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Regression Modelling in Practice - Assign 4: Test a Logistic Regression Model - V2 (Nicotine Dependence)

I am exploring the factors associated with the nicotine dependence such as introductory age to cigarette, alcohol, marijuana, and the number of best friends of respondent who smoked at least one cigarette a day.

Binary Dependent/Response Variable: Nicotine Dependence: H1TO3 {Yes=1 / No=0} = smoked cigarettes regular, at least 1 cigarette in the last 30 days.

Explanatory Variable:

H1TO2 = Introductory Age to Cigarette

H1TO14 = Introductory Age to Alcohol

H1TO30 = Introductory Age to Marijuana

H1TO9 = # of best friends, how many smoke at least 1 cigarette a day

Data Preparation:

The data was already collected in binary format for the response variable, therefore the data preparation of the response variable was not heavily required. However I set unknown for rejected or refused answers.

Results:

Individually, logistic regression analysis concluded that the association between the response variable and explanatory variable were statistically significant for the following pairs.

1. Nicotine Dependence vs introductory age to cigarette

2. Nicotine Dependence vs Introductory Age to Alcohol

3. Nicotine Dependence vs Introductory Age to Marijuana

4. Nicotine Dependence vs # of best friends, how many smoke at least 1 cigarette a day

However, when looking at all the explanatory variables in a single model, it shows that introductory age to marijuana was not statistically significant.

I went further and removed association of the number of best friends who smokes from model, which indicated that introductory age to marijuana, introductory age to alcohol was also not statistically significant.

From this, I conclude that the number of best friends smoking cigarette, confounds the relationship between the introductory age to marijuana and nicotine dependence. Also, the same can be said for the number of best friends smoking, confounds the relationship between the introductory age to alcohol and nicotine dependence.

The odd ratio indicates for individuals with best friend smoking are 2.114 times more likely to be nicotine dependence. In addition the confidence interval is between 1.87 and 2.4 for nicotine dependence and the number of best friends who smokes.

/* Student Name: Rajeev.S Assignment 04 File: c3_assign4-v09.sas Date: 2016-03-13 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

LABEL

H1TO2 = "Introductory Age to Cigarette" /*1 to 20*/ H1TO14 = "Introductory Age to Alcohol" /*1 to 19*/ H1TO30 = "Introductory Age to Marijuana" /*1 to 18*/ H1TO9 = "# of best friends, how many smoke at least 1 cigarette a day?" H1TO3 = "Have you ever smoked cigarettes regularly, that is, at least 1 cigarette every day for 30 days?" ;

/* Tried smoking everyday: 6 = Refused; 7= Legit Skip; 0 = no; 1 = yes;*/ if H1TO3 > 1 then H1TO3 = .; /*Of your 3 best friends, how many smoke at least 1 cigarette a day? */ if H1TO9 > 3 then H1TO9 = .; /* Introductory Age to Cigarette */ if H1TO2 = 0 or H1TO2 > 95 then H1TO2 = .;

/*Introductory Age to Alcohol 1 to 19*/ if H1TO14 = 0 or H1TO14 > 95 then H1TO14 = .; /*Introductory Age to Marijuana - 1 to 18*/ if H1TO30 = 0 or H1TO30 > 95 then H1TO30 = .;

/* Introductory Age to Cigarette */ PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1TO2;

/*Introductory Age to Alcohol 1 to 19*/ PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1TO14; run;

/*Introductory Age to Marijuana - 1 to 18*/ PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1TO30; run;

/*# of Best friends smoking atleast 1 cigeratte a day*/ PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1TO9; run;

/*Model with all the explanatory variables*/ PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1TO2 H1TO14 H1TO30 H1TO9; run;

/*Model with all the explanatory variables - Removed the Best Friends smoking*/ PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1TO2 H1TO14 H1TO30 ; run;

#sas logistic regression rajeev299

Regression Modelling in Practice - Assign 4: Test a Logistic Regression Model

Hypothesis

For this assignment I would like to look into the following hypothesis/research question. An individual’s amount of cigarette smoking(nicotine dependence) is associated with the individual’s age on when the very first cigarette was introduced/smoked. Nicotine dependence could also be associated with the introductory age to other form of substance/drugs. (Eg. First time smoke Marijuana, First time used cocaine, First time used Alcohol and etc).

I also hypothesis that nicotine dependence of an individual is also associated with the number of best friends who smoke regular with that individual and how well the individual’s self rated depression feelings.

Nicotine dependence is also associated with the self rated depression feeling.

Degree of feeling depression:

0=Never or rarely 1=Sometimes

2=A lot of time

3=Most of the time

When I ran the GLM test with this categorical variable, I discovered that none of these groups were significantly different in number of cigarettes smokes (nicotine dependence). This test was performed for each of the categorical group of depression feeling and none of the groups result yielded any significant differences.

Examining an individual’s nicotine dependence is also associated with the number of best friends smoked.

Categorical Variable: Number of best friends smoked (0, 1, 2, 3)When I ran the GLM test with this categorical variable, I discovered that none of these groups were significantly different in number of cigarettes smokes (nicotine dependence). This test was performed for each of the categorical group of # of best friends smoking cigarettes and none of the groups result yielded any significant differences. However group 0 and group 3 came very close. (The diagram below is the result for the categorical group zero. ).

Logistic Regression for a Binary Response Variable

From the table below we see that, none of the logistic regression is not significant at an alpha level.

To examine the probability of nicotine dependence changing in the presence of feeling depressed, or # of best friends smoking cigarettes using the odd ratio.The odd ratio for nicotine dependence and self rated feeling of depression is less than one. Therefore the nicotine dependence will increase with or without the feeling of self rated depression.The odd ratio for nicotine dependence and best friends smoking is greater than one. Therefore as the number of friends who smokes increases, the nicotine dependency will likely to increase.However, since none of the odd ratios are significant, I am unable to make the following statements on the probability of explanatory variable and response variable. “The adolescents with best friends smoking cigarettes is 1.486 times more likely to be nicotine dependence with 95% confidence of this probability falling between 0.963 and 2.293 for new sample data.”

Since none of the regression analysis were statistically significance, there ere no confounders either.

