Introduction of basics of Research Methodology

 Meaning of Research
 Characteristics of Research
 Objective of Research
 Research Types
 Research Approaches
 Research Process
Research Methodology by- Amol Narayane 2

 According to Redman and Mory (1923), research is a “systematized
effort to gain new knowledge”.
 According to Clifford Woody (Kothari, 1988), research comprises
“defining and redefining problems, formulating hypotheses or
suggested solutions; collecting, organizing and evaluating data;
making deductions and reaching conclusions; and finally, carefully
testing the conclusions to determine whether they fit the formulated
hypotheses”.
 Research is an original addition to the available knowledge, which
contributes to its further advancement. It is an attempt to pursue truth
through the methods of study, observation, comparison and
experiment. