PSS Lab – Practice T-tests and Chi Square

APPENDIX E SPSS – Sample T-Tests and Chi Square

(CLO 1-4)

The SPSS paper is a 3-4 page paper in APA format (excluding title page, abstract, references and tables/appendices) that reviews t-tests and Chi Square and requires the student to utilize research data to formulate their own t-test/Chi square tables utilizing the SPSS program. This paper should be submitted in Word format and should follow proper APA (7th ed.) guidelines.

The assignment should include the following subheadings: 1. Understanding of Statistical Tests:

• Paired Sample T-Test: • Define and explain the concept of a paired sample t-test, including its

purpose and when it is appropriate to use. • Describe the assumptions underlying the paired sample t-test, such as

normality and independence of observations. • Discuss the formula for calculating the t-value and how it relates to the

differences between paired observations. • Chi-Square Test:

• Define and explain the concept of a chi-square test, including its purpose and when it is appropriate to use.

• Describe the types of data suitable for chi-square tests, such as categorical or nominal data.

• Explain the calculation of the chi-square statistic and its interpretation in assessing the association between variables.

2. Data Source Explanation: • In the paper, provide a detailed explanation of where the input data was obtained

from. Include information about the dataset's origin, sample size, data collection methods, and any relevant ethical considerations.

• Justify why the chosen dataset is suitable for addressing the research question and conducting the paired sample t-test and chi-square test.

3. Analysis and Interpretation: • Use SPSS to create a file to enter raw data from an article similar to the project

you wish to emulate. Perform a paired sample t-test to examine differences between paired observations and a chi-square test to explore associations between categorical variables. This should be created using SPSS and reflect a datafile demonstrating your work to create the table and enter data.

• Interpret and explain the output generated by SPSS for both analyses, focusing on statistical significance and implications for the research question.

4. Submission: Submit the written paper, SPSS syntax file (if used), and any output files.

By incorporating an explanation of data sources into the assignment, students will not only demonstrate their proficiency in statistical analysis but also their ability to critically evaluate and justify the use of data in healthcare/nursing research.

RUBRIC

Exemplary 4 Proficient 3 Developing 2 Emerging 1 Not Complete

Understanding of Statistical Tests 5 points (20/100)

Exceptionally thorough and insightful explanation of sample t-tests and chi squares

Generally clear explanation of sample t-tests and chi squares

Unclear explanation of sample t-tests and chi squares OR only explained one of the two tests

Poor explanation of both sample t-tests and chi squares

No explanation of sample t-tests or chi squares

Data Source Explanation 2.5 points (10/100)

Thorough explanation of the data source including justification of the use of this set of data

Generally clear explanation of the data source including justification of the use of this set of data

Unclear explanation of the data source including justification of the use of this set of data

Little explanation of the data source OR gave no justification of the use of this set of data

No clear discussion of the data source nor justification of the use of this set of data

Analysis and Interpretation 5 points (20/100)

Provided a detailed, in-depth interpretation of the data tables created including implications of the results and what they mean. Table is included within the paper.

Provided a clear interpretation of the data tables created including implications of the results and what they mean. Table is included within the paper

Provided an unclear interpretation of the data tables created including implications of the results and what they mean, OR the table was incorrect in the paper.

Provided little interpretation of the data tables created including implications of the results and what they mean OR the table was not included in the paper

No explanation of the of the data tables created. Section missing

Conclusion 2.5 points (10/100)

Detailed and clear summary of the importance of using the t-test/chi square to determine results in research

Generalized summary of the importance of using the t-test/chi square to determine results in research

Unclear summary of the importance of using the t-test/chi square to determine results in research

Poorly executed summary of the importance of using the t-test/chi square to determine results in research

No closing discussion of the t-test/chi square given

SPSS Datafile 2.5 points (20/100)

Well detailed, proficient creation of t tests and chi square data files submitted

Somewhat detailed, proficient creation of t tests and chi square data files submitted but missing some information

Unclear creation of t tests and chi square data files submitted or missing large pieces of information

Poorly executed creation of t tests and chi square data files submitted.

No datafiles submitted

APA: Communication Style and Mechanics 5 points (20/100)

Exceptional organization and focus throughout paper. Paper clearly addresses all required elements. Exceptionally clear writing style. Scholarly tone, without being pedantic. Exceptional use of APA format throughout paper. All referenced materials properly cited in text and reference lists. Proper grammar and spelling throughout.

Organization and focus throughout paper. Paper attempts to address all required elements. Generally clear writing style. Few lapses in scholarly tone. Few errors in use of APA format. Few missing or improperly formatted citations and/or references. Few grammatical and/or spelling errors.

Lapses in organization and focus, detracting from content and flow. Some required elements omitted. Occasional lack of clarity. Informal tone. Many errors in APA formatting. Several missing or incorrect citations and/or references. Grammar and/or spelling errors, interfering with content.

General lack of organization or focus. Many missing elements. Unclear or disrespectful writing style. Informal or disrespectful tone. Significant errors in use of APA format throughout paper. Absent references. Extensive grammatical and/or spelling errors, interfering with content.

Total /100

Journal of Adolescent Health 70 (2022) 496e499

www.jahonline.org

Adolescent health brief

Prevalence of Depression Among Adolescents in the U.S. From 2009 to 2019: Analysis of Trends by Sex, Race/Ethnicity, and Income

Michael Daly, Ph.D. * Department of Psychology, Maynooth University, Co. Kildare, Ireland

Article history: Received June 24, 2021; Accepted August 26, 2021 Keywords: Major depressive disorder; Mood disorders; Depression; Prevalence trends; Sociodemographic characteristics

See Related Editorial on p.354

A B S T R A C T IMPLICATIONS AND

Purpose: Major depression is a leading cause of disability and represents a significant health concern for adolescents. Evidence of temporal trends in adolescent depression stratified by soci- odemographic characteristics is needed. Methods: This study drew on 11 years of the National Survey on Drug Use and Health (N ¼ 167,783), a nationally representative survey of adolescents aged 12e17 years conducted between 2009 and 2019. Results: The prevalence of past-year major depressive episode (MDE) increased by 7.7 percentage points from 8.1% to 15.8% between 2009 and 2019. MDE increased by 12 percentage points from 11.4% to 23.4% among girls. The gender difference in the prevalence of MDE increased from 6.4% to 14.8% between 2009 and 2019. Black participants experienced a comparatively small increase in depression (4.1%). Conclusions: Since 2009 there has been a sharp and sustained increase in depression among adolescent girls in the U.S. This concerning trend highlights the critical importance of directing prevention and intervention efforts toward this group.

� 2021 Society for Adolescent Health and Medicine. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Conflicts of interest: No conflicts of interest reported. Data Availability: The data that support the study findings can be accessed from the Substance Abuse and Mental Health Data Archive (https://www.datafiles. samhsa.gov/). * Address correspondence to: Michael Daly, Ph.D., Department of Psychology,

Maynooth University, 1.1.7 Education House, Maynooth, Ireland. E-mail address: [email protected].

1054-139X/� 2021 Society for Adolescent Health and Medicine. Published by Elsevier Inc. This is an open access artic creativecommons.org/licenses/by/4.0/). https://doi.org/10.1016/j.jadohealth.2021.08.026

CONTRIBUTION

This study provides na- tionally representative evidence that the preva- lence of major depressive episode doubled among adolescent girls over an 11-year period from 2009 to 2019. Identifying the causes of this increase and enhancing mental health promotion, prevention, and intervention efforts targeting adolescent girls will now be crucial.

Depression is a leading cause of impairment and disability depression reported by U.S. adolescents since 2010 [5,6]. Although

globally [1] and major contributor to suicidal behavior [2]. The prevalence of fatal suicide among U.S. adolescents and young adults increased by 57.4% between 2007 and 2018 [3]. This trend has been attributed to a range of potential causes including increased prevalence of anxiety and depressive disorders over this period [4]. Of particular concern is a rise in the prevalence ofmajor

national trends in depression have been documented, reliable up- to-date estimates of trends among key subpopulations are needed. To address this gap, this study drew on nationally representative data to estimate temporal trends in the prevalence of past-year major depressive episode (MDE) across sex, race/ ethnicity, and household income groups between 2009 and 2019.

