Analysis of Caffeine's Influence on Gender and Age: Research Report

This report presents a comprehensive analysis of caffeine's effects on the human body, specifically focusing on how these effects differ between men and women, and across different age groups. The study employs experimental data and statistical tools, including SPSS, to investigate the relationship between caffeine consumption and various physiological responses. The research utilizes descriptive statistics to establish a baseline understanding of the data, including measures of dispersion and central tendency. Inferential statistical methods, such as linear regression, ANOVA, and correlation analysis, are then employed to explore the relationships between caffeine intake, age, gender, and choices related to caffeine consumption. The analysis includes model summaries, ANOVA tables, and regression coefficients to determine the significance of the findings. The study aims to validate or nullify the hypothesis that caffeine affects people of different ages in different ways, providing insights into the potential variations in caffeine's impact based on gender and age, and the factors influencing these differences.

Running head: CAFFEINE IN THE BODY, RESEARCH 1
How Does Caffeine Affect Women And Men Differently?
Name:
Institutional Affiliations:

CAFFEINE IN THE BODY, RESEARCH 2
Abstract
The use of the caffeine as a food additive in daily lives is an important element that
people need to have full knowledge. Its impact on the people using the products is of core
importance. Caffeine is a known stimulant substance that can alter the normal functioning of the
body once it is used. Further, caffeine can create addictive behavior of the persons who are using
it more often. It is in this respect that a study is necessary to be undertaken to understand the
impacts that the caffeine substances can cause in the body. The manner in which the female
persons interact and get influenced by caffeine is important to analyze. Therefore, there is need
to analyze and understand how caffeine would influence the boy based on the general
orientation. However, in this study, the need to confirm that caffeine can influence the boy will
be necessary. As a result, the study focuses on the people who are given the caffeine content and
the people who are not given the substance. The people who are not treated with caffeine are
considered the control group whose behavior analysis will be used to compare the behavior of
the people who were given the caffeine substances. Further, it is useful to understand how the
elements may vary depending on the amount of caffeine that the people have been treated. This
paper presents a discussion of the conditions leading to the test of the caffeine influence on the
people, the sample population used in the study, both for the control experiment and the
experimental data. Further, the study indulges in the development of analysis into the elements
that may have contributing factors alongside caffeine in the body. As a result, various
assumptions are made that would be necessary to help the researchers make meaningful
inferences from the research study results. The various kinds of analytics tools like line
regression analysis, ANOVA tests, and the t-tests are used to model the analysis of the study
results.

CAFFEINE IN THE BODY, RESEARCH 3
Introduction and Literature Review
Is there a possibility that caffeine affects women and Men Differently? This is the
research question that the analysis in this documents needs to model and present in a way that
will lead to the development of understanding of the research models and concepts. This study
deploys the experimental data that presents how different people reacted to the use of caffeine.
The analysis is undertaken using SPSS. The kinds of tests and analysis were undertaken in the
process include descriptive tests, linear regression analysis, ANOVA analysis and correlation
analysis. The analytical values are presented in tables and graphs for easy reading and
interpretations. This paper, therefore, aims to validate or nullify the null hypothesis, “Caffeine
does not affect people of different ages in different ways.”
The amount of caffeine that people use may have different kinds of impacts on their lives
(Satel, 2006). Nehlig and Boyet believe that some caffeine may have a different level of impacts
on the lives of the consumers, based on the quantity that they consume and the gender of the
persons consuming caffeine (Nehlig & Boyet, 2000 p. 75-7). Notably, caffeine consumption and
its impacts on the body can be modeled and studied using the statistical tools of analysis (Lean
and Crozier, 2012). Through the development of corresponding knowledge in the field of the
study, different kinds of analysis can be undertaken to help in examining the extent of the
caffeine use in the body. Some analytical tools are descriptive, and others are inferential. The
descriptive analysis is considered superficial, showing the basic characteristics of the data
analyzed in the study (Eriksson et al., 2013). Nonetheless, the descriptive statistical analysis
methods provide the foundation for the study. Therefore, the study deploys both the inferential
and the descriptive statistical data analysis. The descriptive data analysis method include the
analysis of the mean, mode, variances and the measures of the standard deviations in some cases.

