1. Dr. Alok Kumar Gaurav
Assistant Professor
Department of Public Administration
QUANTITATIVE RESEARCH DESIGN
2. Quantitative Research Design
• relates to the design of a research project which uses quantitative
research methods. The design varies depending on the method used,
which could be
• telephone interviews,
• face-to-face interviews,
• online surveys, or surveys by post for instance.
• Other methodologies include SMS / Test Message surveys,
• or physical counts.
3. Quantitative Research Design
aimed at discovering how many people think, act or feel in a specific way.
Quantitative projects involve large sample sizes, concentrating on the quantity of responses, as
opposed to gaining the more focused or emotional insight that is the aim of qualitative research.
The standard format in quantitative research design is for each respondent to be asked the same
questions, which ensures that the entire data sample can be analysed fairly.
The data is supplied in a numerical format, and can be analysed in a quantifiable way using statistical
methods.
Surveys can, however, be tailored to branch off if the respondent answers in a certain way - for instance
people who are satisfied or dissatisfied with a service may be asked different questions subsequently.
4. Quantitative research design tends to favour
closed-ended questions
• . Providing respondents with a set list of answers,
• they will not normally be able to give lengthy open-ended responses.
• This design ensures that the process of quantitative research is far more efficient than it
would be if qualitative-style open ended questions were employed.
• It is more efficient because it is then not necessary to carry out the time-consuming process
of coding vast quantities of open-ended responses.
• Quantitative research design does often allow the inclusion of an ‘Other’ category in the list of
possible responses to questions,
• This allows those respondents who do not fit directly into the main categories to still get
their precise responses recorded and used in the analysis of the research project results.
5. QUANTITATIVE
Quantitative research
• determine the
relationship between one
thing (an independent
variable) and another (a
dependent or outcome
variable) in a population.
Quantitative research
designs
• descriptive (subjects
usually measured once)
• experimental (subjects
measured before and
after a treatment).
7. A Descriptive
Design
• seeks to describe the
current status of a
variable or
phenomenon.
• The researcher does
not begin with a
hypothesis, but
typically develops one
after the data is
collected.
• Data collection is
mostly observational in
nature.
A Correlational
Design
• explores the
relationship between
variables using
statistical analyses.
• it does not look for
cause and effect and
therefore, is also
mostly observational in
terms of data
collection.
A Quasi-
Experimental
Design
• (often referred to as
Causal-Comparative)
seeks to establish a
cause-effect
relationship between
two or more variables.
• The researcher does
not assign groups and
does not manipulate
the independent
variable.
• Control groups are
identified and exposed
to the variable.
• Results are compared
with results from
groups not exposed to
the variable.
Experimental
Designs,
• often called true
experimentation, use
the scientific method
to establish cause-
effect relationship
among a group of
variables in a research
study.
• Researchers make an
effort to control for all
variables except the
one being manipulated
(the independent
variable).
• The effects of the
independent variable
on the dependent
variable are collected
and analyzed for a
relationship.
12. Quantitative Data
• Obtained when the variable being studied
is measured along a scale that indicates
how much of the variable present.
• Reported in terms of scores.
• Higher scores indicate that more of the
variable
• Ex: The amount of money spent on sports
equipment by various schools
Categorical Data
• Simply indicate the total number of
objects, individuals or events a particular
category.
• Ex: The representation of each ethnic
group in a school.
13. Raw Scores
• Initial score obtained
• Difficult to interpret and
it has a little meaning.
Derived Scores
• Obtained by taking raw
scores and converting
them into more useful
scores.
Age and Grade-level Equivalents
• Tell us of what age or grade an
individual score is typical.
Percentile Ranks
• Refers to percentage of individuals
scoring at or below a given raw
score
Standard Scores
• Provide an other means of
indicating how one individual
compares to other individuals in a
group
14. • Frequency Polygons
• Skewed Polygons
• Histograms and Stem-Leaf Plots
• The Normal Curve
• Averages
• Spreads
• Standard Scores and the Normal Curve
• Correlation
15. • When the data are simply listed in no
apparent order, it is difficult to tell.
• We must put the information into some
sort of order.
• Frequency distribution – list the scores in
rank order from high to low (Table 10.2)
• Grouped frequency distribution –
information grouped into intervals and
quite informative (Table 10.3)
• Frequency polygon – present the data in
graph (graphical display).