/* ***************************************** */

/* SAS code */

/* ***************************************** */

/* Student Name: Rajeev.S Assignment 03 File: c3_assign4-v05a.sas Date: 2016-01-25 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

LABEL H1GI20 = "Adolecent Age" H1TO2 = "Introductory Age to Cigarette" /*1 to 20*/ H1TO11 = "Introductory age to Chewing Tobacco" /*1 to 18*/ H1TO14 = "Introductory Age to Alcohol" /*1 to 19*/ H1TO30 = "Introductory Age to Marijuana" /*1 to 18*/ H1TO34 = "Introductory age to Cocaine" /*1 to 18*/ H1TO37 = "Introductory Age to Inhalant" /*1 to 18*/ H1TO40 = "Introductory Age to Illegal Drugs" /*1 to 18*/

H1TO32 = "Number of Marijuana smoked per month" H1TO36 = "Number of times Cocaine used in a month" H1TO39 = "Number of times Inhalants used in a month" H1TO42 = "Number of times Illegal drugs used in a month" H1TO7 = "Number of Cigarettes smoked in a day, (within past 30 days)" H1TO16 = "Number of Drinks per Each Drink" H1GI8="Which one category best describes your racial background?" H1FS6="You felt depressed in the last week" H1FS16="You felt sad in the last week" H1FS5="You had trouble keeping your mind on what you were doing in the last week." H1TO9 = "# of best friends, how many smoke at least 1 cigarette a day?" H1TO33 = "# of best friends, smoked marijuana at least 1 a day?" H1TO3 = "Have you ever smoked cigarettes regularly, that is, at least 1 cigarette every day for 30 days?" ;

/*Missing Values: Racial Background 6-Refused; 7-Legitimate Skip; 8-don’t know; 9-Not Applicable*/ if H1GI8 > 5 THEN H1GI8 = .; if H1TO3 > 1 then H1TO3 = .; /* Tried smoking everyday */

if H1TO9 > 3 then H1TO9 = .; if H1TO33 > 3 then H1TO33 = .;

IF H1FS5 > 3 THEN H1FS5 =. ; IF H1FS6 > 3 THEN H1FS6 =. ; IF H1FS16 > 3 THEN H1FS16 =. ;

/*Introductory age*/ IF H1TO2 = 0 or H1TO2 > 20 THEN H1TO2 = .; IF H1TO11 = 0 OR H1TO11 > 18 OR H1TO11 = '.' THEN H1T011 = .; IF H1TO14 > 19 THEN H1T014 = .; IF H1TO30 = 0 OR H1TO30 > 18 THEN H1T030 = .; IF H1TO34 = 0 OR H1TO34 > 18 THEN H1T034 = .; IF H1TO37 = 0 OR H1TO37 > 18 OR H1TO37 = '.' THEN H1TO37 = .; IF H1TO40 = 0 OR H1TO40 > 18 OR H1TO40 = '.' THEN H1TO40 = .;

/*# of Cig that's smoked in the past 30 days*/ if H1TO7 = 0 or H1TO7 > 95 THEN H1TO7 = .; IF H1TO16 => 90 THEN H1TO16 = .; if H1TO32 = 0 OR H1TO32 > 800 THEN H1TO32 = .; IF H1TO36 = 0 OR H1TO36 > 33 THEN H1TO36 = .; IF H1TO39 = 0 OR H1TO39 > 789 OR H1TO39 = '.' THEN H1TO39 = .; IF H1TO42 = 0 OR H1TO42 > 132 OR H1TO42 = '.' THEN H1TO42 = .;

/*Adolecent Age*/ IF H1GI20 = 96 OR H1GI20 = 97 OR H1GI20 = 98 OR H1GI20 = 99 THEN H1GI20 =.;

*Find the means to center Introductory Age to Tobacco, alcohol; PROC MEANS; VAR H1TO2 H1TO11 H1TO14 H1TO30 H1TO34 H1TO37 H1TO40 H1TO7 H1TO16 H1TO32 H1TO36 H1TO39 ; RUN;

*Centering: - Introductory age to Tobacco - Introductory age to alcohol; data new2; set new; IntroAgeCigarette = H1TO2 - 12.8609587; IntroAgeTobacco = H1TO11 - 91.3552753; IntroAgeAlcohol = H1TO14 - 64.3797663; IntroAgeMarijuana = H1TO30 - 5.1107011; IntroAgeCocaine = H1TO34 - 1.9412669; IntroAgeInhalant = H1TO37 - 12.0000000; IntroAgeIllegalDrugs = H1TO40 - 14.1563126; NumDrinkPerOccasion = H1TO16 - 5.3429454; NumMarijuanaSmokedMonthly = H1TO32 - 14.4137515; NumCocainUsedMonthly = H1TO36 - 7.0379747; NumInhalantUsedMontly = H1TO39 - 14.0714286; NumIllegalDrugsUsedMonthly = H1TO42 - 9.6756757; run;

/*Feeling Depressed*/ PROC GLM; class H1FS6 (ref="0"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1FS6 /solution; run;

/*Feeling Depressed*/ PROC GLM; class H1FS6 (ref="1"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1FS6 /solution; run;

/*Feeling Depressed*/ PROC GLM; class H1FS6 (ref="2"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1FS6 /solution; run;

/*Feeling Depressed*/ PROC GLM; class H1FS6 (ref="3"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1FS6 /solution; run;

/*# of Best friends smoking atleast 1 cigeratte a day*/ PROC GLM; class H1TO9 (ref="0"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1TO9 /solution; run;

/*# of Best friends smoking atleast 1 cigeratte a day*/ PROC GLM; class H1TO9 (ref="1"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1TO9 /solution; run;

/*# of Best friends smoking atleast 1 cigeratte a day*/ PROC GLM; class H1TO9 (ref="2"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1TO9 /solution; run;

/*# of Best friends smoking atleast 1 cigeratte a day*/ PROC GLM; class H1TO9 (ref="3"); model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion H1TO9 /solution; run;

PROC GLM; model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion /solution; run;

PROC LOGISTIC DESCENDING; MODEL H1TO3 = H1FS6 H1TO9 H1TO33 H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion ;

#sas rajeev299 linear regression

Regression Modeling in Practice - Assign3: Test a Multiple Regression Model

Hypothesis For this assignment I would like to look into the following hypothesis/research question. An individual’s amount of cigarette smoking(nicotine dependence) is associated with the individual’s age on when the very first cigarette was introduced/smoked. Nicotine dependence could also be associated with the introductory age to other form of substance/drugs. (Eg. First time smoke Marijuana, First time used cocaine, First time used Alcohol and etc).

