Quantitative Research Analysis on Community Center Data

This report presents a quantitative research analysis focused on data from a community center, employing various statistical methods to examine relationships between different variables. The study investigates the influence of class types on attendance, revealing no significant impact. It also explores the association between self-esteem and independence, finding a moderate and significant positive correlation. Furthermore, the research examines memory problems across genders, indicating significant differences. The report also delves into the impact of family support and wellbeing on independence, highlighting their significant contributions. Regression analysis is used to determine the influence of family support, self-esteem, and wellbeing on independence levels. The report concludes with a discussion on the importance of statistical tools in data analysis and decision-making.

QUANTITATIVE RESEARCH
METHODS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
(1) Class type influence on attendance across year.........................................................................1
(2) Association between self esteem and level of independence.....................................................2
(3) Memory problem across gender.................................................................................................2
(4) Contribution of family support and wellbeing to independence level.......................................3
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6
APPENDIX......................................................................................................................................7

INTRODUCTION
In the present research report data analysis is done on raw data that is related to community
centre providing classes to elder people. In research work focus is on multiple areas like
influence that class type which people have joined have on attendance on relevant programs.
Relationship between self esteem and level of independence is also investigated and it is
identified whether if self esteem level is high in individual then in that case independence is also
high or not. In further study, it is identified whether male and female have same memory
problem or there is difference between both in terms of memory level. Apart from this, attention
is paid on finding out impact that family factor and well being have on the independence level of
individual. Thus, it can be said that research is done on wide basis in current report.
(1) Class type influence on attendance across year
H0: There is no significant impact of influence of class type on attendance across year.
H1: There is significant impact of influence of class type on attendance across year.
Interpretation
Regression analysis method is used to investigate relationship between class type and
total duration of sessions. It is important to note that above mentioned approach not only indicate
whether there is significant impact of one variable on the other (Qvortrup, 2015). It also indicates
the pace at which dependent variable expect to change with slight upward or downward
movement in independent variable. Table given above is explaining relationship between
variable in detail. It can be observed that value of (R = 0.109, R Square = 1%, P = 0.171>0.05, F
= 1.893, Coefficient = 1.385). On analysis of these facts it can be said that there is no significant
difference between dependent and independent variable. This is concluded because value of P is
greater than 5% value of level of significance. Hence, null hypothesis accepted. R square value is
0.019 which is reflecting weak relationship between variables. This is strong evidence of the fact
that whichever class people join it does not lead to any big variation in attendance in respect to
class in community centre. R square value of 1% is further underpin above conclusion. As this
indicating that that independent variable is explaining 1% variation in dependent variable.
Hence, it is concluded that change in class in data does not bring big change in the total
attendance across year.
1

(2) Association between self esteem and level of independence
H0: There is no significant association between self esteem and level of independence.
H1: There is significant association between self-esteem and level of independence.
Interpretation
Major requirement of query was to find out find out association between self esteem and
independence. In respect to this correlation method is employed which assist analyst in
identifying association between variables. Correlation value is 0.491 which is indicating
moderate relationship among variables. Value of statistic (P = 00>0.05) which is indicating that
there is significant association between both variables. Thus, it can be concluded that with
change in self esteem level at least moderate change comes in independence level of individuals.
So, it can be said that alternative hypothesis accepted. In practical life also, this thing proved
correct. This is because self-esteem means that one has respect about him in his own eye and
cannot tolerate if someone makes insult (Salkind, 2016). If individual will have such kind of high
esteem then in that case it will definitely have highest level of independence. If one will not have
independence then in that case self-esteem individual cannot survive. Hence, it can be said that if
any individual have a very high self-esteem then in that case level of independence will be
certainly high.
(3) Memory problem across gender
H0: There is no significant association between gender segments in respect to memory level.
H0: There is significant association between gender segments in respect to memory level.
2

Chi square test is used to identify association between varied options of categorical
variables with each other. Rationale behind application of this approach was that there was
requirement to explore relationship of male and female individually with memory level and to
find out whether both category of people have similar or different memory level. Value of level
of significance given above in the table is (P = 0.00<0.05) which is reflecting that there is
significant difference between male and female in respect to relationship they have with memory
level. Means that memory problem across both male and female is quite different.
(4) Contribution of family support and wellbeing to independence level
H0: There is no significant impact of family support and wellbeing on independence level.
H1: There is no significant impact of family support and wellbeing on independence level.
Interpretation
In this section effort is made to identify relation of family support and wellbeing on
independence level. In this regard regression analysis approach is used because it will help in
deeply investigating impact of family support and well being on independence level. Value of
statistic is (F = 23.65, R= 0.412, R Square = 0.169, P = 0.00<0.05). Value of level of
significance is less then 5% level of significance and this is sufficient to conclude that there is
3

