Describing the impact of Smoking and Drinking Alcohol on Poor Physical or Mental health of individuals, using The Behavioral Risk Factor Surveillance System (BRFSS) dataset.

In 1984 a study began by The Centers for Disease and Control Center (CDC) (2018b) in the United States using The Behavioral Risk Factor Surveillance System (BRFSS) survey (e.g. state based). This research by the CDC was conducted in all states using cellular and landline telephone (CDC, 2018b). One of the goals of the BRFSS is to gather data on risk behaviors and or preventative health practices (CDC, 2016). BRFSS assessed several factors, such as alcohol consumption, tobacco use and or exercising (CDC, 2016). The CDC received their data from the each state health departments, which allowed them to conduct analysis’s (CDC, 2016). 

This quantitative study is being conducted to describe the impact of depression, alcohol and or smoking on poor physical or mental health. CDC (2018b) argues that in America the leading preventable cause of death is tobacco use. Control programs and Tobacco programs needs to be appropriate (CDC, 2018b).It has been found that factors of binge drinking are injuries (e.g. car accidents), alcohol poisoning and or liver damage (CDC, 2018b). Smoking and substance abuse has been associated with depressive disorder (CDC). The CDC (2018B) argues that it is beneficial to investigate and or address these conditions to minimize the habitualness of poor physical and mental status.

Social Change 

This research is purposeful in bringing about social change to the social justice issue of poor physical or mental health of individuals. Kinderman, (2014) argues that the role of psychologist in bringing about social change is to study why individuals behave in certain ways. Humans face problems and psychologist needs to help society understand and bring solutions to those problems (Kinderman, 2014). This research will describe how the predictor variables smoking and alcohol is correlated with poor physical and mental health. This research results and analysis of the data, will allow for the researcher to bring social change to individuals, institutions and or society. The research will allow the researcher to look at methods to minimize the impact of alcohol, smoking and or depression on poor physical or mental health of individuals, such as improved treatment planning, creation of curriculum’s, conducting seminars and or new methods of therapy. This research can also be purposeful in bringing about social change, in determining how we can improve the conditions of those who have poor physical and mental health. 

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Poor Physical and Mental Health

The BRFSS survey questions for the variable poor health was “During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?” (CDC, 2018a, p. 18). Forty Three percent of the participants (N= 102,920) reported they had days of poor physical or mental health (CDC, 2018a). Fifty-five percent participants (N= 129,598) reported not having days of poor physical or mental health (CDC, 2018a). Only two percent reported they did not know or was sure that they had poor physical or mental health (CDC, 2018a). These results were derived from individuals who had been diagnosed with depression and anxiety (CDC, 2018a). “The estimated prevalence of adults reporting poor physical or mental health for ≥14 of the last 30 days also varied by geographic region” (CDC, 2018b).

Poor Physical Health

According to the CDC (2018b) injuries or physical illness are forms of poor physical health. In a study using the BRFSS data Bosch et al (2017) found that smoking, physical inactivity, binge drinking and or high risk sexual behaviors are negative health outcomes for women who experience intimate partner violence (IPV). Women who experience IPV need medical care, due to their acute physical injuries (Bosch et al, 2017). One risk factor for women experiencing IPV is having a lower socioeconomic status, which can cause women to be financially dependent and not leave their partner (Bosch et al, 2017). Anderson (2013) conducted a study using the BRFSS data to examine how poor physical health is impacted by stress caused by an individual experiencing racism. It was found that African Americans experience high emotional and physical distress, due to racism (Anderson, 2013).

Poor Mental Health

Characteristics of poor mental health include problems with emotions, stress and depression (CDC, 2018B). CDC (2018b) reports adults can manage depression better if they enhance mental health care and minimize stigmatization related to mental illness. In a study by Fluetsch et al (2019) using BRFSS data, it was found that participation in some form of physical activity (e.g. aerobic exercise) minimizes days of poor mental health. Characteristics of depression disorder are absence of positive effect and or behavioral and cognitive symptoms (Fluetsch et al, 2019). Fluetsch et al (2019) reported stress is characterized as having sleep disturbances, fatigue and or irritability.