CAFFEINE IN THE BODY, RESEARCH 4
However, the inferential statistical analysis deployed in the study include the linear regression
analysis, correlational analysis, and analysis of variance (Selvin, 2004).
Method Results Discussion
Before going deep into the data trends and values, it would be useful for the descriptive
analysis to show the component of the research elements. The descriptive analysis shows the
measures of dispersion alongside the standard deviation and the variance are useful tools that
give a background insight into the data analyzed (Salkind, 2016). From the research study, it
would be useful to understand the percentage distributions of the various elements in the group.
The composition of the mean, median, mode and the standard deviations of the elements present
in the different groups, treated to caffeine or not helps to understand the analyzed data better
(Warner, 2008). The following illustrated table shows the descriptive elements of the research
and the analysis that follows.

CAFFEINE IN THE BODY, RESEARCH 5
Statistics
Gender Age
Experiment
Time Simple GNG Choice
N Valid 160 160 160 160 160 160
Missing 0 0 0 0 0 0
Mean 29.4438 330.5811 .7624 .4527 .5612
Median 26.0000 349.2100 .3735 .4100 .4515
Mode 20.00 50.01a .30a .38 .38a
Std. Deviation 1.08990E
1
223.85582 .95458 .15175 .61571
a. Multiple modes exist. The smallest value is shown
There were 160 respondents from the research and among them there were no missing
responses from the research. This shows that all the respondents indicated their gender, age, had
experiment time recorded and indicted their “simple” parameter. They also had the GNG and the
choice options. Further, understanding the distribution of the people regarding gender is
important for this research. The researcher may be interested in future to know how different
gender groups would react to the consumption of caffeine, and as such, having knowledge on the
gender composition is useful. The below illustration shows the gender table.

CAFFEINE IN THE BODY, RESEARCH 6
Gender
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid F 81 50.6 50.6 50.6
M 79 49.4 49.4 100.0
Total 160 100.0 100.0
There were 81 female persons who participated in the research, representing 50.6% and
79 males representing 49.4%. This shows that the research was evenly distributed among the
male and e female members. There could be a further possibility that the impacts caffeine could
have on people may be age variant. As a result, it is useful to illustrate and understand the age
distributing of the respondents. A table like the one below shows the respondents’ ages as they
participated in the research.
Age
Frequency Percent Valid Percent Cumulative Percent
Valid 17 14 8.8 8.8 8.8
18 8 5.0 5.0 13.8
19 9 5.6 5.6 19.4
20 15 9.4 9.4 28.8
21 7 4.4 4.4 33.1
22 4 2.5 2.5 35.6
23 8 5.0 5.0 40.6
24 5 3.1 3.1 43.8
25 8 5.0 5.0 48.8
26 4 2.5 2.5 51.2
27 6 3.8 3.8 55.0
28 2 1.2 1.2 56.2
30 6 3.8 3.8 60.0

CAFFEINE IN THE BODY, RESEARCH 7
31 6 3.8 3.8 63.8
32 4 2.5 2.5 66.2
33 2 1.2 1.2 67.5
34 4 2.5 2.5 70.0
35 2 1.2 1.2 71.2
36 3 1.9 1.9 73.1
37 5 3.1 3.1 76.2
38 1 .6 .6 76.9
39 4 2.5 2.5 79.4
40 2 1.2 1.2 80.6
41 4 2.5 2.5 83.1
42 1 .6 .6 83.8
43 5 3.1 3.1 86.9
44 3 1.9 1.9 88.8
45 3 1.9 1.9 90.6
46 2 1.2 1.2 91.9
48 1 .6 .6 92.5
49 2 1.2 1.2 93.8
50 2 1.2 1.2 95.0
53 2 1.2 1.2 96.2
54 2 1.2 1.2 97.5
55 2 1.2 1.2 98.8
56 1 .6 .6 99.4
58 1 .6 .6 100.0
Total 160 100.0 100.0
Those people who were 20 years old comprised the majority of the respondents, because
their number, 15, representing 9.4% of all the respondents participated in the research. Further,
those who were 17 years old were also significantly higher in distributions, their numbers
reaching 14 in number and representing 8.8% of all the respondents. Other elements also made
up the descriptive models. The descriptive statistics gives the useful background on which the

