58 2.14 2.19
15 4.0 0, 15 4.94 7.11 2.28 2.22

(p b 0.05) in LSD-adjusted (ANOVA) or Bonferroni-adjusted (Kruskal–Wallis) post hoc
re rounded to the nearest whole number in cases with decimal points.

53 B.C. Kelly et al. / Journal of Substance Abuse Treatment 48 (2015) 49–55

Fig. 1. Relationship of motivational factors to prescription drug problems. Unstandardized results of the hypothesized model (non-significant paths displayed as dotted
lines). Note: Model controls for demographic factors, though not depicted for model clarity. p-Values indicated via asterisks: †p ≤ .06; * b .05; ** b .01; *** b .001.
adjusted post-hoc comparisons. Those with less education typically
reported greater problems with substance use on both the SIP-AD and
CIDI than those with more education (p b .001). We found no
significant differences on any variables for gender/sexual orientation,
relationship status, or parental socioeconomic status.

We next sought to examine the hypothesized model whereby
negative, positive, and tempting situations lead to increases in the
frequency of recent prescription pain killer, sedative, and stimulant
misuse, which in turn leads to increased levels of substance use
problems as measured with the CIDI and the SIP-AD. The results of this
hypothesized model are displayed in Fig. 1, with significant paths
shown as solid lines and non-significant paths shown as dotted lines.
The negative situations subscale was positively associated with the
frequency of all three forms of prescription drug misuse. A one-unit
increase in this subscale was associated with a 59% increase in the rate
of pain killer misuse (IRR = 1.59, 95% CI [1.24, 1.95]) and a 60%
increase in the rate of stimulant misuse (IRR = 1.60, 95% CI [1.23,
Fig. 2. Final path model of the influence of motivational factors on prescription drug problem
estimated (non-significant but freely estimated paths are displayed as dotted lines and pat
depicted for model clarity. †p ≤ .06; *p ≤ .05; **p ≤ .01; ***p ≤ .001.

SCS 502 Final Project Milestone Two: Analyzing Guidelines and Rubric

Overview: For this milestone, you will begin your analysis of the studies that you have selected by continuing the work you began in Milestone One. Your analysis
will focus on the research design of the studies and how the conclusions are or are not supported by the results of the studies. Use the same three articles you
selected for Milestone One and expand upon the methodology.

Be sure that your submission addresses the following critical elements:

 Compare the research methodologies used in your chosen studies and explain why these methodologies were used over other methodologies. In other
words, in your comparison you could include why methodologies are informed by the hypothesis created by the author of the study. Be sure to support
your response with examples and support from the chosen studies.

 Explain why the research designs of your chosen studies ensure that the study is or is not valid. Be sure to support your response with examples and
support from the chosen studies.

 Explain why the research designs of your chosen studies ensure that the study is or is not reliable. Be sure to support your response with examples and
support from the chosen studies.

 Explain why the research designs of your chosen studies ensure that the study is or is not credible. Be sure to support your response with examples and
support from the chosen studies.

 Explain how the results of your chosen studies appropriately support the conclusions that were reached. If the results do not support the conclusions
reached, briefly describe the more appropriate conclusions given the results presented. In other words, you could consider the results section and how
the authors used that information to reach their conclusions, even if their claims are inaccurate.

For more detailed instructions, see the following steps:

1. First copy and paste the title page and reference list from Milestone One into a new document. Hopefully the running head copies over as well, but if it
does not, add that back in. Be sure to address any feedback from your instructor on these sections so you have the best version moving forward.

2. Next, begin the new narrative by comparing and contrasting the research methodologies from the three articles. In Milestone One, you simply described
the articles. Now you will begin to integrate them into a larger discussion, using material you have learned in this class. How are the methodologies
similar? How are they different? Why might the authors have made these methodological choices, given their stated hypotheses? Be sure to incorporate
specific points from all three articles to support your arguments.

3. Then, discuss each article in terms of reliability and validity. Do you think their methodology leads to credible results? Why or why not? It is certainly
possible that you will reach different conclusions for some of the articles. Talk about how the individual methodology led you to those conclusions. Again,
be sure to incorporate specific points from all three articles to support your arguments. Also make sure that for each article, you talk specifically about
reliability, validity, and overall credibility (That is, should we trust these results? Beyond reliability, are the results logical? Current? Anything else that
might concern or impress you?).

4. Close by discussing the conclusions reached by each of the articles. Again, you described this in Milestone One but now you are using the information
developed in Milestone Two to critique the conclusions. Are those conclusions supported? If so, what specifically makes you believe the conclusions are

valid? Are those conclusions not supported, given the information provided above? If so, cite specific reasons as to why we should question the given
conclusions.