Now it has been found individuals who smoke tobacco are comorbid (22% to 25% with depressive disorder and or more likely at 2 times to 8.1 times to have depressive disorder and other health conditions (e.g. substance use disorder and anxiety) (APA, 2013). Individuals who smoke cigarettes and or smokeless tobacco can have Tobacco Use Disorder (APA, 2013). Tobacco withdrawal is due to nicotine deprivation and or the level of nicotine in cigarettes (APA, 2013). Guydish et al, 2016 argues use of tobacco products, “smokeless tobacco, standard cigars, little filtered cigars (LFCs), or e-cigarettes/vape pens” may be infrequent, experimental or current (p. 95). Alcohol intoxication can cause depressive symptoms (APA, 2013). Alcohol use disorder and or intoxication can cause alcohol induced depression and alcohol use disorder can be comorbid with depressive disorders, anxiety disorder, anti-social personality disorder and schizophrenia (APA, 2013).

Study Purpose/Research Objectives 

 The purpose of this quantitative descriptive study is to describe the impact of the predictor variables (IV) Smoke Every Day (X1) [coded 1], Smoke Some Days (X2) [coded 2] and Smoke Not at All (X3) [coded 3] Drink Alcohol Yes (X4) [coded 1] and Drink Alcohol No (X5) [coded 2]) has on the dependent variable (DV) poor physical and mental health [POORHLTH], using The Centers for Disease and Control Center (CDC) 2015 Behavioral Risk Factor Surveillance System (BRFSS) data.

Research Question

RQ1: Is the IV Smoke Every Day (X1) [coded 1], Smoke Some Days (X2) [coded 2] and Smoke Not at All (X3) [coded 3], Drink Alcohol Yes (X4) [coded 1] and Drink Alcohol No (X5) [coded 2] a significant predictor of  DV Poor Physical and Mental Health [POORHLTH] (Y) when controlling for other variables?

 Hypothesis

  Ho1: Smoke Every Day (X1) [coded 1] is not a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ha1: Smoke Every Day (X1) [coded 1] is a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ho2: Smoke Some Days (X2) [coded 2] is not a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ha2: Smoke Some Days (X2) [coded 2] is a significant predictor of Poor Physical and Mental Health [POORHLTH] when controlling for all other variables.   

Ho3: Smoke Not at All (X3) [coded 3] is not a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ha3: Smoke Not at All (X3) [coded 3] is a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ho4: Drink Alcohol Yes (X4) [coded 1] is not a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ha4: Drink Alcohol Yes (X4) [coded 1] is a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y)when controlling for all other variables.

Ho5: Drink Alcohol No (X5) [coded 2] is not a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables.

Ha5: Drink Alcohol No (X5) [coded 2] is a significant predictor of Poor Physical and Mental Health [POORHLTH] (Y) when controlling for all other variables

Variables

The independent variables (IV) and or predictor variable for this quantitative descriptive research study are Frequency of Days now smoking [SMOKDAY2] and Drink any alcoholic beverages in the past 90 days [DRNKANY5]. The dependent variable and or outcome variable is Poor Physical and Mental Health [POORHLTH]. The dummy variables utilized were (Smoke Every Day (X1) [coded 1], Smoke Some Days (X2) [coded 2] and Smoke Not at All (X3) [coded 3]), reference variable Frequency of Days now smoking [SMOKDAY2]. Dummy variable utilized were (Drink Alcohol Yes (X4) [coded 1] and Drink Alcohol No (X5) [coded 2], reference variable Drink any alcoholic beverages in the past 90 days [DRNKANY5].