Identifying the explanatory variables that could affect the nicotine independence:

Introductory Age to Cigarette, Introductory age to marijuana have a parameter estimate in negative association with a significant P Value.

Introductory age to Illegal drugs has a parameter estimate in positive with a significant P Value.

Nicotine Dependence is negatively associated with introductory age to smoking after controlling introductory age to marijuana and illegal drugs. (Negative Relationship)

AND

Nicotine Dependence is negatively associated with introductory age to marijuana after controlling introductory age to cigarette and illegal drugs. (Negative Relationship)

AND

Nicotine Dependence is positively associated with introductory age to illegal drugs after controlling introductory age to cigarette and marijuana.

Studying the Confidence Interval of the explanatory variables that are associated with the response variable (nicotine dependence).

Individual's with younger introductory age to cigarette on average will have -1.192 nicotine dependence. As per the confidence interval, the introductory age to cigarette will have nicotine dependence between -1.896 and -0.4886, if a different sample is selected from the population.

Individual's with younger introductory age to marijuana on average will have -1.248 nicotine dependence. As per the confidence interval, the introductory age to marijuana will have nicotine dependence between -1.979 and -0.516 , if a different sample is selected from the population.

Individual's with introductory age to illegal drug on average will have 1.247 nicotine dependence. As per the confidence interval, the introductory age to illegal drugs will have nicotine dependence between 0.237 and 2.256, if a different sample is selected from the population.

Evaluating the fit of the model for the curve linear plot:

The curve linear plot of the quadratic term is not statistically significant.

Q-Q Plot

I using the Q-Q plot to evaluate the assumption that the residuals from the regression model are normally distributed. From the graph below the dots deviates at the higher and lower quantiles. This is an indication that the model's estimated residuals do not follow a perfect normal distribution. From this, it can be concluded that the linear association that we observed in the scatter plot may not be fully estimated by the linear term.

Standardized Residual

For standard normal distribution, the residuals plots should fall between 2 standard deviations for more than 95% (from the observation). There are 5 extreme outliers which fall above 3 standard deviations. The residuals are greater than the absolute value of 2.5. This is enough evidence that the level of error within the model is unacceptable. In other words this models a fairly poor fit to the observed data.

Therefore this hints that I should be including more explanatory variable to explain the variably in order to improve the fit of this model.

The leverage Plot:

Using the leverage plot, I can identify the observations that unusually have large impact on the estimation of the predicted value of the response variable (nicotine dependence) or their outlier.

This graph can show us by how much the predicted score will differ in the observation. In this case the outliers (red dots) are close to zero leverage values which means they don't have strong influence on the estimation of the regression parameters. The circled observation in the graph has a high leverage but it s not an outlier.

/****************************************************************/

/* CODE */

/* Student Name: Rajeev.S Assignment 03 File: c3_assign3-v06.sas Date: 2016-01-25 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

/*Adolecent Age*/ IF H1GI20 = 96 OR H1GI20 = 97 OR H1GI20 = 98 OR H1GI20 = 99 THEN H1GI20 =.;

*Find the means to center Introductory Age to Tobacco, alcohol; PROC MEANS; VAR H1TO2 H1TO11 H1TO14 H1TO30 H1TO34 H1TO37 H1TO40 H1TO7 H1TO16 H1TO32 H1TO36 H1TO39 ; RUN;

* Multiple regression model with centered introductory variables introductory ages to any from drugs; PROC GLM; model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion /solution; run;

* Multiple regression model with centered introductory variables introductory ages to any from drugs wtih confidence intervals; PROC GLM; model H1TO7 = H1TO2 IntroAgeAlcohol IntroAgeTobacco IntroAgeMarijuana IntroAgeCocaine IntroAgeInhalant IntroAgeIllegalDrugs NumDrinkPerOccasion /solution clparm; run;

*Scatterplot with linear regression line; proc sgplot; reg x=H1TO2 y= H1TO7 / lineattrs=(color=blue thickness=2) clm; yaxis label ="# of Cigarettes Smoked in last 30 days"; xaxis label ="Introductory age to Cigarette"; run;

*Scatterplot with linear regression line; proc sgplot; reg x=H1TO2 y= H1TO7 / lineattrs=(color=blue thickness=2) degree=1 clm; reg x=H1TO2 y= H1TO7 / lineattrs=(color=green thickness=2) degree=2 clm; yaxis label ="# of Cigarettes Smoked in last 30 days"; xaxis label ="Introductory age to Cigarette"; run;

*The Linear regression Model; proc glm; model H1TO7= IntroAgeCigarette / solution clparm; run;

*The Quadratice Regression term; proc glm; model H1TO7 = IntroAgeCigarette IntroAgeCigarette*IntroAgeCigarette /solution clparm; run;

*Evaluating Model Fit; Proc glm plots (unpack)=all; model H1TO7= IntroAgeCigarette IntroAgeMarijuana IntroAgeIllegalDrugs/SOLUTION CLPARM; OUTPUT RESIDUAL=RES STUDENT=STDRES OUT=RESULTS; RUN;

*Evaluating the Residual's Stardard deviation; proc gplot; label stdres ="standarized residual" H1GI20="Addolecent age"; plot stdres*H1GI20/vref=0; run;

/* END*/

/****************************************************************/

OUTPUT

#sas #rajeev #rajeev299

Regression Modeling in Practice: Assign-02 - Test a Basic Linear Regression Model

Hypothesis For this assignment I would like to look into the following hypothesis/research question. An individual’s amount of cigarette smoking is associated with the individual’s age on when the very first cigarette was introduced/smoked.

Variables of my Analysis: Explanatory Variable (Quantitative): Age to the introduction of the 1st Cigarette Response Variable (Quantitative): Number of cigarettes smokes within the last 30 days

Here is my output of my Code:

Discussion:

The F Statistics is 60.34 and the P value is <0.001, so we can reject the null hypothesis and it can be concluded that the introduction age to cigarette is significantly associated with nicotine dependence (the number cigarettes smoked). Beta Sub one value = -0.59926312 Beta Sub Zero value = 14.81884194 By examining the scatter plot there is only one outlier (highlighted in yellow).