64, 27, 61, 56, 52, 51, 34, 17, 27, 17,
24, 64, 31, 29, 31, 29, 29, 31, 31, 59,
56, 31, 27, 17
Listed below are the scores of a group of
students on mid semester biology test.
16.
17. Skewed
Positively Skewed Polygon
• The tail of the distribution trails off
to the right, in the direction of the
higher scores values.
Negatively Skewed Polygon
• The longer tail of the distribution
goes off to the left, in the direction
of the lower scores values.
18. &
Histogram is a bar graph used to display
quantitative data at the interval or ratio level of
measurement. Arranged from left to right on
the horizontal axis. At the intersection of the
two axis is always zero.
Steam-leaf plot is a display that organizes a set
of data to show both in shape and distribution.
Each data value is split into stem and leaf. The
leaf usually the last digit of the number and the
other digits to the left is stem. Ex: 149
leaf 9
stem 14
19. The
• The smooth curve not just connecting the series of dots,
but rather showing a generalized distribution of scores
that is not limited to one specific set of data.
• This smooth curves are known as distribution curves.
• When a distribution curve is normal, the large majority
of the scores concentrated in the middle and the scores
decrease in frequency far away from the middle.
• It is based on a precise mathematical equation.
• Useful for researchers.
20. Mod Median
• Is the point below and
above which 50 percent of
the scores in distribution
fall (midpoint).
7, 6, 5, 4, 3, 2, 1
Mean
• It is determined by adding
up all the scores and then
dividing this sum by the
total number of scores.
• Is the most frequent score
in a distribution
25, 20, 19, 17, 16, 16, 16,
14, 14, 11,10, 9, 9
70, 74, 82, 86, 88, 90
• Median is 84 the point
halfway between the two
middlemost scores.
52, 68, 74, 86, 95, 105
• The mean score is 80.
21. • Is the extent to which a distribution
is stretched or squeezed.
• The two distributions differ in what
statisticians call variability.
Inter Quartile Range
Overall Range
Standard Deviation
• The most useful index of variability.
• A single number that represents the
spread of a distribution.
A B
22.
23. Standard Scor&es
T scores
z scores
A raw score that is exactly
on the mean corresponds
to a z score of zero.
• A raw score that is exactly one SD above
the mean equals a z score of +1, while
below the mean equals a z score of -1.
• Ex: Mean = 50, SD = 2 48 50 52 z score +2
z score +2
24. z scores Of course z scores are not always exactly
one or two standard deviation away from
the mean.
25. • Convert the raw scores below the mean
from negative to positive.
• One way to eliminate negative z scores
and convert them to T scores.
T scores = (z scores x 10) + mean
26. When two sets of data are strongly linked together we say they
have a High Correlation. The word Correlation is made of
Co- (meaning "together"), and Relation. Correlation is Positive
when the values increase together, and. Correlation is
Negative when one value decreases as the other increases.
scatterplots
A pictorial representation of the
relationship between two
quantitative variables.
28. • Relate to data that are flexible and
do not follow a normal distribution.
• Make no assumptions.
INFERENTIAL
STATISTICS
PARAMETRIC NON-PARAMETRIC
• Make assumptions about the
parameters (defining properties) of the
population distribution(s) from which
one's data are drawn,
T-test ANOVA
Pearson
Correlation
ANCOVA
Chi-Square Friedman
Mann-Whitney
U test
Spearman
Correlation
29. PARAMETRIC INFERENTIAL STATISTICS
• T-test is used to determine whether there is a significant
difference between the means of two groups.
• Analysis of variance (ANOVA) is used to check if the means of
two or more groups are significantly different from each other.
• Pearson's Correlation Coefficient is a measure of the
strength of the association between the two variables.
30. NON-PARAMETRIC INFERENTIAL
STATISTICS
• Chi square test is any statistical hypothesis test where the sampling
distribution of the test statistic is a chi-squared distribution when the null
hypothesis is true.
• The Mann-Whitney U test is used to compare differences between two
independent groups when the dependent variable is either ordinal or
continuous, but not normally distributed.
• The Friedman test is used to test for differences between groups when
the dependent variable being measured is ordinal.
• The Spearman is often used to evaluate relationships involving ordinal
variables.