Methodology 

A Data from the 2015 The Behavioral Risk Factor Surveillance System (BRFSS) survey by The Centers for Disease and Control Center (CDC) was used for this research (2018b). The sample for this survey measuring behavior risk factors was derived from non institutionalized adult individuals (e.g. 18 and older) living throughout the United States (e.g. 53 states) (CDC, 2016). Data collection was derived from the Computer-Assisted Telephone Interview (e.g. software package Ci3 WinCATI) (CDC, 2016). The interviews were conducted by contractors or state personal, following the BRFSS guidelines (CDC, 2016). The interviews lasted between twenty-three minutes and twenty-eight minutes, due to the additional questions added by state personal (CDC, 2016). Originally multiple regression analysis (MRA) was utilized to examine if the dummy coded predictor variables (e.g. Smoke Every Day (X1) [coded 1], Smoke Some Days (X2) [coded 2], Smoke Not at All (X3) [coded 3], Yes Depressive Disorder (X4) [coded 1], No Depressive Disorder (X5) [coded 2], (Yes Alcohol (X6) [coded 1] and No Alcohol (X7) [coded 2], have an impact on the dependent variable (Y) Poor Physical and Mental Health POORHLTH] (CDC, 2016). After checking for assumptions dummy variable (Yes Depressive Disorder (X4) [coded 1] and No Depressive Disorder (X5) [coded 2]), reference variable Ever told you had a depressive disorder [ADDEPEV2], was removed from the model. The predictor variable Ever told you had a depressive disorder [ADDEPEV2], did not meet the assumptions for  multicollinearity, (Yes Depressive Disorder, Tolerance = .055, VIF = 18.261; No Depressive Disorder Tolerance = .055, VIF = 18.237), see Table 9 (e.x. Dart, 2013)., The multiple regression analysis (MRA) allowed for additional statistics (e.g. regression coefficients [estimates and or confidence intervals-95%], descriptive, part and partial correlations, collinearity diagnostics and or plots of standardized residuals) to be run.

A Multiple Regression Analysis with two or more predictor variables 

 According to Vogt (2005) a multiple regression analysis (MRA) is a statistical method, where the dependent (e.g. outcome) variable is measured by more than one independent (or predictor) variable to determine the effect. An MRA answer two questions, “What is the effect (as measured by a regression coefficient) on a dependent variable (DV) of a one-unit change in an independent variable (IV), while controlling for the effects of all the other IVs and or what is the total effect (as measured by the R2) on the DV of all the IVs taken together” (Vogt, 2005) Warner (2013) argues in controlling for all other predictors, when two or more predictors are in the regression, each slope (e.g. for the predictor variable) is calculated. Most forms of an ANOVA analysis are possible with a multiple regression analysis (MRA) (Vogt, 2005). Comparison of variables to determine how the (Y) outcome is related to each (X) predictor variable the multiple regression analysis produces scores of beta coefficients (Warner, 2013). 

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Analysis and Results

The sample (N = 425) for this multiple regression analysis was derived from individuals who took the he Behavioral Risk Factor Surveillance System (BRFSS) survey (format, Childers, (n.d). According to the CDC (2018) healthy behaviors are individuals who avoid cigarette smoking, who participate in physical activity and or individuals who do not binge drink. The model met the assumptions for outliners +/- 3.29 (Std. Residual Min = -1.917, Std. Residual Max = 2.168), see table 6 (e.x. Dart, 2013). Predictor variables (Smoke Every Day, Tolerance = .865, VIF = 1.156; Smoke Some Days Tolerance = .934, VIF = 1.070; Smoke Not at All, Tolerance = .871 VIF = 1.148; Drink Alcohol Yes, Tolerance = .198 VIF = 5.054 and Drink Alcohol No, Tolerance = .202 VIF = 4.948), met the assumptions for multicollinearity (e.x. Dart, 2013). According to table 4, independent errors assumption was met (Durbin-Watson value = 1.99) (e.x. Dart, 2013).

Descriptive statistics in Table 1 indicated the mean (M = .69, SD = .463) for the predictor variable Drink Alcohol No (X5) [coded 2] was higher than the mean (M = .29, SD = .456) for Smoke Not At All (X3) [coded 3] and higher than the means for Drink Alcohol Yes (X4) [coded 1]  at (M = .26, SD = .441), Smoke Every Day (X1) [coded 1] at (M = .13, SD = .340) and Smoke Some days (X2) [coded 2] at (M = .06, SD = .235).  Table 1 indicated that adults Drink Alcohol No at an average of 69% (SD = .463), Smoke Not At All at an average of 29% (SD = .456), Drink Alcohol Yes at an average of 26% (SD = .441), Smoke Every Day at an average of 13 % (SD = .340), Smoke Some Days at an average of 6% (SD = .235) (ex, Napoli, n.d.).