Equation: Nicotine Dependence = 14.81884194 - 0.59926312 (introduction age to cigarette)

/* CODE: */

/* Student Name: Rajeev.S Assignment 02 File: c3_assign2-v03.sas Date: 2015-12-21 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

LABEL H1TO2 = "Age of introduction to the first Cigeratte" H1TO7 = "Number of Cigarettes smoked in a day, (within past 30 days)";

/*Intro to Smoking age*/ IF H1TO2 = 0 or H1TO2 > 20 THEN H1TO2 = .;

/*# of Cig that's smoked in the past 30 days*/ if H1TO7 = 0 or H1TO7 > 95 THEN H1TO7 = .;

PROC GLM; MODEL H1TO7=H1TO2;

RUN;

#rajeev299 sas LinearRegression

Regression Modeling in Practice: Assign-01 - Writing about your Data

Step 1: Describe your sample. Provide enough detail so that your reader can clearly understand the population that the study sample came from. Use meaningful labels. Do not use abbreviations (“PPM100”) or variable names.

a) Describe the study population (who or what was studied).

The sample population is students from high school in United States of America. The focus of this study was on student adolescents. The data is collected in 5 waves from the same sample set and each wave indicates a maturing phase of the individual.

b) Report the level of analysis studied (individual, group, or aggregate).

Each adolescents student was studied individually through self-reporting and through interview.

c) Report the number of observations in the data set. There were 6504 adolescent students participated in the study.

d) Describe your data analytic sample (the sample you are using for your analyses). The following data elements are potential candidates for my research. Each data elements were part of the wave 1 research and it was collected via in-home interview.

From section 2 under daily activities the following data elements attribute to the explanatory variable.

H1DA1 (House Work/Chores) H1DA2 {Hobbies} H1DA3 {TV/Video Games} H1DA4 {Physical Activity -> biking…roller blading} H1DA5 {Active Sports -> Soccer, baseball} H1DA6 {Exercise}

From section 10 under feeling scale the followingH1FS1 {bothered by things that usually don’t bother you.}

H1FS4 {feel good as other people} H1FS6 {feel Depressed} H1FS7 {Feel too tired} H1FS9 {feel life is a failure} H1FS11 {feel happy} H1FS14 {feel people were unfriendly to you} H1FS15 {enjoyed life} H1FS16 {feel sad} H1FS17 {felt that people disliked you} H1FS18 {was hard to get started doing things} H1FS19 {felt life was not worth living}

Step 2: Describe the procedures that were used to collect the data.

a) Report the study design that generated that data (for example: data reporting, surveys, observation, experiment). The study design that was used to generate the data was survey (questionnaires) filled in by an In-Home interviewer.

b) Describe the original purpose of the data collection. The purpose of this longitudinal data collection is to study the environmental impacts and the outcome of the adolescent students.

c) Describe how the data were collected. The data was collected by an interviewer. He recorded the answers of the response of the respondents

d) Report when the data were collected. The wave1 Data was collected between 1994 and 1977. This wave1 holds data of my research interest.

e) Report where the data were collected? The wave1 data was collected via home interview from a fixed set of questions.

Step 3: Describe your variables.

a) Describe what your explanatory and response variables measured. The list below are the explanatory and response variable as potential candidates, selected for my research.

From section 2 under daily activities the following data elements are potential candidates to the explanatory variable.

H1DA1 (House Work/Chores) H1DA2 {Hobbies} H1DA3 {TV/Video Games} H1DA4 {Physical Activity -> biking…roller blading} H1DA5 {Active Sports -> Soccer, baseball} H1DA6 {Exercise}

From section 10 under feeling scale the following data elements are potential candidates for reponse variable.

H1FS1 {bothered by things that usually don’t bother you.} H1FS4 {feel good as other people} H1FS6 {feel Depressed} H1FS7 {Feel too tired} H1FS9 {feel life is a failure} H1FS11 {feel happy} H1FS14 {feel people were unfriendly to you} H1FS15 {enjoyed life} H1FS16 {feel sad} H1FS17 {felt that people disliked you} H1FS18 {was hard to get started doing things} H1FS19 {felt life was not worth living}

b) Describe the response scales for your explanatory and response variables. The explanatory and the response variables are both categorical.

c) Describe how you managed your explanatory and response variables. Since, all the selected data elements for explanatory and response variables are categorical, the survey had 4 options to indicate their degree or frequency or measure as a response. There were two more options provided for the respondent to refuse to answer or to claim unawareness. For the latter two options, I used it as missing data.

#sas rajeev299 data adolescents

Data Analysis Tools: Assign-04 - ANOVA and a Potential Moderator

For this assignment I would like to look into the following hypothesis/research question. An individual’s amount of cigarette smoking is associated with the individual’s age on when the very first cigarette was introduced/smoked. In addition, the individual’s amount of cigarette smoking is moderated by the number of best friends who are also smokers.

Variables of my Analysis: Independent Categorical Variable: Age to the introduction of the 1st Cigarette (20 Levels) Dependent Continuous Variable: Number of cigarettes smokes within the last 30 days Moderating Categorical Variable: Number of friends who are smokers (3 levels)

Please note that Anova test on introduction to 1st Cigarette vs the number of cigarettes smoked had a significance difference. However, when I conducted the post hoc Duncan test it turned out only 3 groups were significantly difference. I am not sure if Duncan result has any relevance as indicator in proceeding with the testing of significance in the moderation variable (please feel free to comment on this). For the sake of this assignment, I am going to proceed with the testing significance of the moderation variable.

The Image Below is the summary of the ANOVA analysis. From ANOVA analysis, there was a significant difference for each of the moderating group.

The below image/table is created by consolidating the means of each group with the moderating variable resulted from ANOVA analysis. I have selected few age groups from my independent variable by highlighting them to demonstrate the impact of the moderating variable. The age group is broken down further by the moderating group levels. It clearly shows an increase in the number of cigarettes smoked as the number of friends who smokes increases.

Therefore I conclude that number of friends who smokes could be a potential moderator.