Methods

Study population

Participants were 167,783 adolescents aged 12e17 years who took part in the 2009e2019 waves of the National Survey on

le under the CC BY license (http://

M. Daly / Journal of Adolescent Health 70 (2022) 496e499 497

Drug Use and Health (NSDUH), administered using computer- assisted interviewing methods [7]. The NSDUH is an annual cross-sectional survey of the U.S. population with a high response rate (�70% for those aged 12e17 across the years examined) [7]. Each year the NSDUH utilizes a stratified, multi- stage area probability sampling method to select participants and provide nationally representative estimates for noninstitu- tionalized individuals aged 12 and older [8]. This study involved secondary analysis of anonymized microdata files and did not require institutional approval from the Maynooth University Social Research Ethics Sub-Committee.

Past-year depression

The presence of MDE in the past year was assessed using a structured interview based on DSM-5 criteria and adapted from the depression module of the National Comorbidity Survey- Adolescent which used modified version of the World Health Organization Composite International Diagnostic Interview [7,9,10]. Past-year MDE was defined as present when a partici- pant reported �5 of nine symptom criteria for MDE (e.g., prob- lems with sleeping, eating, fatigue/loss of energy, feelings of worthlessness, recurrent thoughts of death of suicidal ideation) in the same 2-week period in the past year, where �1 of the criteria was either depressed mood or loss of interest or pleasure in daily activities during this period [7].

Demographics

Participants reported their age in years (coded as 12e14, 15e 17), sex (male, female), and race/ethnicity (categorized as non- Hispanic white, non-Hispanic black, Hispanic, and other race/ ethnicity). Household income was classified as either less than $50,000 per annum or greater than or equal to $50,000 per annum.

Statistical analysis

Logistic regression analysis with cluster robust standard er- rors followed by the Stata margins postestimation commandwas used to estimate percentage-point differences in the prevalence

Table 1 Estimated changes in the prevalence of past-year MDE among U.S. adolescents from 2

Demographic characteristic 2009 survey wave (N ¼ 17,162) 2019

% of sample Depression % (95% CI)a % of s

Overall sample e 8.1 (7.5e8.7) e

Age 12e14 46.8 5.4 (4.7e6.2) 49.6 Age 15e17 53.2 10.5 (9.5e11.4) 50.4 Male 51.2 5.0 (4.3e5.6) 51.1 Female 48.8 11.4 (10.5e12.3) 48.9 White 58.6 8.3 (7.6e9.0) 51.7 Hispanic 19.5 8.1 (6.5e9.8) 24.8 Black 15.0 7.4 (5.9e8.9) 13.4 Other race/ethnicityc 7.0 7.9 (5.9e9.9) 10.1 Income <$50,000 46.4 8.0 (7.1e9.0) 40.0 Income �$50,000 53.6 8.2 (7.5e8.9) 60.0

a Estimates are from marginal effects calculated after logistic regression with clust significant at the p < .001 level.

b Percentage change in depression levels relative to 2009 levels defined as: [Perce depression in 2009].

c Race/ethnicity was self-reported. The other race/ethnicity group includes Asian, Am non-Hispanic multiracial groups which were combined due to small group sizes.

of MDE between 2009 and 2019, with statistical significance defined as two-sided, p < .05. Interactions between survey year and demographic characteristics were tested to identify whether changes in the prevalence of MDE from 2009 to 2019 differed as a function of participant sex, race/ethnicity, household income, and age groups. All analyses incorporated sampling weights taking into account the NSDUH’s complex survey sampling design to generate nationally representative estimates.

Results

In the NSDUH, the prevalence of MDE increased significantly from 8.1% (95% confidence interval [CI] 7.5e8.7) in 2009 to 15.8% (95% CI 15.2e16.5) in 2019, an increase of 7.7 percentage points (95% CI 6.8e8.6) (Table 1, Figure 1A). Depression levels increased by 3.2 percentage points (95% CI 2.3e4.1) from 2009 to 2014 and by 4.5 percentage points (95% CI 3.5e5.5) from 2014 to 2019.

