Quantitative Research Report: Statistical Analysis & SPSS Insights

This report provides a comprehensive analysis of quantitative research methods, focusing on levels of measurement (nominal, ordinal, interval, and ratio scales) with examples. It elucidates the meaning of central tendency and measures of dispersion, highlighting their significance in data analysis. The report differentiates between descriptive and inferential statistics, outlining their respective uses and outcomes. Key statistical concepts such as hypothesis testing (null and alternate), independent and dependent variables, and extraneous variables are explained. Furthermore, the report includes an interpretation of SPSS results, analyzing frequency tables, cross-tabulations, Chi-square tests, and Phi/Cramer's V to derive meaningful conclusions about the relationship between variables, specifically focusing on the association between noisy neighborhoods and age groups. The analysis concludes that quantitative research, with its use of numeric data, offers precise and accurate conclusions, and that scales of measurement are valuable tools for data categorization and analysis.

Written report

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
1. Explaining four levels of measurement by giving example....................................................3
2. Meaning of central tendency and measures of dispersion......................................................3
3. Difference within descriptive and inferential statistics...........................................................4
4. Explain the following points...................................................................................................4
5. Interpretation for the SPSS result............................................................................................5
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................7
APPENDIX......................................................................................................................................8

INTRODUCTION
Quantitative research is being referred to as the way of analysing the numeric data and
facts and figures relating to the research problem. Use of quantitative research is important as it
provides precise and accurate information. The current study will outline the different level of
measurement along with dispersion and central tendency. Further it will outline the distinction
between inferential and descriptive and different terms and interpretation of SPSS output.
MAIN BODY
1. Explaining four levels of measurement by giving example
The level of measurement of scale is being referred to as the way through which any
element can be measured. There are different types of level of scale which can used and are as
follows-
 Nominal scale- this is a type of scale which involves least amount of information and
each variable is being assigned with the number. For example, data relating to gender,
marital status.
 Ordinal scale- within this type of level the information can be categorized and ranked
but it cannot be stated about the interval between the ranking. For example, top 5
Olympic medallist
 Interval level- within this type of the scale equal interval within the neighbouring data
points but in this case there is not any true point (Level of Measurement, 2022). For
example, temperature in Fahrenheit
 Ratio level- this is a type of the scale which involves categorising rank and having equal
interval and also there is a true zero- point value which is not in case of interval scale. For
example, height, temperature in Kelvin.
2. Meaning of central tendency and measures of dispersion
The measures of central tendency are being defined as the statistical tool which assist in
deciding a single value for analysing the whole data. this generally involves different types of
tools like the mean, median and mode. all these three tools are assistive to companies and
researcher in analysing the data and infer conclusion from that.

On the other hand, the measures of dispersion are being defined as the tool which assist
in analysing the dispersion of the scattering of data (Boeren, 2018). This tool helps the researcher
in analysing the disparity within the data and the variability being present within the data set.
3. Difference within descriptive and inferential statistics
Basis Descriptive statistics Inferential statistics
Meaning It is the branch of statistics
which involves describing the
population within the study.
Inferential statistics is being
referred to as focus on
drawing conclusion about the
population on basis of sample
analysis (Rutberg and
Bouikidis, 2018).
Usage This type of statistics is used
to describe the situation.
The inferential is used in order
to explain the chances of
occurrence of any event.
Final result The final result of descriptive
is in form of graphs, chart and
tables.
The final result for inferential
statistics includes the
probability.
4. Explain the following points
a. Hypothesis and null hypothesis
The hypothesis is being referred to as the set of assumption or the idea which is being
proposed so that it can be tested true. This is necessary for the researcher to set the hypothesis
because it provides a base for the research. on the other hand, null hypothesis is stated as a
statement which propose the fact that there is not any significant relation being present within the
variables being tested.
b. Independent and dependent variable
The independent variable is being defined as the variable which has the capacity of
manipulating or making changes within the other variable as well. This variable is being used by
the researcher for changing or controlled within the scientific experiment for testing the
dependent variable (Bloomfield and Fisher, 2019). In against of this the dependent variable is the

one which is being tested and for this variable the test is being conducted. Any change within the
independent variable will lead to change in dependent variable as well.
c. Extraneous variable
This is a type of variable which is not being tested but have the potential effect over the
whole research study. in case these variables are not controlled then it will lead to inaccurate
conclusions relating to the relation being present within the independent and dependent variable.
5. Interpretation for the SPSS result
a. Frequency table
With the help of the frequency table it is clear that from maximum of the participant 52
% agrees that the noisy neighbourhood is not at all common within the population (Haas and
Hadjar, 2020). Along with this it was also identified that 35 % of the population states that noisy
neighbour or loud parties is not very common. However, on the other hand, remaining 8.1 % and
4.7 % agrees to fairly common and very common respectively.
b. Cross tabulation
With the analysis of the cross tabulation it is clear that majority of elderly people agrees
that noisy neighbour is not at all common and adult is 49.3% and young adult is 41.2 %. Also in
case of not very common includes 40.7 % for young adult, 37.1 % for adult and 28.3%. further in
case of fairly common the age distribution was 11.6% for young adult, 8.2% for adult and in case
of elderly it is 5.9%. At last in case of very common includes 6.6% for young adult, 5.5 % adult
and 2.2% for elderly.
c. Chi- square result
With the help of the chi- square significance value is 0.000 which simply implies that
alternate hypothesis is accepted and there is a relation is being present in noisy neighbourhood
and age.
d. Phi/ Cramer V
Further with help of Phi it is clear that the value is 0.171 and the Cramer V is 0.121. this
simply states that there is good association between both the variables. From the above analysis,
it is clear that Cramer V is much better to analyse data as compared to phi.

CONCLUSION
In the end it is concluded that using the quantitative research is important as this includes
using numeric data and it provides more precise and accurate conclusion. The above discussion
outlined that using scale for measurement is helpful like ratio, ordinal and others. Also it was
analysed that there is a different being present within the descriptive and inferential statistics.

REFERENCES
Books and Journals
Bloomfield, J. and Fisher, M. J., 2019. Quantitative research design. Journal of the Australasian
Rehabilitation Nurses Association. 22(2). pp.27-30.
Boeren, E., 2018. The methodological underdog: A review of quantitative research in the key
adult education journals. Adult Education Quarterly. 68(1). pp.63-79.
Haas, C. and Hadjar, A., 2020. Students’ trajectories through higher education: a review of
quantitative research. Higher Education. 79(6). pp.1099-1118.
Rutberg, S. and Bouikidis, C. D., 2018. Focusing on the fundamentals: A simplistic
differentiation between qualitative and quantitative research. Nephrology Nursing
Journal. 45(2). pp.209-213.
Online
Level of Measurement. 2022. [Online]. Available through:
<https://corporatefinanceinstitute.com/resources/knowledge/other/level-of-measurement/>

APPENDIX
Frequency table
Cross tabulation
Chi-square test

Phi/Cramer’s V