-------------

-- CODE

-------------

/* Student Name: Rajeev.S Assignment 04 File: c2_assign4-v06.sas Date: 2015-11-29 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

LABEL H1TO2 = "Age of introduction to the first Cigeratte" H1TO7 = "Number of Cigarettes smoked in a day, (within past 30 days)" H1TO9 = "Number of Best friends smoke atleast 1 cigerate a day " ;

/*Intro to Smoking age*/ IF H1TO2 = 0 or H1TO2 > 20 THEN H1TO2 = .;

/*# of Cig that's smoked in the past 30 days*/ if H1TO7 = 0 or H1TO7 > 95 THEN H1TO7 = .;

/*# of best friends smoke cigarette a day*/ if H1TO9 > 5 OR H1TO9 = "." OR H1TO9 = '.' THEN H1TO9 = .;

PROC SORT; BY H1TO9;

/* # of Cigarettes smoked is associated with age of when the first cigarette WAS introduced. */ /* It is moderated by number of smoking friends. */

PROC ANOVA; CLASS H1TO2; MODEL H1TO7 = H1TO2; MEANS H1TO2/DUNCAN; PROC ANOVA; CLASS H1TO2; MODEL H1TO7 = H1TO2; MEANS H1TO2; BY H1TO9; RUN;

#SAS Moderating Variable ANOVA Analysis Data Analysis Rajeev299

Data Analysis Tools: Assign-03 - Pearson correlation coefficient test

My initial research is to study the association between self rated mental health and the physical activities. All the data elements related to my research is categorical. Subsequently for the sake of this assignment I would like to change my research question to the following.

Research Question: The association between age and the hours spent listening to radio.

Quantitative Variables to study the associations: - Year or Birth - Hours spent Listening to Radio

From the scatter plot and from the bar graph I see a negative linear relationship. I get an R Value of -0.11849 and p-value of 0.0001 from the Pearson correlation coefficient test . My R-value indicates a very week negative relationship even though my p-value is less than 0.05. In order for the association to be significant the p-value has to be less than 0.05 and R has to closer to -1 or +1. Hence, this test indicates that the observation is due chance or luck. (Check out this link for more info on my type of finding ) .

(Please note: Line of Best Fit was drawn using paint, just for analysis purposes.)

/* Student Name: Rajeev.S Assignment 03 File: c2_assign3-v02.sas Date: 2015-11-22 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

LABEL H1DA11="Hours Listening to Radio" H1GI1Y="Year of Birth" H1DA8="Hours Watching Television" ; /* Missing value: 6:Refused; 8:Don't Know */ IF H1DA11 > 99 OR H1DA11 = 0 THEN H1DA11 =.; IF H1DA8 > 99 OR H1DA8 = 0 THEN H1DA8 =.;

PROC FREQ; TABLES H1DA11 H1DA8 H1GI1Y;

PROC SGPLOT; SCATTER X=H1GI1Y Y=H1DA11; PROC SGPLOT; SCATTER X=H1GI1Y Y=H1DA8; PROC CORR; VAR H1DA11 H1DA8 H1GI1Y;

PROC GCHART; VBAR H1GI1Y/DISCRETE TYPE=MEAN SUMVAR= H1DA11 ; PROC GCHART; VBAR H1GI1Y/DISCRETE TYPE=MEAN SUMVAR= H1DA8;

RUN;

/*The output from the following code is what I will be using for the dicussion.*/ DATA newset; SET NEW;

IF H1GI1Y <= 75 THEN H1GI1Y =.;

PROC CORR; VAR H1DA11 H1GI1Y;

PROC GCHART; VBAR H1GI1Y/DISCRETE TYPE=MEAN SUMVAR= H1DA11; PROC SGPLOT; SCATTER X=H1GI1Y Y=H1DA11; RUN;

#pearson correlation coefficient #pearson correlation example SAS

Data Analysis Tools: Assign-02 Chi-square test

My research is related to the association between physical activities and self rated mental health. Based on the AddHealth data set, I hypothesize that there is an association between exercising and feeling depressed.

Explanatory variable is the frequency of exercising in the past week, and as follows the frequency of feeling depressed in the past week will be the response variable. These two variables are categorical with 4 groups.

Null Hypothesis: There is no association between exercising and feeling depressed. Alternate Hypothesis: There is association between exercising and feeling depressed.

A 4X4 chi square test is performed to determine if there is a significant association between my two variables. The P value from 4X4 chi square test is 0.2966 which is greater than the significance level which is 0.05. This is sufficient to accept my null hypothesis, because the observed values are significantly different from the expected values.

For the practice of this assignment, I will pretend that my P value < 0.05 and try to avoid Type 1 Error. Therefore, I will compare 6 pairs of groups to examine difference in the significance level per pair. The difference in the significance level should be less than 0.0083, following the Bonferroni adjustments.

Significance Level: 0.05 Total Number of Comparison = 6 Bonferroni Adjustment => 0.05 / 6 = 0.0083;

From the Chi Square Statistics: P Value of Group 0 & 1 = 0.88 P Value of Group 0 & 2 = 0.27 P Value of Group 0 & 3 = 0.76 P Value of Group 1 & 2 = 0.02 P Value of Group 1 & 3 = 0.38 P Value of Group 2 & 3 = 0.42

For all the comparison, the observed values are significantly different from the adjusted significance level of 0.0083. Therefore, I should accept my Null hypothesis.

----------------------- ----------------------- -----------------------

CODE used to Generate the above graphs ----------------------- ----------------------- -----------------------

/* Student Name: Rajeev.S Assignment 01 File: c2_assign2-v04.sas Date: 2015-11-14 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

LABEL H1DA6="Frequency of exercise in the past week" H1FS6="Frequency of feeling depressed in the past week";

/*For my research, I am only interested in Adolecents who are aged from 13 to 18. There is no reference as to when the survey was conducted. Therefore I am going to assume grade 9 to grade 12 as adolcents*/

/*The missing Values*/ /* IF H1GI20 = 7 OR H1GI20 = 8 OR H1GI20 = 96 OR H1GI20 = 97 OR H1GI20 = 98 OR H1GI20 = 99 THEN H1GI20 =.; */ /* Missing value: 6:Refused; 8:Don't Know */ IF H1FS6 = 6 OR H1FS6 = 8 THEN H1FS6=.; IF H1DA6 = 6 OR H1DA6 = 8 THEN H1DA6=.;

PROC FORMAT; VALUE H1DAf 0="Not at all" 1="1 or 2 times" 2="3 or 4 times" 3="5 or more times" 6="Refused" 8="Unsure";

VALUE H1FSF 0="Never or rarely" 1="Sometimes" 2="A lot of time" 3="Most of the time" 6="Refused" 8="Unsure";

PROC FREQ; TABLES H1FS6 H1DA6; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

DATA COMP1; SET NEW; IF H1DA6 = 0 or H1DA6 = 1; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

DATA COMP2; SET NEW; IF H1DA6 = 0 or H1DA6 = 2; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

DATA COMP3; SET NEW; IF H1DA6 = 0 or H1DA6 = 3; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

DATA COMP4; SET NEW; IF H1DA6 = 1 or H1DA6 = 2; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

DATA COMP5; SET NEW; IF H1DA6 = 1 or H1DA6 = 3; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

DATA COMP6; SET NEW; IF H1DA6 = 2 or H1DA6 = 3; PROC FREQ; FORMAT H1DA6 H1DAF. H1FS6 H1FSF.; TABLES H1FS6*H1DA6/CHISQ; RUN;

Data Analysis Tools: Assign01 - Data Running an analysis of variance

My previous research question does not involve any quantitative variables, and consequently I am changing my research question for this assignment to the following.