Statistically significant increases in the prevalence of MDE were observed across all demographic groups examined (Table 1). Depression levels among female participants increased by 12 percentage points (95% CI 10.4e13.5) between 2009 and 2019, from 11.4% to 23.4%. This increase was 8.3 percentage points (95% CI 6.2e10.4) larger than the increase experienced by males over the same period (3.7%, 95% CI 2.5e4.8) (Figure 1B). The gender difference in adolescent depression levels increased from 6.4 percentage points (95% CI 5.4e7.5) in 2009 to 14.8 percentage points (95% CI 12.9e16.6) in 2019. Both males and females experienced statistically significant increases in depression from 2009 to 2019 across all age, race/ethnicity, and income groups examined, as shown in Table S1. Female partici- pants experienced a significantly larger increase in depression than male participants in each age, income, and race/ethnicity group except for black participants where the change in depression did not differ significantly between males and fe- males (Table S1).

Black participants experienced a 4.1 percentage point (95% CI 1.7e6.5) increase in the prevalence of MDE which was smaller than the increase experienced by white (�3.3% lower increase, 95% CI �6.1 to �.6), Hispanic (�5.6%, 95% CI �9.1 to �2.1), and other race/ethnicity participants (�5.1%, 95% CI �9.4 to �.7). Depression trends for black and white/Hispanic/other race/

009 to 2019 by sociodemographic characteristics

survey wave (N ¼ 12,950) Change in depression % (95% CI)a

Percentage increaseb

ample Depression % (95% CI)a

15.8 (15.2e16.5) 7.7 (6.8e8.6) 95.1% 12.2 (11.2e13.3) 6.8 (5.5e8.0) 126.0% 19.4 (18.1e20.7) 8.9 (7.4e10.5) 84.8% 8.6 (7.6e9.6) 3.7 (2.5e4.8) 74.0%

23.4 (22.1e24.7) 12.0 (10.4e13.5) 105.3% 15.8 (14.8e16.7) 7.5 (6.3e8.7) 90.4% 17.8 (16.0e19.6) 9.7 (7.3e12.1) 119.8% 11.5 (9.6e13.5) 4.1 (1.7e6.5) 55.4% 17.1 (14.3e19.8) 9.2 (5.8e12.6) 116.5% 15.5 (14.2e16.8) 7.5 (5.9e9.1) 93.8% 16.0 (15.1e17.0) 7.9 (6.7e9.1) 96.3%

er robust standard errors. All estimates of change in depression are statistically

ntage point change in depression from 2009 to 2019 � 100]/[Percentage with

erican Indian/Alaska Native, Native Hawaiian and Other Pacific Island, and other

Figure 1. Trends in the prevalence of past-year MDE from 2009 to 2019 in the (A) overall NSDUH sample, for (B) males and females, and (C) nonblack and black race/ ethnicity groups. Note: NSDUH graphs are derived from logistic regression analysis of 167,783 participants. 95% confidence intervals are presented.

M. Daly / Journal of Adolescent Health 70 (2022) 496e499498

ethnicity participants is shown in Figure 1C. The magnitude of the increase in the prevalence of MDE between 2009 and 2019 did not differ significantly by household income or participant age.

Discussion

This study of over 160,000 adolescents aged 12e17 years drew on nationally representative data to show that the preva- lence of MDE approximately doubled between 2009 and 2019 (from 8.1% to 15.8%). This prolonged rise in MDE is concerning because adolescent depression tends to persist into adulthood

[11] and forecasts adverse health and socioeconomic conse- quences throughout life [9,12].

In the current study, the gender disparity in depression more than doubled (from 6.4 to 14.8 percentage points) between 2009 and 2019 driven by a substantial rise in the prevalence of MDE among females over this period. This finding extends existing evidence that has found significant though less pronounced in- creases in internalizing problems [13,14] and depressive symp- toms [15] among adolescent girls since the beginning of the 21st century. Potential reasons for this increase are manifold and include increases in bullying and victimization [16] and use of social media and technology [17] which may have been more impactful for girls than boys. In addition, reduced sleep quality

M. Daly / Journal of Adolescent Health 70 (2022) 496e499 499

and quantity [18], the long-term impact of the Great Recession, and rising educational expectations [19] may have contributed to the rise in depression.