The Data Analysis was derived using SPSS 27 Software (ex, Napoli, n.d.).  To examine the correlation of predictor variables Smoke Every Day, Smoke Some Days, Smoke Not at All, Drink Alcohol Yes and Drink Alcohol No on the outcome variable Poor Physical and Mental Health a Pearson R was utilized (e.x Napoli, n.d.). Smoke Every Day and Poor Physical and Mental Health were significantly, positively and weakly related (r (490) = .120, p < .001, r2= .01) (e.x Napoli, n.d.). The variance between Smoke Every Day and Poor Physical or Mental Health is 1%, the correlation coefficient .120 = r squared = .01 for 1%. Smoke Some Days and Poor Physical and Mental Health were not significantly significant (r (490) = .057, p = .103, r2= .00) (e.x Napoli, n.d.). There is no variance between Smoke Some Days Day and Poor Physical or Mental Health is 0%, the correlation coefficient .057 = r squared = .00 for 0%.  Smoke Not At All and Poor Physical and Mental Health are not statistically significantly (r (490) = .026, p = .281, r2= .00) (e.x Napoli, n.d.). There is no variance between Smoke Not At All and Poor Physical or Mental Health is 0%, the correlation coefficient .026 = r squared = .00 for 0%. Drink Alcohol Yes on Poor Physical and Mental Health were significantly, negatively and weakly related (r (490) = -.154, p < .001, r2= .02) (e.x Napoli, n.d.). The variance between Drink Alcohol Yes and Poor Physical or Mental Health is 2%, the correlation coefficient -.154 = r squared = .02 for 2%. Drink Alcohol No and Poor Physical and Mental Health were significantly, positively and weakly related (r (490) = .139, p < .001, r2= .02) (ex, Napoli, n.d.). The variance between Drink Alcohol Yes and Poor Physical or Mental Health is 1%, the correlation coefficient .139 = r squared = .02 for 2%.

A Multiple Regression Analysis was utilized to determine how the dependent (e.g. outcome) variable was impacted by the predictor variable Smoke Every Day, Smoke Some Days Smoke Not at All, Drink Alcohol Yes and Drink Alcohol No (ex, Napoli, n.d.). A total of five predictor variables was entered into the regression and is displayed in Tables 1-8 (ex, Napoli, n.d.). The ANOVA Analysis indicated that the overall model was significant, R2 = .06 (F (5, 589) = 6.49, p < .001, see table 4 and 5 (ex, Napoli, n.d.). The R2 equaled .765 (adjusted R2 .05), which indicates that all predictor variables Smoke Every Day, Smoke Some Days Smoke Not at All, Drink Alcohol Yes were statistically significant and Drink Alcohol No were not significant with the dependent variable, Poor Physical or Mental Health (ex, Napoli, n.d.).

Table 6, displays regression weights indicating predictor variables having a positive and negative significance with the dependent variable Poor Physical and Mental Health (ex, Napoli, n.d). The regression coefficient (B) for Smoke Every Day equaled 6.002, (95% CI for B = 3.050 to 8.954) is statistically significant (t(489) = 3.99, p < .001, = sr2 = .03, see table 6. The regression coefficient (B) for Smoke Some Days equaled 5.163, (95% CI for B = 1.008 to 9.318) is statistically significant (t(489) = 2.44, p = .02, = sr2 = .01, see table 6. The regression coefficient (B) for Smoke Not At All equaled 2.711, (95% CI for B = .492 to 4.930) is statistically significant (t(489) = 2.40, p = .02, = sr2 = .01, see table 6. The regression coefficient (B) for Drink Alcohol Yes equaled -5.304, (95% CI for B = -4.601 to 4.472) is not statistically significant (t(489) = -2.057, p = .04, = sr2 = .90, see table 6. The regression coefficient (B) for Drink Alcohol No equaled -.064, (95% CI for B = -9.842 to -.225) is statistically significant (t(489) = -.028, p ˃ .98, = sr2 = .00, see table 6.