"There is a difference in racial background and the amount of hours spent watching television among adolescents."

My Explanatory variable is H1GI8: Racial Background which consists of 5 groups.

-1 White -2 Black or African American -3 American Indian or Native American -4 Asian or Pacific Islander -5 Other

My Reponses Variable is H1DA8: Amount of hours watching television per week.

Null Hypothesis: There is no association in racial background and the amount of hours spent watching television among adolescents.

Alternate Hypothesis: There are association in racial background and the amount of hours spent watching television among adolescents.

My P-value is 0.66m which is greater than 0.5 which indicates my null hypothesis can be rejected. There is no association in racial background and the amount of hours spent watching television among adolescents. Therefore, I accept the null hypothesis.

For practice of this assignment, the Duncan test was conducted (the Post Hoc tests for ANOVA). By conducting the Duncan test, there is only one group which indicates that none of the groups are significantly difference.

/*Code Used to Generate Graphs and charts above*/

/* Student Name: Rajeev.S Assignment 01 File: c2_assign1-v03.sas Date: 2015-11-09 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

PA = .; SRMH =.;

LABEL H1GI8="Which one category best describes your racial background?" H1DA11="Hours listening to radio" H1DA8="How many hours a week do you watch television"

;/*For my research, I am only interested in Adolescents who are aged from 13 to 18. There is no reference as to when the survey was conducted. Therefore I am going to use the grades to filter the adolescent which is from grade 9 to grade 12*/

/*The missing Values*/ IF H1GI20 = 7 OR H1GI20 = 8 OR H1GI20 = 96 OR H1GI20 = 97 OR H1GI20 = 98 OR H1GI20 = 99 THEN H1GI20 =.;

/*Missing Values: Racial Background 6-Refused; 7-Legitimate Skip; 8-don't know; 9-Not Applicable*/ if H1GI8 > 5 THEN H1GI8 =. ;

/* Missing value: Watching TV {996=Refused; 998=Don't know; 0=Don't Watch; */ IF H1DA8 = 996 OR H1DA8 = 998 OR H1DA8 = 0 THEN H1DA8 =.;PROC ANOVA; CLASS H1GI8;

MODEL H1DA8 = H1GI8; MEANS H1GI8/DUNCAN;

RUN;

Assignment 04 - Creating graphs for your data in SAS (Leisure Activity vs Depression Level)

For my initial research, I hypothesized that adolescents doing physical activities will have a positive influence on self-rated mental health.

For this assignment, I have used the variables self-rated depression feeling (H1FS6) and the self-rated leisure activity level (H1DA4) such as roller-skating, skate-boarding and etc for the analysis.

Self rated depression is one measure out of many that contributes to a self-rated mental health and leisure activity is one of many measure to rate the physical activity level of an individual.

Leisure activity is the explanatory or independent variable and the depression feeling will be my response variable. The following are the two categorical variables values that I will be analyzing in this assignment.

Category - Independent Variable –Leisure activity’s rating value

0 - not at all

1 - 1 or 2 times

2 - 3 or 4 times

3 - 5 or more times

Category Response Variable – Depression feeling’s rating value

0 - Never or rarely

1 - Sometimes

2 - A lot of the time

3 - Most of the time or all of the time

Graph: Physical activity

Graph: Depression Feeling

The physical activity and the self-rated mental health measure graphs are both negatively skewed. The mode for PA graph is 0 which indicates that most of the adolescents were never involved in any leisure physical activity. The mode for the depression level is also zero, which indicates that the adolescents never felt depressed.

Graph: Depressive feeling vs Physical activity

Adolescents who never did leisure activity felt depressed 57% frequently depressed. Adolescents who did 1 or 2 times leisure activity a week felt 50% frequently depressed. Adolescents who did 3 or 4 times of leisure activity a week felt 53% frequently depressed and the adolescents who 5 or more times leisure activity felt 45% frequently depressed.

---------------- SAS code ----------------

/* Student Name: Rajeev.S Assignment 04 File: assign3-v04.sas Date: 2015-10-25 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

PA = .; SRMH =.;

LABEL H1DA4="Frequency of doing leisure activities (walking, roller blading, skating..) in the past week?" H1FS6="You felt depressed in the last week" H1FS6_F ="Frequency of depression feeling in the last week"; ;

/*For my research, I am only interested in Adolecents who are aged from 13 to 18. There is no reference as to when the survey was conducted. Therefore I am going to use the grades to filter the adolecent which is from grade 9 to grade 12*/

/*The missing Values*/ IF H1GI20 = 7 OR H1GI20 = 8 OR H1GI20 = 96 OR H1GI20 = 97 OR H1GI20 = 98 OR H1GI20 = 99 THEN H1GI20 =.;

IF H1GI20>=9 AND H1GI20<= 12;

/* For PHYSICAL ACTIVITY VARIABLE & Self Rated Mental Health varaiables, I am going to filter out data where the respondent has refused or was unsure. For my research, I am only interested in the data who have given a definite answer to the survey questions.

FILTERED OUT MISSING DATA: 6 refused; 8 don’t know; */

IF H1DA4 LE 4; IF H1FS6 LE 4;

IF H1FS6=0 THEN H1FS6_F = 3; ELSE IF H1FS6=1 THEN H1FS6_F = 2; ELSE IF H1FS6=2 THEN H1FS6_F = 1; ELSE IF H1FS6=3 THEN H1FS6_F = 0;

/* PROC FREQ; TABLES H1DA4 H1FS6 H1FS6_F; PROC UNIVARIATE; VAR H1DA4; PROC UNIVARIATE; VAR H1FS6; */ PROC GCHART; VBAR H1DA4/Discrete TYPE=PCT; PROC GCHART; VBAR H1FS6/Discrete TYPE=PCT;

PROC GCHART; VBAR H1DA4/DISCRETE TYPE=MEAN SUMVAR= H1FS6;

/* PROC GCHART; VBAR H1DA4/DISCRETE TYPE=MEAN SUMVAR= H1FS6_F; */ RUN;

Assignment03: Making Data Management Decisions

SUMMARY

For my research, I hypothesized that adolescents doing physical activities will have a positive influence on self-rated mental health.