significant impact variables family support and wellbeing have on the independence level. This
assumption is further supported by the fact that value of R is 0.412 which indicate that there is
near to moderate relationship between variables. Value of R square is 0.169 which means that
16% variation in dependent variable is explained by independent variable. Thus, alternative
hypothesis accepted and it is concluded that if family support level and wellbeing will change
then in that case independence level will surely change moderately. In practical life also, we can
see that every family have its own environment and accordingly people live in their homes
(Schroeder and et.al,., 2015). For example, in home there is specific individual who hold entire
power and of very angry nature then in that case other people living in home will remain
frightened and will have less freedom of speech. Thus, it is true fact that family support has
strong connection with the independence level of individual. If there is less family support then
in that case individual will have less independence.
Further this analysis is extending and now investigation is done on finding out
relationship between family support and self-esteem to independence level.
H0: There is no significant impact of family support and self-esteem on independence level of
individuals.
H1: There is significant impact of family support and self-esteem on independence level of
individuals.
Interpretation
In above table regression analysis is done to explore relationship between family support
and self-esteem on independence level of individuals. Value of statistic is (F = 38.37, P =
0.00<0.05, R = 0.499 and R square = 0.249 or 24%). Statistic is indicating that there is
significant impact of independent variables on dependent variables. Value of level of
significance is also lower then 0.05 which indicate that alternative hypothesis is accepted. As
mentioned from practical life point of view it is again proved that self esteem and family support
have significant impact on independence level of individuals (Biryukova and Sinyavskaya,
2017). Thus, it is recommended that at workplace, at home or any community centre there must
be family like environment so that individual can be more expressive in nature and feel more
independent.
4e regression equation
Y = a+bx
4

Where Y refers to the independent variable and a refers to the alpha value which can be seen in
the regression model. X refers to the independent variable. By using alpha, beta and independent
variable value final number of dependent variable is identified.
Independence = 58.2+(-0.181) *Family support+5.670*Self esteem
4f performance level anticipation
Y = 58.32-0.181*25+5.670*4
= 76.47
CONCLUSION
On basis of above discussion, it is concluded that there is significant importance of the
statistical tool for the analysts. This is because by using same in varied ways data can be
analysed and decisions can be made in proper manner. Linear regression is the one of the most
popular method that is used for analysing data as it reflects change that can be observed in one
variable with change in another variable. Apart from this, correlation is another approach which
help in assessing strength of relationship between variables.
Present research study shows that whatever class individual join in the community centre no
impact comes in attendance rate. Across male and female memory problem vary. It is also
concluded that well being and family condition have huge impact on the independence level of
individuals. Apart from this, self esteem is another factor that play a vital role in determining
individual independence level.
5

REFERENCES
Books and Journals
Biryukova, S. and Sinyavskaya, O., 2017. Children out of parental care in Russia: What we can
learn from the statistics. The Journal of Social Policy Studies. 15(3). pp.367-382.
Qvortrup, J., 2015. A voice for children in statistical and social accounting: A plea for children’s
right to be heard. In Constructing and reconstructing childhood (pp. 74-93). Routledge.
Salkind, N.J., 2016. Statistics for people who (think they) hate statistics. Sage publications.
Schroeder, S and et.al,., 2015. childLex: A lexical database of German read by
children. Behavior research methods. 47(4). pp.1085-1094.
6

APPENDIX
(1)
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Class2b . Enter
a. Dependent Variable: Totalduration
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .109a .012 .006 6.36509
a. Predictors: (Constant), Class2
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 76.706 1 76.706 1.893 .171b
Residual 6401.269 158 40.514
Total 6477.975 159
a. Dependent Variable: Totalduration
b. Predictors: (Constant), Class2
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 29.526 1.597 18.485 .000
Class2 1.385 1.006 .109 1.376 .171
a. Dependent Variable: Totalduration
(2)
7

Correlations
SelfEst Indep
SelfEst
Pearson Correlation 1 .491**
Sig. (2-tailed) .000
N 235 235
Indep
Pearson Correlation .491** 1
Sig. (2-tailed) .000
N 235 235
**. Correlation is significant at the 0.01 level (2-tailed).
(3)
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * MemProb 235 100.0% 0 0.0% 235 100.0%
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Pearson Chi-Square 22.708a 1 .000
Continuity Correctionb 21.481 1 .000
Likelihood Ratio 23.091 1 .000
Fisher's Exact Test .000 .000
Linear-by-Linear Association 22.611 1 .000
N of Valid Cases 235
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 57.26.
b. Computed only for a 2x2 table
(4)
(A)
Variables Entered/Removeda
8

Model Variables
Entered
Variables
Removed
Method
1 Wellbeing,
FamilySupb . Enter
a. Dependent Variable: Indep
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .412a .169 .162 14.504
a. Predictors: (Constant), Wellbeing, FamilySup
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 9951.146 2 4975.573 23.654 .000b
Residual 48801.620 232 210.352
Total 58752.766 234
a. Dependent Variable: Indep
b. Predictors: (Constant), Wellbeing, FamilySup
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -143.938 33.146 -4.343 .000
FamilySup .004 .127 .002 .034 .973
Wellbeing 12.850 1.869 .412 6.875 .000
a. Dependent Variable: Indep
(B)
Variables Entered/Removeda
9

Model Variables
Entered
Variables
Removed
Method
1 SelfEst,
FamilySupb . Enter
a. Dependent Variable: Indep
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .499a .249 .242 13.795
a. Predictors: (Constant), SelfEst, FamilySup
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 14604.466 2 7302.233 38.373 .000b
Residual 44148.300 232 190.294
Total 58752.766 234
a. Dependent Variable: Indep
b. Predictors: (Constant), SelfEst, FamilySup
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 58.323 3.335 17.488 .000
FamilySup -.181 .122 -.085 -1.484 .139
SelfEst 5.670 .647 .504 8.758 .000
a. Dependent Variable: Indep
10