Research Paper Undergraduate 1,037 words

Tenth Grade Drug and Alcohol Attitudes: Factor Analysis

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Abstract

This paper presents an exploratory factor analysis of selected variables from the 2008 Tenth Grade National Survey conducted by SAMHSA and ICPSR, examining the underlying structure of attitudes toward substance use among U.S. tenth graders. Using Stata software, the analysis extracted five factors—Marijuana, Alcohol, Weekend Alcohol, Graduation, and Periodicals—accounting for 88.467% of total variance. The results indicate that respondents perceived marijuana as higher risk and warranting greater disapproval than tobacco, while alcohol consumption was seen as risky primarily when excessive or concentrated on weekends. Notably, tenth graders expressed optimism about graduation and college attendance, and reported high confidence in their access to educational information. The analysis reveals limited data on mental health issues and suggests future research should incorporate DSM-related items for more comprehensive understanding of adolescent behavioral health.

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What makes this paper effective

  • Clear methodological transparency: The paper explicitly documents sampling strategy, participant selection, and statistical procedures (Varimax rotation, Kaiser normalization), allowing readers to evaluate validity and reproducibility.
  • Structured presentation of factor results: Each factor is presented with consistent metrics (eigenvalue, variance percentage, item count), making comparative analysis straightforward and conclusions defensible.
  • Grounded interpretation: The discussion connects statistical findings to real-world implications—for example, explaining why Weekend Alcohol emerged as a distinct factor and how respondent language influenced factor loading—rather than reporting numbers in isolation.
  • Honest limitations: The conclusion explicitly acknowledges gaps (minimal mental health data, tobacco pattern differences) and proposes targeted improvements for future research, demonstrating scholarly rigor.

Key academic technique demonstrated

The paper exemplifies exploratory factor analysis (EFA) as a data-reduction and pattern-discovery technique. Rather than testing predetermined hypotheses, EFA uses eigenvalues and scree plots to uncover hidden structure in large datasets, then applies orthogonal rotation (Varimax) to improve interpretability. This technique is particularly valuable when working with national survey data where relationships among variables may not be theoretically obvious beforehand.

Structure breakdown

The paper follows a standard empirical research format: introduction establishes the data source and SAMHSA's role; methods section details the NSDUH design, sampling strategy, and participants; data collection describes survey content and scope; results present each of five factors with corresponding statistics and interpretation; discussion contextualizes findings relative to adolescent behavior; and conclusions acknowledge limitations while proposing next steps. This organization moves logically from research infrastructure through methodology, findings, and implications.

Introduction and Background

Access to national-level behavioral health data is important to professionals in the fields of health and human services. Under the United States Department of Health and Human Services (HHS), the collection, analysis, and dissemination of behavioral health data is the primary responsibility of the Center for Behavioral Health Statistics and Quality (CBHSQ), Substance Abuse and Mental Health Services Administration (SAMHSA). The Substance Abuse and Mental Health Data Archive (SAMHDA) initiative is funded through a contract with SAMHSA. Moreover, the University of Michigan, Inter-University Consortium for Political and Social Research (ICPSR), is under contract to CBHSQ to disseminate data and maintain the SAMHDA website and the bibliography of publications. The purpose of this paper is to provide an exploratory factor analysis of selected variables from the 2008 tenth grade national survey conducted by SAMHDA and ICPSR.

The National Survey on Drug Use and Health (NSDUH) series is designed to measure the prevalence and correlates of drug use in the United States. Surveys are conducted to obtain relevant data estimates on a quarterly and annual basis. Data are collected on use of controlled substances, such as drugs and alcohol, and illicit drugs by members of U.S. households who are 12 years of age or older. The sampling plan included stratification and weighting to assure representative samples across the states. Variance estimates were computed by using a clustered data analysis software package.

Methods and Participants

The respondent universe for the 2008 national survey was the civilian, noninstitutionalized population aged 12 years or older residing in the United States and the District of Columbia. The total targeted sample of 67,000 was allocated across three age groups: 12 to 17 years, 18 to 25 years, and 26 years and older. For the 2008 tenth grade survey, the sampling frame consisted of those individuals in tenth grade from the 12 to 17 year old group.

The survey includes questions regarding respondents' age at first use, past month, annual, and lifetime use of eight drug classes. The survey also collects data about treatment history and perceived need for treatment for substance abuse. Questions regarding treatment for mental health disorders and questions from the Diagnostic and Statistical Manual (DSM) of Mental Disorders, which enable the application of diagnostic criteria, are included in the survey. An exploratory factor analysis of select variables was conducted using Stata software.

An exploratory factor analysis (EFA) was used to uncover the underlying structure of the large set of variables in the 2008 national survey. The objective was to identify relationships between measured variables for which no a priori hypotheses were formulated about the factors or the patterns of measured variables. Variables were selected that were represented by multiple measured variables in the analysis, with the common factors, unique factors, and errors of measurements expressing the measured variables.

Data Collection and Variables

An underlying assumption of exploratory factor analysis is that any measured variable may be associated with any factor. A rule of thumb was used to identify the number of variables as loading on factors if the absolute values of their factor loadings were .40 or greater. For this analysis, a five-factor structure was selected. The eigenvalues for the correlation matrix and the plot values from the largest to smallest were computed. The scree plot was used to determine the last substantial drop in the magnitude of eigenvalues and to determine the number of factors to extract. The number of plotted points before the last drop was used as the number of factors to include in the model.

Factor Analysis Results

Factor interpretation was improved through rotation, as it maximized the loading of each variable on one of the extracted factors while minimizing the loading on all other factors. The absolute values of the variables are changed through rotation, but the differential values remain constant. The factors in this study were rotated orthogonally using the Varimax procedure with Kaiser normalization.

Total variance for the analysis was 88.467%. A single rotated factor loading with variable labels and eigenvalues, and the percentage of variance explained by the factors separately and cumulatively, is provided in Table 1 [Insert Table 1 about here].

Factor 1 shows an eigenvalue of 3.585 and a variance percentage of 22.389%, with five items loading on the factor.

Factor 2 shows an eigenvalue of 3.267 and a variance percentage of 20.408%, with seven items loading on the factor.

Factor 3 shows an eigenvalue of 2.520 and a variance percentage of 15.743%, with one item loading on the factor.

Discussion of Findings

Factor 4 shows an eigenvalue of 2.484 and a variance percentage of 15.514%, with four items loading on the factor.

Factor 5 shows an eigenvalue of 3.585 and a variance percentage of 22.389%, with four items loading on the factor.

The use of marijuana and its attendant risks showed the strongest factor loading. Respondents' answers were associated with the language in the question items. For example, questions that used the words risk or disapprove showed high factor loading. The Alcohol and Weekend Alcohol variance showed the most unique pattern, with greater variance associated with alcohol use and less variance associated with disapproval of alcohol use.

The factor loading for Graduation suggests that the tenth graders are optimistic about graduation and attending college. The variance for Periodicals is high, which indicates that the respondents consider their access to and use of periodicals such as newspapers and magazines to be high. Corresponding questions for television and radio show low variance.

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Key Concepts in This Paper
Exploratory Factor Analysis Tenth Grade Survey Marijuana Perception Alcohol Attitudes SAMHSA Data Varimax Rotation Eigenvalues Youth Risk Perception Factor Loading Behavioral Health
Cite This Paper
PaperDue. (2026). Tenth Grade Drug and Alcohol Attitudes: Factor Analysis. PaperDue. https://paperdue.com/study-guide/tenth-grade-substance-attitudes-factor-analysis-196280

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