Since my project only involved adolescents, the age group has to be from 13 to 17. Since there is no age available, I will be deriving it from the grades or class that is studying in. I am assuming that students from grade 9 to 12 should fit the adolescent age group.

To measure physical activities, From adolescent health code book there are individual variables for house work (H1DA1), leisure physical activity(H1DA4), active sports(H1DA5) and exercise(H1DA6). The same frequency scale has been used for all the indicated variables. Therefore, for my research, the secondary variable physical activity is derived by combining (adding and averaging) the values of the individual associated variables.

To measure the self-rated mental health, this secondary variable need to be derived by combining the variables from the survey such as feeling bothered by things, poor appetite, unable to shake of blues, trouble focusing, depression, feeling tiered, feeling life as a failure, feeling feared, feeling lonely, feeling sad, feeling life was not worth it and feeling that people dislike the respondent.

FREQUENCY CHARTS

CODE

/* -------- --------------------------------------- --------------------------------------- */

/* Student Name: Rajeev.S Assignment 03 File: assign3-v02.sas Date: 2015-10-18 */

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/ DATA new; set mydata.addhealth_pds;

PA = .; SRMH =.;

LABEL H1DA1="Frequency of doing house work (chores) in the last week" H1DA4="Frequency of doing leisure activities (walking, jogging, gym..) in the past week?" H1DA5="Frequency of playing active sports (baseball, soccer, softball..) in the past week?" H1DA6="Frequency of doing exerecise in the last week"

H1FS1="You were bothered by things in the last week" H1FS2="You didn’t feel like eating (poor apetite) in the last week" H1FS3= "Unable to shake off the blues, even with help from your family and your friends in the last week." H1FS5="You had trouble keeping your mind on what you were doing in the last week." H1FS6="You felt depressed in the last week" H1FS7="You felt that you were too tired to do things in the last week" H1FS9="You thought your life had been a failure." H1FS10="You felt fearful in the last week" H1FS13="You felt lonely in the last week" H1FS16="You felt sad in the last week " H1FS17="You felt that people disliked you in the last week" H1FS19="You felt life was not worth living in the last week" PA="Derived variable - Frequency of doing Physcial activities" SRMH="Derived variable - Degree of feelling towards Self Rated Mental Health" ;

/*The missing Values*/ IF H1GI20 = 7 OR H1GI20 = 8 OR H1GI20 = 96 OR H1GI20 = 97 OR H1GI20 = 98 OR H1GI20 = 99 THEN H1GI20 =.;

IF H1GI20>=9 AND H1GI20<= 12;

FILTERED OUT DATA: 6 refused; 8 don’t know; */

/*8 - is unknown.*/

IF H1DA1 LE 4; IF H1DA4 LE 4; IF H1DA5 LE 4; IF H1DA6 LE 4;

IF H1FS1 LE 4; IF H1FS2 LE 4; IF H1FS3 LE 4; IF H1FS5 LE 4; IF H1FS6 LE 4; IF H1FS7 LE 4; IF H1FS9 LE 4; IF H1FS10 LE 4; IF H1FS13 LE 4; IF H1FS16 LE 4; IF H1FS17 LE 4; IF H1FS19 LE 4;

PA_GROUP = .;

PA = (H1DA1 + H1DA4 + H1DA5 + H1DA6)/4; SRMH = (H1FS1 + H1FS2 + H1FS3 + H1FS5 + H1FS6 + H1FS7 + H1FS9 + H1FS10 + H1FS13 + H1FS16 + H1FS17 + H1FS19)/12;

PROC FREQ; TABLES H1GI20 PA SRMH;

RUN;

/* -------- --------------------------------------- --------------------------------------- */

/* END OF PROGRAM */

/* -------- --------------------------------------- --------------------------------------- */

Assignment 02: Running Your first program SAS Frequency Distribution

#1: program

/* -------------------------------------------------------------------------- */ /*--------------------------------------------------------------------------*/

/* Student: Rajeev.s Assignment 02 File: assign2-v04.sas Date: 2015-10-11

/*Indicates where the data resides*/

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;

/* Addolecents Health Data Sets*/

DATA new; set mydata.addhealth_pds;

LABEL

H1DA6="How many times did the adolescent do exercise (walking, jogging, gym..) in the past week?"

H1FS6="In general to what degree did the adolescent feel depressed in the past week?"

H1FS7="In general to what degree did the adolescent feel too tiered in the past week?"

;

/* For PHYSICAL ACTIVITIE VARIABLE, I am going to filter out data where the respondent has refused or was unsure about the physical activities.For my research, I am only interested in the data who have given a definite answer to the survey questions related to the physical activities.

FILTERED OUT DATA: 6 refused; 8 don’t know; */

In my data set, the feeling section is an indicator of Self Rated Mental Health. For FEELINGS VARIABLE, I am going to filter out data where the respondent has refused or was unsure about on how they feel. For my research, I am only interested in the data who have given a definite answer to the survey questions related to how they feel. FILTERED OUT DATA: 6 refused; 8 don’t know; */

IF H1DA6>=0 and H1DA6<=3;

IF H1FS6>=0 and H1FS6<=3;

IF H1FS7>=0 and H1FS7<=3;

/* For my research, I am only interested in Adolescents who are aged from 13 to 18. There is no reference as to when the survey was conducted. Therefore I am going to use the grades to filter the adolescent which is from grade 9 to grade 12*/

IF H1GI20>=9 AND H1GI20<= 12;

PROC FORMAT;

VALUE H1DAf 0="Not at all"

1="1 or 2 times"

2="3 or 4 times"

3="5 or more times";

VALUE H1FSF

0="Never or rarely"

1="Sometimes"

2="A lot of time"

3="Most of the time or all the time";

PROC FREQ; FORMAT

H1DA6 H1DAF.

H1FS6 H1FSF.