Helpful hints for Milestone Two:

● Use citations appropriately so the reader can distinguish between each of the articles and your original thoughts.
● Remember this is a methodological discussion in a research methods course, so while it is OK to use some other information if you feel that is important

for your argument, your primary focus should be on the methods themselves.
● If you are making any claims that are not directly from the articles or are not common knowledge, you should use scholarly research to support these

claims. For example, you should not make claims such as “Addiction to alcohol comes from a genetic predisposition” or “Children respond better to an
authoritarian parenting style” unless you can support them with a peer-reviewed research article. Feel free to reference your textbook if you need to
define research terms or other basic methodology. Note that using this additional information or making additional claims is not a requirement, but an
option for those who want to further support their arguments. The point is that if you are going to include this information, you are required to cite it.

PLEASE NOTE: You are not required to use any new references in this section, but if you do, make sure to cite them in APA style in the reference list.

Guidelines for Submission: Your paper must be submitted as Microsoft Word document with double spacing, 12-point Times New Roman font, one-inch
margins, a running head, a title page, and at least three sources cited in APA format. This submission should be approximately 3–4 pages (not counting title or
reference page). It is anticipated that you will use the same title page and reference page as submitted in Milestone One, with any feedback from your instructor
incorporated.

Critical Elements Exemplary (100%) Proficient (90%) Needs Improvement (70%) Not Evident (0%) Value

Research Analysis:
Research

Methodologies

Meets “Proficient” criteria and
draws a connection between the
hypotheses of the chosen
studies and the design selection

Compares the research
methodologies used in the
chosen studies and explains why
those methodologies were used,
supporting response with
examples and other information

Compares the research
methodologies used in the
chosen studies and explains why
those methodologies were used
but explanation is cursory,
contains inaccuracies, or does
not support response with
examples and support from the
research

Does not compare the research
methodologies used in the
chosen studies and explain why
those methodologies were used

30

Research Analysis:
Valid

Meets “Proficient” criteria and
evidence referenced from the
chosen studies makes cogent
connections between how the
methodology of a study ensures
that it is or is not valid

Explains why the methodologies
of the chosen studies ensure
that the study is or is not valid,
supporting response with
examples and other information

Explains why the methodologies
of the chosen studies ensure
that the study is or is not valid
but explanation is cursory,
contains inaccuracies, or does
not support response with
examples and support from the
research

Does not explain why the
methodologies of the chosen
studies ensure that the study is
or is not valid

10

Research Analysis:
Reliable

Meets “Proficient” criteria and
evidence referenced from the
chosen studies makes cogent
connections between how the
methodology of a study ensures
that it is or is not reliable

Explains why the methodologies
of the chosen studies ensure
that the study is or is not
reliable, supporting response
with examples and other
information

Explains why the methodologies
of the chosen studies ensure
that the study is or is not reliable
but explanation is cursory,
contains inaccuracies, or does
not support response with
examples and support from the
research

Does not explain why the
methodologies of the chosen
studies ensure that the study is
or is not reliable

10

Research Analysis:
Credible

Meets “Proficient” criteria and
evidence referenced from the
chosen studies makes cogent
connections between how the
methodology of a study ensures
that it is or is not credible

Explains why the methodologies
of the chosen studies ensure
that the study is or is not
credible, supporting response
with examples and other
information

Explains why the methodologies
of the chosen studies ensure
that the study is or is not
credible but explanation is
cursory, contains inaccuracies,
or does not support response
with examples and support from
the research

Does not explain why the
methodologies of the chosen
studies ensure that the study is
or is not credible

10

Research Analysis:
Support

Meets “Proficient” criteria and
explanation demonstrates keen
insight into the thought process
used by the authors of the
studies

Explains how the results of the
chosen studies appropriately
support the conclusions that
were reached or if the results do
not support the conclusions
reached, explains more
appropriate conclusions given
the results presented

Explains how the results of the
chosen studies appropriately
support the conclusions that
were reached or if the results do
not support the conclusions
reached, explains more
appropriate conclusions given
the results presented but
explanation is cursory or
contains inaccuracies

Does not explain how the results
of the chosen studies
appropriately support the
conclusions that were reached

30

Articulation of
Response/Sources

Submission is free of errors
related to citations, grammar,
spelling, syntax, and
organization, is presented in a
professional and easy-to-read
format, and includes at least
three sources cited

Submission has no major errors
related to citations, grammar,
spelling, syntax, or organization
and includes at least three
sources cited

Submission has major errors
related to citations, grammar,
spelling, syntax, or organization
that negatively impact
readability and articulation of
main ideas or has fewer than
three sources cited

Submission has critical errors
related to citations, grammar,
spelling, syntax, or organization
that prevent understanding of
ideas and no sources are cited

10

Total 100%

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