Data Analysis for Smoking Growth Rate using AUTOS and FTND Methods

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Running Had: DATA ANALYSIS
Data Analysis
Student Name
Institution
Date

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Introduction
Data analyses are executed using AMOS data analysis software. The data analysis is
overpowered to identify the actual treatment impact due to the bigger sample. But, 344 subjects
are proposed to produce a close-precise estimation of satisfactory scope for experimental
analysis (Lenz, 1997). Descriptive statistics are employed to categorize features of contributors
in terms of smoking-linked variables, psychosocial and speculative variables, end results and
demographics. Cronbach’s alpha is computed to evaluate the reliability of AUTOS as well as
FTND. Spearman’s rank correlation coefficients are assessed to estimate the simultaneous and
analytical rationalities of the processes (BECKER, 2000).
Variables
This analysis counts with one dependent, and one independent variable. The dependent
variable is the growth experienced by smokers, and the independent variable is the type of
smoking type; divided into two categories: FIND and AUTOS. The dependent variable will be
measured using the smoker’s growth rate for the last three years. The data will be retrieved
directly from the psychology report.
Hypotheses
The null hypothesis for this study is: smokers increase rate for the last three years using
an AUTOS method. The alternate hypothesis for this study is: smokers’ growth rate for the last
three years using an FIND method.
Ho:μOnline=μBrick and mortar
Ha:μOnline>μBrick and mortar

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Data Analysis Technique
The proposed study will follow a quantitative research model, as all the variables are
going to be quantified. The purpose of the study is to explain the relationship between the
variables, and how the samples differ from each other. To address the research question, a
descriptive method could be used to look at the numbers, and understand how the independent
variable is affecting the dependent variable (Azumi and Shirakawa, 1988).
The mean smokers is analyzed to determine if there are differences in the AUTOS method when
compared with brick and mortar. The mean for smokers will be compared using a two-sample t-
test. The level of significance will be 0.05. The null hypothesis Ho:μOnline=μBrick and mortar ,
will be examined.
Statistics t-test enhances answering the research question through the application of t-test statistic
in finding out the ap-value which shows the probability of the results by chance and if the null
hypothesis are actual (Ho:μOnline=μBrick and mortar) (Chen and Giles, 2008). If there is less
than 5% (p-value is less than 0.05) chance of getting the observed difference
(Ha:μOnline>μBrick and mortar), the null hypothesis is rejected, and it can be concluded that
there is statistically significant difference between the two groups. If there is more than 5% (p-
value is greater than 0.05) chance of getting the observed difference, the study would conclude
that there is not enough evidence to reject the null hypothesis, and it would fail to reject it.
Results
FTND scores oscillated from 2 to 9 with a mean of 5.80 (SD = 1.70). AUTOS scores ranged
from 9 to 36 with a mean of 22.40 (SD = 7.55). Item-to-total score correlation coefficients varied
from .21 to 0.63 for the FTND (α = .32) and from 0.51 to 0.76 for the AUTOS (α = 0.87).