Research

IC 601
Research Methodology
Quantitative Research
Qualitative Research
Research and research methods
• Research methods are split broadly into quantitative and qualitative
methods
• Which you choose will depend on

• your research questions
• your underlying philosophy of research
• your preferences and skills
Data and Data Collection
Fundamentally--2 types of data
Quantitative – Numbers, tests, counting,

measuring
Qualitative – Words, images, observations,

conversations, photographs
Data Collection Techniques
Observations,
Tests,
Surveys,
Document analysis
(the research literature)
A note about data
• Quantitative data is about quantities, and therefore numbers
• Qualitative data is about the nature of the thing investigated, and
tends to be words rather than numbers
• Difference between primary and secondary data sources
• Be aware of research data management practices and archives of data
sets (both in terms of downloading and uploading)
Choosing your approach
• Your approach may be influenced by your colleagues’ views, your organisation’s approach, your
supervisor’s beliefs, and your own experience
• There is no right or wrong answer to choosing your research methods
• Whatever approach you choose for your research, you need to consider five questions:
• What is the unit of analysis? For example, country, company or individual.
• Are you relying on universal theory or local knowledge? i.e. will your results be generalisable, and produce universally
applicable results, or are there local factors that will affect your results?
• Will theory or data come first? Should you read the literature first, and then develop your theory, or will you gather
your data and develop your theory from that? (N.B. this will likely be an iterative process)
• Will your study be cross-sectional or longitudinal? Are you looking at one point in time, or changes over time?
• Will you verify or falsify a theory? You cannot conclusively prove any theory; the best that you can do is find nothing
that disproves it. It is therefore easier to formulate a theory that you can try to disprove, because you only need one
‘wrong’ answer to do so.
Quantitative approaches
• Attempts to explain phenomena by collecting and analysing numerical
data
• Tells you if there is a “difference” but not necessarily why
• Data collected are always numerical and analysed using statistical
methods
• Variables are controlled as much as possible (RCD as the gold standard) so
we can eliminate interference and measure the effect of any change
• Randomisation to reduce subjective bias
• If there are no numbers involved, its not quantitative
• Some types of research lend themselves better to quant approaches than
others
Quantitative data
• Data sources include
• Surveys where there are a large number of respondents (esp where you have
used a Likert scale)
• Observations (counts of numbers and/or coding data into numbers)
• Secondary data (government data; SATs scores etc)
• Analysis techniques include hypothesis testing, correlations and
cluster analysis
Analysing quant data
• Always good to group and/or visualise the data initially →
outliers/cleaning data
• What average are you looking for?
Mean, median or mode?
• Spread of data:
• skewness/distribution
• range, variance and standard deviation
Defining Surveys and Experiments
• A survey design provides a quantitative description of trends,
attitudes, and opinions of a population, or tests for associations
among variables of a population, by studying a sample of that
population.
• Survey designs help researchers answer three types of questions: (a)
descriptive questions (b) questions about the relationships between
variables (c) questions about predictive relationships between
variables over time
Defining Surveys and Experiments
• An experimental design systematically manipulates one or more
variables in order to evaluate how this manipulation impacts an
outcome (or outcomes) of interest.
• Importantly, an experiment isolates the effects of this manipulation
by holding all other variables constant. When one group receives a
treatment and the other group does not (which is a manipulated
variable of interest), the experimenter can isolate whether the
treatment and not other factors influence the outcome.
Quantitative Methods
Experiment: Research situation with at least

one independent variable, which is
manipulated by the researcher
Quantitative approaches
• Attempts to explain phenomena by collecting and analysing
numerical data
• Tells you if there is a “difference” but not necessarily why
• Data collected are always numerical and analysed using statistical
methods
• Variables are controlled as much as possible (RCD as the gold
standard) so we can eliminate interference and measure the
effect of any change
• Randomisation to reduce subjective bias
• If there are no numbers involved, its not quantitative
• Some types of research lend themselves better to quant
approaches than others
Quantitative data
• Data sources include
• Surveys where there are a large number of respondents (esp where you have
used a Likert scale)
• Observations (counts of numbers and/or coding data into numbers)
• Secondary data (government data; SATs scores etc)
• Analysis techniques include hypothesis testing, correlations and
cluster analysis
Independent Variable: The variable in the study
under consideration. The cause for the outcome
for the study.
Dependent Variable: The variable being affected