H1FS7 H1FSF.;

TABLES H1DA6

H1FS6 H1FS7 ;

RUN;

/* -------------------------------------------------------------------------- */ /* End of Program */ /*--------------------------------------------------------------------------*/

#2 The output that displays three of your variables as frequency tables

Please click on the link to view the image for the frequency distribution generated by the above code.

http://datamgmtandvisualization.tumblr.com/image/130988935850

#3 Few sentences describing your frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.

I have selected adolescents' frequency of exercising and the degree of feeling depressed and tirelessness as my variable.

I have filtered the sample to be of only adolescents who are in grade 9 to 12. I was unable to extrapolate age using date of birth because the surveyed date was missing, and hence I had use their grade/class level to select the adolescents.

As part of handling the missing data, I have filtered out the observations who have refused to give an answer or were unsure about giving a definite answer to the survey question. 100% of the adolescents have given a definite answer to the frequency of the exercise question. For the self rated mental health variables (feeling tiered or depressed) an accumulative of 0.5% of 4366 adolescents observation contributes to the missing data.

In addition, ~0.11 percent of 4533 contributes to the missing data in identifying the adolescents. For the self rated mental health variables (feeling tiered or depressed), with the inclusion of missing data in identifying the adolescents yields ~0.68 percent.

According to my frequency chart my sample size is 4357. However the subjects feeling depressed is ~2.5% and feeling often tired is ~3.2 percent of the sample size and the adolescents who never exercised is ~17% which is the smallest among the distribution of the frequency of exercises.

Assignment 02: The output that displays three of your variables as frequency tables

Developing a Research Question and Creating Your Personal Code Book

STEP 1: Choose a data set that you would like to work with.

National Longitudinal Study of Adolescent Health (AddHealth)

STEP 2. Identify a specific topic of interest

Does physical active lifestyle has positive influence on feelings and it is transitively has a positive influence on others perception (remarks). Physical Active Lifestyle in my view are sports, or games and exercise. Having hobbies and performing house chores that requires the individual to move.

from AddHealth codebook

Section 1: General Introductory Section 2: Daily Activities { Physical Activities} Section 3: General Health { Physical Activities}

STEP 3. Prepare a codebook of your own (i.e., print individual pages or copy screen and paste into a new document) from the larger codebook that includes the questions/items/variables that measure your selected topics.)

from AddHealth codebook

Section 1: General Introductory -> H1GI1M (Birth Date) Section 1: General Introductory -> H1GI1Y (Birth Date) Section 1: General Introductory -> H1GI19 {Schooling?} Section 1: General Introductory -> H1GI20 {Grade/Class} Section 1: General Introductory -> H1GI21 {School status} Section 2: Daily Activities -> H1DA1 (House Work/Chores) Section 2: Daily Activities -> H1DA2 {Hobbies} Section 2: Daily Activities -> H1DA3 {TV/Video Games} Section 2: Daily Activities -> H1DA4 {Physical Activity -> biking...roller blading} Section 2: Daily Activities -> H1DA5 {Active Sports -> Soccer, baseball} Section 2: Daily Activities -> H1DA6 {Exercise}

STEP 4. Identify a second topic that you would like to explore in terms of its association with your original topic.

from AddHealth codebook

Section 10: Feelings Scale

STEP 5. Add questions/items/variables documenting this second topic to your personal codebook.

from AddHealth codebook

Section 10: Feelings Scale -> H1FS1 {bothered by things that usually don’t bother you.}

Section 10: Feelings Scale -> H1FS4 {feel good as other people}

Section 10: Feelings Scale -> H1FS6 {feel Depressed}

Section 10: Feelings Scale -> H1FS7 {Feel too tired}

Section 10: Feelings Scale -> H1FS9 {feel life is a failure}

Section 10: Feelings Scale -> H1FS11 {feel happy}

Section 10: Feelings Scale -> H1FS14 {feel people were unfriendly to you}

Section 10: Feelings Scale -> H1FS15 {enjoyed life}

Section 10: Feelings Scale -> H1FS16 {feel sad}

Section 10: Feelings Scale -> H1FS17 {felt that people disliked you}

Section 10: Feelings Scale -> H1FS18 {was hard to get started doing things}

Section 10: Feelings Scale -> H1FS19 {felt life was not worth living}

STEP 6. Perform a literature review to see what research has been previously done on this topic. Use sites such as Google Scholar (http://scholar.google.com) to search for published academic work in the area(s) of interest. Try to find multiple sources, and take note of basic bibliographic information.

STEP 7. Based on your literature review, develop a hypothesis about what you believe the association might be between these topics. Be sure to integrate the specific variables you selected into the hypothesis.

Step 6 & 7

I would like to explore the physical activities of an adolescent and how they feel about themselves. The domain of the variable physical activity(PA) can be biking, roller blading, sports, exercise and also the housework (chores). The variable feeling is a measure of how the adolescents would feel about themselves on their life, depression, happiness and etc.

Based on the literature review, I have discovered the health related quality of life (HRQL) indicates how individual perceive their physical, mental and social health[4]. There are also evidenced indications that physical activity is directly related to HRQL while sedentary behaviour is inversely associated with poor mental and physical health[4].

The self rated mental health (SRMH) [2] which is how an adolescent perceive about their mental health is also strong indicator of their overall self rated health. A study indicates physical activities are related to physical self concept which is related to global self esteem; which itself is an indicator of the mental health[3].

As per my literature review, there have been numerous studies have been conducted SRMH however nothing was more specifically conducted on SRHM of adolescents and on physical active lifestyle.

Therefore I hypothesis that adolescents engaged in physical activities would rate themselves with high self rated mental health (SRMH) than the adolescents who are less engaged in physical activity.

References

1. Canadian Institute for Health Information, Improving the Health of Canadians: Exploring Positive Mental Health (Ottawa: CIHI, 2009)

2. Levinson D & Kaplan G (2014), What does self rated mental health represent. Journal of Public Health Research; volume 3:287, 122-127

3. Schmidt M, Blum M, Valkanover S, Conzelmann A (2014). Motor ability and self-esteem: The mediating role of physical self-concept and perceived social acceptance. Psychology of Sport and Exercise (17) 15-23

4. Guallar-Castillón P, López-García E, Bayán-Bravo A, Gutierrez-Fisac J, León-Muñoz L, Rodríguez-Artalejo F, et al (2014). The association of major patterns of physical activity, sedentary behavior and sleep with health-related quality of life: A cohort study. Preventive Medicine (67) 248–254

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