Data Analysis Plan

Data analysis plan
This paper proposes an analytical approach in the research involving examining the relationship
between sexual orientation, body mass index and methamphetamine use among Chicago youth.
First, the paper includes a statement of the original research questions and hypotheses. After
which, the paper is organized to include a plan on how the collected data will be analyzed. The
third and fourth section will include examination of the threats to the validity of this research and
the ethical procedures which will be observed during conducting data collection and dealing with
participants.
Research questions and Hypotheses
The following research question and hypotheses will be addressed in this study:
1. Is there any association between body mass index (independent variable) and
methamphetamine use (dependent variable) among Chicago youth?
Ho1: There is no statistically significant association between body mass index
(independent variable) and methamphetamine use (dependent variable) among
Chicago youth?
Ha1: There is a statistically significant association between body mass index (independent
variable) and methamphetamine use (dependent variable) among Chicago youth?
2. Does the association between body mass index (independent variable) and
methamphetamine use (dependent variable) differ by sexual orientation (moderating
variable)?
Ho2: The association between body mass index (independent variable) and
methamphetamine use (dependent variable) does not differ by sexual orientation
(moderating variable)?

Ha2: The association between body mass index (independent variable) and
methamphetamine use (dependent variable) differs by sexual orientation (moderating
variable)?
3. Is there an association between methamphetamine use (independent variable) and body
mass index (dependent variable) among Chicago youth?
Ho3: There is no statistically significant association between methamphetamine use
(independent variable) and body mass index (dependent variable) among Chicago youth.
Ha3: There is a statistically significant association between methamphetamine use
(independent variable) and body mass index (dependent variable) among Chicago youth.
4. Does the association between methamphetamine use (independent variable) and body
mass index (dependent variable) differ by sexual orientation (moderating variable)?
Ho2: The association between methamphetamine use (independent variable) and body
mass index (dependent variable) does not differ by sexual orientation (moderating
variable)?
Ha2: The association between methamphetamine use (independent variable) and body mass index
(dependent variable) differs by sexual orientation (moderating variable)?
Software
For the analysis of the collected data, the study will use the Statistical Package for social
sciences (SPSS) version 24.0 which was released by IBM in 2016. The analyses will include
quantitative analyses given that this is a quantitative research project. For the quantitative
analysis, the paper will: involve use of some descriptive and inferential to examine the
distribution of the data, after which multiple logistic and linear regression analyses to examine

the influence of exogenous variables such as methamphetamine and body mass weight etcetera
with an aim to address the research objective.
Data cleaning and screening
This is the process of inspecting data for any potential errors as well as taking care of them
before any data analysis is done. It generally includes examining the research’s raw data to
determine if there are any outliers and eventually dealing with any missing data and the outliers
as well through reshaping and deleting the outliers. Data cleaning which is a subsequent process
after screening helps mitigate potential problems with the data that are identified during
screening that might end up affecting the statistical results during data analysis thus influencing
the inferences and conclusions drawn by the researchers regarding the research objective. As
such, the process of data cleaning in this project will consist of examining the:
Missing data
After collection of the data, there might be a number of missing observations which arise from
non-response by the respondents towards research questions and when recording the data into the
data files. Missing data, if any, will be taken care of through data imputation. Data imputation in
SPSS includes the replacement of missing observation by the series mean i.e. the mean of the
available observations is calculated and added to the missing observations rather than excluding
the rows of missing observations.
Reshaping the data
When entering the data observations, the researcher might enter values with different shapes i.e.
enter 705 where the observations take values in a likert scale. Examining the shape of the data
will enable us to determine if there are values which do not correspond to the distribution of the

dataset i.e. outliers. In SPSS, the outliers will be examined using box and whiskers which will
inform of the location on the outlier.
Normality
Normality tests are conducted prior to statistical analysis to determine if the data is normally
distributed, if not transformations such as log transformation will be used to normalize the
dataset (Loxton, 2008).
Statistical tests
After optimally cleaning the data, quantitative data analysis will be conducted which as
mentioned in the previous subsection will include multiple logistic, linear regression and
associated tests i.e. tests of significance and t tests for difference in means.
Linear regression
A linear regression essentially examines the relationship between one exogenous variables with
one response variable i.e. Y = α 0 + α1X1 + £i where, £i are the error terms of the model, α1 is the
regression coefficient, X1 are independent variables and Y is the dependent variable that is being
predicted.
Multiple logistic regression
Since the research also includes nominal variables such as sexual orientation, a multiple logistic
regression. It is thus efficient since it is adopted in cases involving one nominal variable and two
or more measurement variables, to examine how the effect of the measurement variables on the
nominal variable (McDonald, 2015).
Chi-Square test
Further, the use Chi-Square test which is relevant when the data sample has two nominal
variables, such that each nominal variable has only two or more values. It examines if the