by the independent variable. The effect of the
study
y = f(x)
Which is which here?
Bias: When observations favor some individuals
in the population over others.
Confounding Variables: When the effects of two or more

variables cannot be separated.
Extraneous Variables: Any variable that has an effect on the

dependent variable. any variable that you're not investigating
that can potentially affect the dependent variable of your
research study.
Need to identify and minimize these variables.
e.g., Erosion potential as a function of clay content. rainfall intensity, vegetation & duration
would be considered extraneous variables.
Key Factors for High Quality
Experimental Design
Data should not be contaminated by poor
measurement or errors in procedure.
Eliminate confounding variables from study or

minimize effects on variables.
Representativeness: Does your sample represent the

population you are studying? Must use random sample
techniques.
What Makes a Good Quantitative
Research Design?
4 Key Elements
1. Freedom from Bias

2. Freedom from Confounding
3. Control of Extraneous Variables
4. Statistical Precision to Test Hypothesis
Precision versus accuracy
"Precise" means sharply defined or measured.

Precision refers to how close measurements of
the same item are to each other.
"Accurate" means truthful or correct. Accuracy
refers to how close a measurement is to the
true or accepted value.
Both Accurate Accurate
and Precise Not precise
Not accurate
But precise
Neither accurate
nor precise
Introduction to Sampling
Sampling is the problem of accurately acquiring

the necessary data in order to form a
representative view of the problem.
This is much more difficult to do than is

generally realized.
Overall Methodology:
* State the objectives of the survey
* Define the target population
* Define the data to be collected
* Define the variables to be determined
* Define the required precision & accuracy
* Define the measurement `instrument'
* Define the sample size & sampling method,
then select the sample
Sampling
Distributions:
When you form a sample you often show it by a
plotted distribution known as a histogram .
A histogram is the distribution of frequency of

occurrence of a certain variable within a
specified range.
NOT A BAR GRAPH WHICH LOOKS VERY SIMILAR

Data Representation
Interpreting quantitative findings
Descriptive Statistics : Mean, median, mode,

frequencies
Error analyses
Mean
• In science the term mean is really the arithmetic
mean
• Given by the equation
1
•X= / n  n
xi
i=1
Or more simply put, the sum of values divided by the number of values
summed
Median
• Consider the set

• 1, 1, 2, 2, 3, 6, 7, 11, 11, 13, 14, 16, 19
• In this case there are 13 values so the median is the
middle value, or (n+1) / 2
• (13+1) /2 = 7
• 1, 1, 2, 2, 3, 6, 7, 11, 11, 13, 14, 16
• In the second case, the mean of the two middle values
is the median or (n+1) /2
(12 + 1) / 2 = 6.5 ~ (6+7) / 2 = 6.5
Mode
The most frequent value in a data set
• 1, 1, 1, 1, 2, 2, 3, 6, 11, 11, 11, 13, 14, 16, 19
• In this case the mode is 1 because it is the most common value
• There may be cases where there are more than one

mode as in this case

• 1, 1, 1, 1, 2, 2, 3, 6, 11, 11, 11, 11, 13, 14, 16, 19
• In this case there are two modes (bimodal) : 1 and 11 because
both occur 4 times in the data set.

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IC 601

• Which you choose will depend on

Quantitative – Numbers, tests, counting,

Qualitative – Words, images, observations,

Experiment: Research situation with at least

Dependent Variable: The variable being affected

Confounding Variables: When the effects of two or more

Extraneous Variables: Any variable that has an effect on the

Eliminate confounding variables from study or

Representativeness: Does your sample represent the

1. Freedom from Bias

"Precise" means sharply defined or measured.

Sampling is the problem of accurately acquiring

This is much more difficult to do than is

A histogram is the distribution of frequency of

NOT A BAR GRAPH WHICH LOOKS VERY SIMILAR

Descriptive Statistics : Mean, median, mode,

• Consider the set

• There may be cases where there are more than one

• Consider the set

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