The current study is limited in its reliance on self-reports of depressive symptoms that may differ from clinical evaluations and could be subject to recall bias. This study utilized repeated cross-sectional data and it remains possible that the surveyed populations differed over time, though the NSDUH’s high response rate, consistent sampling design, and use of survey weights safeguards against this possibility.

The results highlight the need for further investment in adolescent mental health promotion, mental health services, and intervention programs. School group-based interventions, exer- cise, and psychological therapy have demonstrated effectiveness in reducing adolescent depressive symptoms [20] and could help tackle the marked rise in depression among adolescent girls identified in this study.

Funding Sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary Data

Supplementary data related to this article can be found at https://doi.org/10.1016/j.jadohealth.2021.08.026.

References

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[2] Simon GE. Social and economic burden of mood disorders. Biol Psychiatry 2003;54:208e15.

[3] Curtin SC. State suicide rates among adolescents and young adults aged 10e24: United States, 2000e2018. Natl Vital Stat Rep 2020;69:1e10.

[4] Miron O, Yu KH, Wilf-Miron R, Kohane IS. Suicide rates among adoles- cents and young adults in the United States, 2000-2017. JAMA 2019;321: 2362e4.

[5] Weinberger AH, Gbedemah M, Martinez AM, et al. Trends in depression prevalence in the USA from 2005 to 2015: Widening disparities in vulnerable groups. Psychol Med 2018;48:1308e15.

[6] Twenge JM, Cooper AB, Joiner TE, et al. Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005e2017. J Abnorm Psychol 2019;128:185.

[7] Center for Behavioral Health Statistics and Quality. 2019 National survey on Drug Use and health: Methodological summary and definitions. Rock- ville, MD: Substance Abuse and Mental Health Services Administration; 2020.

[8] Center for Behavioral Health Statistics and Quality. 2018 national survey on Drug Use and health (NSDUH): Methodological Resource Book, section 11, Person-level sampling weight calibration. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2020.

[9] Merikangas KR, He JP, Burstein M, et al. Lifetime prevalence of mental disorders in US adolescents: Results from the national Comorbidity survey Replicationeadolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 2010;49:980e9.

[10] Kessler RC, Ustun TB. The World mental health (WMH) survey Initiative version of the World health Organization (WHO) Composite International Diagnostic interview (CIDI). Int J Methods Psychiatr Res 2004;13:93e121.

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[12] Clayborne ZM, Varin M, Colman I. Systematic review and meta-analysis: Adolescent depression and long-term psychosocial outcomes. J Am Acad Child Adolesc Psychiatry 2019;58:72e9.

[13] Bor W, Dean AJ, Najman J, Hayatbakhsh R. Are child and adolescent mental health problems increasing in the 21st century? A systematic review. Aust N Z J Psychiatry 2014;48:606e16.

[14] Fink E, Patalay P, Sharpe H, et al. Mental health difficulties in early adolescence: A comparison of two cross-sectional studies in England from 2009 to 2014. J Adolesc Health 2015;56:502e7.

[15] Keyes KM, Gary D, O’Malley PM, et al. Recent increases in depressive symptoms among US adolescents: Trends from 1991 to 2018. Soc Psychi- atry Psychiatr Epidemiol 2019;54:987e96.

[16] Pontes NM, Ayres CG, Lewandowski C, Pontes MC. Trends in bullying victimization by gender among US high school students. Res Nurs Health 2018;41:243e51.

[17] Twenge JM, Martin GN. Gender differences in associations between digital media use and psychological well-being: Evidence from three large data- sets. J Asolesc 2020;1:91e102.

[18] Twenge JM, Krizan Z, Hisler G. Decreases in self-reported sleep duration among US adolescents 2009e2015 and association with new media screen time. Sleep Med 2017;39:47e53.

[19] Johnson MK, Staff J, Patrick ME, Schulenberg JE. Adolescent adaptation before, during and in the aftermath of the Great Recession in the USA. J Psychol 2017;52:9e18.

[20] Das JK, Salam RA, Lassi ZS, et al. Interventions for adolescent mental health: An overview of systematic reviews. J Adolesc Health 2016;59:S49e60.

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