Quantitative Research Methods for Social Scientists

This article discusses the basics of quantitative research methods for social scientists. It covers the four levels of measurement, measures of central tendency and dispersion, descriptive and inferential statistics, hypothesis and null hypothesis, and interpretation of SPSS results. The article also provides examples and explanations for each concept. Course code and college/university are not mentioned.

Quantitative Research
Methods for Social Scientists
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Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Task 1: Identify and explain the four levels of measurement, giving an example of each one...3
Task 2: Explain what is meant by Measures of Central Tendency and Measures of Dispersion 4
Task3: What is the difference between Descriptive Statistics and Inferential Statistics.............4
Task 4: Explain the following terms............................................................................................5
Task 5: Interpretation of the SPSS results...................................................................................5
REFERENCES................................................................................................................................7
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INTRODUCTION
Quantitative research method focuses on measuring objective data which is the form of
numbers. Here statistical, mathematical and numerical analysis and evaluation of gathered
information is done. Through this method manipulation of collected data via pools, survey or
questionnaire is done by using computational techniques (Liamputton, 2019). Social scientist
collects the data from large sample in order to understand what individual think or perceives
thoughts about a specific situation. The main aim behind quantitative research is to find out the
relationship between independents variables with dependent variable. There are two types of
quantitative research design which are descriptive and experimental.
MAIN BODY
Task 1: Identify and explain the four levels of measurement, giving an example of each one
The four level of measurement are nominal, ordinal, interval and ration. These are used in
order to manipulate data and get useful information out of it which helps in making decision
about the particular topic.
Nominal: In this data are only categorized or labelled. This type of measurement method
is informative as it helps in identify the characteristic by providing the names. In short the
numerical values are being classified with unique name (Frankfort-Nachmias, Leon-Guerrero
and Davis, 2019).
For example: In knowing the sex of the respondent they are classified into two categories which
are male and female. And in nominal they are providing unique identity that is male as 1 and
female as 2.
Ordinal: In ordinal method data is being categorised as well as ranked. The name itself
says providing order to the collected data.
Some of the example of ordinal variables are
Likert scale: Strongly agree, agree, neutral, disagree and strongly disagree.
Interval: In this data are evenly spaced as well as categorized and ranked. It possesses
the property that helps in identifying the difference in the variables (Feng and et. al, 2020).
Some of the example of interval measurement are Fahrenheit and Celsius temperatures.
Ratio: It is identifying as those data which can be Categorised, ranked, evenly spaced but
also has the property of containing zero in the variables.
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Some of the example of ratio measurement method is tome measure in second or minutes, blood
pressure in millimetres and many more.
Task 2: Explain what is meant by Measures of Central Tendency and Measures of Dispersion
Basis Measure of central tendency Measure of dispersion
Definition Central tendency can be
defined as locating the centres
of the distrusted values
Measure of depression means
the amount of speeded data of
the centre data set.
Types There are three types of
central tendency which are
mean, median and mode
(Ashkezari-Toussi, Kamel and
Sadoghi-Yazdi, 2019).
It is classified as range,
standard deviation and variant.
Calculation Measuring central tendency is
quite easier as well as requires
less time.
Measuring dispersion require
more knowledge is tough to
calculate.
Task3: What is the difference between Descriptive Statistics and Inferential Statistics
Basis Descriptive statistics Inferential statistics
Definition It is defined as that method of
statistics which focuses on
describing the population of
the study
In this type of statistics, it is
more concerned in drawing
the conclusion about the
sample participants.
Purpose It present the outcome in
more organised way so that
meaning can be analysed
easily
It compares and test data so
that conclusion can be drawn
out (Kaur, Stoltzfus and
Yellap, 2018).