The null hypothesis for one through four will be rejected for predictor variables Smoke Every Day (X1) [coded 1], Smoke Some Days (X2) [coded 2] and Smoke Not at All (X3) [coded 3]), Drink Alcohol Yes (X4) [coded 1] that they are not a significant predictor of poor physical and mental health. We will accept the null hypothesis five that Drink Alcohol No (X5) [coded 2], is not a significant predictor of poor physical and mental health.

Implications for Social Change

  • Individual: Individuals can be educated on what enhances good physical and mental health.
  • Family: Families can be provided with resources that will change unhealthy behaviors that lead to poor physical and mental health.
  • Organizational: This research will allow organizations to become aware of some of the contributing factors that lead to poor physical and mental health. These organizations can seek evidence based programming and or therapy related treatments based on the findings of this research, which will allow for enhanced programming. 
  • Societal/Policy: The government can continue to create policies that will minimize drinking alcohol and smoking and provide monetary factors to organizations who aspire to educate individuals on behavioral (e.g. unhealthy) risk factors. The government can decide on a health care plan for Americans that will be beneficial in helping individuals maintain good physical and mental health.

Limitations  

  • The Behavioral Risk Factor Surveillance System (BRFSS) survey data set, did not have a variable (interval/scale or nominal) on respondents race (CDC, 2018b). 
  • The Behavioral Risk Factor Surveillance System (BRFSS) survey dataset, did not have a variable (interval/scale and or nominal) on respondents geographical area (CDC, 2018b).
  • Did not use respondent sex for this multiple regression analysis, which could have provided quantitative data on how each gender risk taking behaviors impacts the dependent variable Poor Physical and Mental Health.
  • APA, 2013 indicates that Alcohol Use Disorder can cause physical and or psychological disorder and not being able to fulfill role at work. Further research is needed to examine the correlation of mild, moderate and severe alcohol use disorder on the impact of the outcome variable poor physical and mental health (APA, 2013).
  • Predictor dummy variables for being diagnosed for depression was not a good fit for the model, which could have enhanced validity and or reliability in the model by examining the true impact of the predictor variable’s on Poor Physical and Mental Health.

Validity and Reliability    

  • The Multiple Regression Analysis was valid and reliable and was enhanced due to using the CDC BRFSS survey data set, that had a sample size of (N = 495) of non-institutionalized individuals throughout the United States, which allows the findings to be compared to the U.S. population (CDC, 2018).
  • Validity and reliability could have been minimized due to dummy coding nominal variables and using them as predictors of the outcome variable Poor Physical and Mental Health.
  • The Entire Model met the assumptions for outliners, multicollinearity, homoscedasticity (e.g. normally distributed histogram) and or independent errors (ex, Dart, 2013).

Conclusion

It has been found with that uninsured women smoke cigarettes more than insured women (Robins et al, 2018). Currently cigarette smoking is highest among non-Hispanic White women at (21.1%), non-Hispanic Black women at (15.6%) and Hispanic women at (8.9%) (Robins et al, 2018). High levels of nicotine have been found in African American males and Non-Hispanic Whites are more likely to have tobacco use disorder (APA, 2013). Anic et al, (2018) argues that smokeless tobacco has been more effective than e-cigarettes in reducing cigarette smoking. Individuals who continue to smoke, over half will die from smoke and tobacco related illnesses (APA, 2013). APA (2013) has reported that alcohol use disorder can cause impairment (e.g. work and driving). Despite these findings drinking alcohol yes is negatively and significant and or a strong predictor of poor physical and mental health. Smoking every day was a weak predictor of poor physical and mental health, but was positively and statistically significant to poor physical and mental health.

