Quantitative Research Methods for Social Scientists

This article discusses the four levels of measurement, central tendency, measure of dispersion, descriptive and inferential statistics, hypothesis and null hypothesis, independent and dependent variable, and extraneous variables in quantitative research methods for social scientists. It also includes interpretation of results using frequency table, crosstabulation, chi-square result, Phi/Cramer’s. The article cites various books and journals as references.

Quantitative research methods for
social scientist

Table of Contents
1. Presenting the four levels of measurement..............................................................................2
2. Definition of central tendency and measure of dispersion......................................................2
3. Descriptive Statistics and Inferential Statistics........................................................................3
4. Explaining the terms................................................................................................................4
5. interpretation of the results......................................................................................................4
REFERENCES................................................................................................................................6

1. Presenting the four levels of measurement
 Nominal: Here, the data can be only categorized and it does not involve quantitative
value (Dalati, 2018). Also, in survey questions, it can be performing by using a sign like,
1st for first option, 2nd for second option. For example
Q: Where do you live?
 City
 Town
 Ordinal: In this, data can be categories on the basis of rank and involves quantitative
study. Also, it maintains descriptional qualities with an intrinsic order. In this, Mann-
Whitney U test is used in order to analyse ordinal data. For example,
Q: How satisfied are you with company’s offerings?
 Very satisfied – 1
 Satisfied -2
 Neutral – 3
 Unsatisfied – 4
 Very unsatisfied -5
 Interval: The data can be ranked in an equal intervals between closely associated data but
it cannot be zero. Like test score, temperature in Fahrenheit and Celsius (Steyn and De
Bruin, 2018).
 Ratio: It is similar to interval but here it is true zero point because here zero does mean an
absolute lack of the variable. Further, it can be stated that it provides most detail
information as a researcher in order to calculate the mean, median and mode.
2. Definition of central tendency and measure of dispersion
Central tendency: It is defined as a number that is used to represent center of a set of data.
There are three measures that is used by the scholar like, mean, median and mode. Here, mean

refers to the average of dataset, where median represent the middle value when data is arranged
in ascending order (Mishra and et.al., 2019). Last is mode that identify which data is frequently
occur in a dataset. With the help of this, researcher determine the typical numerical point in a set
of data so that effective outcome can be generated.
Measure of dispersion: It assist to interpret the variability of data by using five measures like,
range, variance, standard deviation, mean deviation and quartile deviation. Therefore, it can be
stated that it is mainly used to describe the spread of data and determine variation around a
central value (Kaliyadan and Kulkarni, 2019). Thus, it can be stated that having a strong
knowledge pertaining to dispersion assist to develop a vital understanding pertaining to statistics.
These both terms are together found in descriptive statistics that is mainly used by the scholar
while formulating quantitative study.
3. Descriptive Statistics and Inferential Statistics
In today’s world, statistics plays an important role in this field of research that assist to
collect as well as analyse the data in an effective manner. Further, it can be stated that through
descriptive and Inferential Statistics, researcher ascertain the role and meet the defined aim of the
study as well.
Particulars Descriptive Statistics Inferential Statistics
Meaning It provide information with regard to raw data
that assist to describe data in an effective
mode.
It helps implication about
defined population by
analysing data drawn from the
same.
Helps in Descriptive Statistics assist in organising,
analysing the data in an effective manner.
It allows to compare the data
from previous research in order
to prove hypothesis and make
prediction (Amrhein, Trafimow
and Greenland, 2019).

Usage It mainly describe a situation about a dataset Inferential Statistics mainly
used to determine chance of
occurrence of an event.
Function It determines about the data and examine
population as well (Guetterman, 2019).
It provide a valid conclusion by
analysing the results
appropriately
Presentation
in report
By using proper table, graph and charts data
can be presented
It is mainly attained by
probability (Zhang and et.al.,
2018).
4. Explaining the terms
a. hypothesis and null hypothesis
In the statistical hypothesis testing, null hypothesis is assist to predict there is no effect or
relationship between a variable (Mertens and Recker, 2020). It can be denoted as H0 whereas
alternative can be represent as H1. On the other side, alternative hypothesis predict relationship
between the same.
b. Independent and dependent variable
Independent variable does not changes because there is no control over it and that is why,
it can be stated that it is a cause. On the other side, dependent variable is depend upon something
and as a result, they keep fluctuate when there is a change identified over other variable
(Williams, 2020). For example, sales depend upon the profit and that is why, profit is considered
as a dependent whereas sales is independent.
c. Extraneous variables
It is a type of variable that is not being study by the scholar but it somehow affects the
dependent variable in a research. For example, time of a day of testing and demographic variable
that affect the overall research (Laksana and et.al., 2020). The only way to control the same is to
choose random sampling method so that chances of occurrence minimized.

5. interpretation of the results
frequency table
It has been interpreted that out of 100%, only 4.7% of them stated that it is very common
to have noisy neighbor and loud parties whereas 8.1% of them stated it is fairly common.
However, 35.1% of the total population stated that it is not very common and only 52% of them
stated that it is not at all common to have noisy neighbor with loud parties.
crosstabulation
From 3134, 148 people stated that it is very common to have noisy neighbor and loud
parties in which 38 are young adults, 90 are adults and 20 are elderly. Whereas 255 of them are
stated it is fairly common in which 67 are young adult and 134 are adults. Further, 1102 people
stated that it is not very common in which 608 are adults and 235 are young adults. Moreover,
majority of them stated that not at all in which 808 are adults and 583 are elder people.
Chi-square result
The results shows that alternative hypothesis is accepted over other because the value of
p is 0.00 which is lower than 0.05 and that is why, there is a relationship between noisy neighbor
and loud parties
Phi/Cramer’s
Phi refers that there is only 17% association between noisy neighbor and loud parties
whereas crammer reflected that there is a positive relation between the variable.

REFERENCES
Books and Journals
Amrhein, V., Trafimow, D. and Greenland, S., 2019. Inferential statistics as descriptive statistics:
There is no replication crisis if we don’t expect replication. The American
Statistician. 73(sup1). pp.262-270.
Dalati, S., 2018. Measurement and Measurement Scales. In Modernizing the Academic Teaching
and Research Environment (pp. 79-96). Springer, Cham.
Guetterman, T.C., 2019. Basics of statistics for primary care research. Family medicine and
community health. 7(2).
Kaliyadan, F. and Kulkarni, V., 2019. Types of variables, descriptive statistics, and sample
size. Indian dermatology online journal. 10(1). p.82.
Laksana, E. and et.al., 2020. The impact of extraneous features on the performance of recurrent
neural network models in clinical tasks. Journal of Biomedical Informatics, 102,
p.103351.
Mertens, W. and Recker, J., 2020. New guidelines for null hypothesis significance testing in
hypothetico-deductive is research. Journal of the Association for Information
Systems, 21(4), p.1.
Mishra, P. . and et.al., 2019. Descriptive statistics and normality tests for statistical data. Annals
of cardiac anaesthesia. 22(1). p.67.
Steyn, R. and De Bruin, G., 2018. Investigating the validity of the Human Resource Practices
Scale in South Africa: Measurement invariance across gender. SA Journal of Human
Resource Management. 16(1). pp.1-10.
Williams, R.A., 2020. Ordinal independent variables. SAGE Publications Limited.
Zhang, J. . and et.al., 2018. Applications of inferential statistical methods in library and
information science. Data and Information Management. 2(2). pp.103-120.