Form of result It represent data in the form of
charts, tables and graphs
In the form of probability
Used for It is used for describing the It is used to explain the
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particular situation in
generalised way (Mishra and
et. al, 2019).
probability of occurring an
event
Task 4: Explain the following terms
The hypothesis and null hypothesis: The hypothesis is used to test the relationship of
effect between the variables ad the null hypothesis is used to test that there is no relationship and
effect between the variables.
Example is
Null hypothesis: There is no relationship between employee turnover on organisational
performance and productivity.
Alternative hypothesis: There is a relationship between employee turnover on organisational
performance and productivity (Cero, Mitchell and Morris, 2021).
Independent and dependent variable: Independent variable is that variables which is
being manipulates or changed in order to see the effect on dependent variable. On the other hand,
is that variable which is measured to see the cause in independent variable. In short independent
variable influences the dependent variable.
For example: To identify the impact of intermitted fasting on weight of the people. In this
intermitted fasting is independent variable and weight of people is dependent variable
(Bloomfield and Fisher, 2019).
Extraneous variables: It is that variable in the research which is not being tested or
investigated but has the potentially to affect the result of the study. This type of variable has the
power to become a threat for internal validity of the study. There are four types of extraneous
variable which are situational variable, participant variable, experimenter effect and demand
characteristic.
Task 5: Interpretation of the SPSS results
1. Frequency table: From the accumulated data it is analysed that out of 3146 participants
148 respondent say that it is very common that there have noisy neighbours and loud
parties. 255 answered that it is Ealey common in aspect, 1105 is of the view that it is not
common to have noisy neighbour or loud parties and the rest 1635 participants that it is
not at all common to have noisy neighbour and loud parties.
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2. Cross tabulation brings something together:
 Nominal-separate: in the above table nominal variable is the age group of the people
which is young adult, adult and elderly. The total participants in the series of young
variable is 578, adult 1640 and elderly it is 916.
 Ordinal-class degree: there are four class degree which is very common (1), fairly
common (2), not very common (3) and not at all common (4). The overall people who
belong to number 1 group is 148 and in 2 it is 255, 3 categories is 1102 and last from
4 it is 1629.
 Internal-age gap: There are three interval age groped which are young adult from 18
to 25. Adult are from 26 to 32 and elder are more than 32.
 Ratio- The ratio of very common answer is 4.7 % and fairly common is 8.1%, not
very common is 35.2 and not at all common is 52%.
3. Chi-square result: From the assemble piece of data it is interpreted that there is no
relationship between loud parties or noise neighbour on different age group because the
significance value is less then 0.05.
4. Phi/Cramer’s: It is used to measure the strength between two variables and from the
data it is seen that the two variables are not associated with each other in strong manner.
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REFERENCES
Ashkezari-Toussi, S., Kamel, M. and Sadoghi-Yazdi, H., 2019. Emotional maps based on social
networks data to analyze cities emotional structure and measure their emotional
similarity. Cities. 86. pp.113-124.
Bloomfield, J. and Fisher, M.J., 2019. Quantitative research design. Journal of the Australasian
Rehabilitation Nurses Association. 22(2). pp.27-30.
Cero, I., Mitchell, S.M. and Morris, N.M., 2021. Causal inference in suicide research: When you
should (and should not!) control for extraneous variables. Suicide and Life‐Threatening
Behavior. 51(1). pp.148-161.
Feng, J., and et. al, 2020. A new measure of ensemble central tendency. Weather and
Forecasting. 35(3). pp.879-889.
Frankfort-Nachmias, C., Leon-Guerrero, A. and Davis, G., 2019. Social statistics for a diverse
society. Sage Publications.
Kaur, P., Stoltzfus, J. and Yellapu, V., 2018. Descriptive statistics. International Journal of
Academic Medicine. 4(1). p.60.
Liamputtong, P. ed., 2019. Handbook of research methods in health social sciences. Singapore::
Springer.
Mishra, P., and et. al, 2019. Descriptive statistics and normality tests for statistical data. Annals of
cardiac anaesthesia. 22(1). p.67.
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