Tallie, S. (2020 November 19). The Impact of Smoking and Alcohol on Poor Physical and Mental Health. [You Tube]. https://youtu.be/Hvr4DhTwvrs\

Tables and Figures are in PowerPoint Presentation

References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing

Anderson, K. F. (2013). Diagnosing discrimination: Stress from perceived racism and the mental and physical health effects. Sociological Inquiry, 83(1), 55–81. https://doi-org.ezp.waldenulibrary.org/10.1111/j.1475-682X.2012.00433.x

Anic, G. M., Holder-Hayes, E., Ambrose, B. K., Rostron, B. L., Coleman, B., Jamal, A., & Apelberg, B. J. (2018). E-cigarette and Smokeless Tobacco Use and Switching Among Smokers: Findings From the National Adult Tobacco Survey. American journal of preventive medicine, 54(4), 539–551. https://doi-org.ezp.waldenulibrary.org/10.1016/j.amepre.2017.12.010

Bosch, J., Weaver, T. L., Arnold, L. D., & Clark, E. M. (2017). The impact of intimate partner violence on women’s physical health: Findings from the Missouri behavioral risk factor surveillance system. Journal of Interpersonal Violence, 32(22), 3402–3419. https://doi-org.ezp.waldenulibrary.org/10.1177/0886260515599162

Centers for Disease and Control Center (CDC). (2016). Behavioral Risk Factor Surveillance System. https://www.cdc.gov/brfss/annual_data/2015/pdf/overview_2015.pdf

Centers for Disease and Control Center (CDC). (2018a). LLCP 2017 Codebook Report Overall version data weighted with _LLCPWT Behavioral Risk Factor Surveillance system.https://www.cdc.gov/brfss/annual_data/2017/pdf/codebook17_llcp-v2-508.pdf

Centers for Disease and Control Center (CDC). (2018b). Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas — Behavioral Risk Factor Surveillance System, United States, 2015 https://www.cdc.gov/mmwr/volumes/67/ss/ss6709a1.htm

Dart, A., (2013). Reporting Multiple Regressions in APA format – Part One. https://www.adart.myzen.co.uk/reporting-multiple-regressions-in-apa-format-part-one/

Guydish, J., Tajima, B., Pramod, S., Le, T., Gubner, N. R., Campbell, B., & Roman, P. (2016). Use of multiple tobacco products in a national sample of persons enrolled in addiction treatment. Drug and Alcohol Dependence166, 93–99. https://doi-org.ezp.waldenulibrary.org/10.1016/j.drugalcdep.2016.06.035

Fluetsch, N., Levy, C., & Tallon, L. (2019). The relationship of physical activity to mental health: A 2015 behavioral risk factor surveillance system data analysis. Journal of Affective Disorders, 253, 96–101. https://doi-org.ezp.waldenulibrary.org/10.1016/j.jad.2019.04.086

Kinderman, P. (2014). The role of the psychologist in social change. International Journal of Social Psychiatry, 60(4), 403–405. https://doi.org/10.1177/0020764013491741

Napoli, A., (n.d.). GPA, Study Time, and Attendance. Walden University. https://class.waldenu.edu/courses/1/USW1.3546.202110/db/_111918439_1/embedded/APA%20Summary%20for%20Multiple%20Regression.pdf

Robbins, C., Boulet, S. L., Morgan, I., D’Angelo, D. V., Zapata, L. B., Morrow, B., Sharma, A., & Kroelinger, C. D. (2018). Disparities in Preconception Health Indicators – Behavioral Risk Factor Surveillance System, 2013-2015, and Pregnancy Risk Assessment Monitoring System, 2013-2014. Morbidity and Mortality Weekly Report. Surveillance Summaries (Washington, D.C. : 2002), 67(1), 1–16. https://doi-org.ezp.waldenulibrary.org/10.15585/mmwr.ss6701a1

Tallie, S. (2020). Describing the impact of Smoking and Drinking Alcohol on Poor Physical or Mental health of individuals, using The Behavioral Risk Factor Surveillance System (BRFSS) dataset [PowerPoint slides]. Walden University. https://www.slideshare.net/SchelandriaRTallie/wk11-finalprojectpresentation-2tallies

Vogt, W. P. (2005). Dictionary of statistics & methodology (Vols. 1-0). SAGE Publications, Inc. doi: 10.4135/9781412983907

Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). SAGE Publications.

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