CAFFEINE IN THE BODY, RESEARCH 8
inferential statistics can be founded. Understanding the inferential statistics begin from the full
knowledge of the descriptive statistical analysis and methods.
Results from Analysis
The inferential statistical analysis is a useful tool that shows the detailed, or the depth
analysis of the projects at hand (Ramsey & Schafer, 2012). Various kinds of statistical analysis
are useful for the modeling of the inferential knowledge. For simplicity in understanding the
relations that caffeine would have in the body of the consumers. A simple linear regression
analysis, simple bivariate correlations and ANOVA analysis are sufficient in making such
analysis. Every statistical element used in the inferential category aims to address the general
hypothesis of the study. The responses aim to show if there is a relationship between the amounts
of caffeine that one takes and the body reactions. At the end of it all, it would be useful to
understand how the relationships pair up to determine the impacts on the body, thus making an
informed decisions based on the study outcome and results.
To determine if there is a relationship between the age of the respondent using caffeine
and the choice that the person makes towards caffeine consumption, an analysis using linear
regression is necessary. The illustration below shows the model summary for the analysis.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .277a .077 .071 10.50472
a. Predictors: (Constant), Choice
b. Dependent Variable: Age

CAFFEINE IN THE BODY, RESEARCH 9
The model summary shows the R-value, the R square values and the adjusted R values in
the analysis. The R-value shows the extent to which the independent variable could influence
and predict the independent variable. From the table, the value of R is given as 0.277. The value
indicates that the extent to which age may influence the choices people make towards the use of
caffeine is 27.7%. Further, it means that 72.3% of the choice people would make towards the
use of caffeine is influenced by other factors, not necessarily the age. This is explained by the
many other factors that would also influence the choices people make towards the use of
caffeine. In the following analytical table, the ANOVA analysis is presented.
ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 1452.338 1 1452.338 13.161 .000a
Residual 17435.156 158 110.349
Total 18887.494 159
a. Predictors: (Constant), Choice
b. Dependent Variable: Age
Apart from the illustration of the regression and the residual regression sum of squares,
ANOVA analysis also presents the F value of the analysis, the significance level, and the DF.
With a df (1,158) giving an F value of 13.161, and with a significance level of 0.00 which is less
than the 0.05 confidence interval, it is believable that the interpretations that will result from the
analysis will be to significantly address the hypothesis. This is pointed out by the bigger value of

CAFFEINE IN THE BODY, RESEARCH 10
F, 13.161 and the higher significance, showing a near 100% confidence level. In line with the
developments, the illustration below shows the results of the linear regression analysis.
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 26.689 1.125 23.719 .000
Choice 4.909 1.353 .277 3.628 .000
a. Dependent Variable: Age
Using the regression equation, it can be determined that the age and the choices people
make about using caffeine would have some relations. The regression equation model states that
Y=C+a1X1+ε0 gives the level of influence that independent variable has in the dependent
variable;. Where C is a constant, a1 is the coefficient of the regression of the variable choice
represented by X1 and ε0 is the error term of the regression (Kutner, Nachtsheim, & Neter, 2004).
The error value itself is presented in the regression table as the standard errors of the coefficients.
The regression histogram can also be used to provide explanations to the elements further as
shown in the following chart.

CAFFEINE IN THE BODY, RESEARCH 11
Further inferential analysis using linear regression
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .614a .377 .361 8.71470
a. Predictors: (Constant), Experiment Time, GNG,
Simple, Choice
b. Dependent Variable: Age

CAFFEINE IN THE BODY, RESEARCH 12
The model summary shows the R-value, the R square values and the adjusted R values in
the analysis. The R-value shows the extent to which the independent variable could influence
and predict the independent variable (O’Brien, 2007). From the table, the value of R is given as
0.614. The value indicates that the extent to which independent variables like experiment time,
GNG, Simple, and Choice have, may influence the dependent variables towards the use of
caffeine is 61.4%. Further, it means that 38.6% of the choice people would make towards the
use of caffeine is influenced by other factors, not necessarily the age. In the following analytical
table, the ANOVA analysis is presented.
ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 7115.855 4 1778.964 23.424 .000a
Residual 11771.638 155 75.946
Total 18887.494 159
a. Predictors: (Constant), Experiment Time, GNG, Simple, Choice
b. Dependent Variable: Age
Apart from the illustration of the regression and the residual regression sum of squares,
ANOVA analysis also presents the F value of the analysis, the significance level, and the DF.
With a df (4,155) giving an F value of 23.424, and with a significance level of 0.00 which is less
than the 0.05 confidence interval, it is believable that the interpretations that will result from the
analysis will be to address the hypothesis significantly. This is pointed out by the bigger value of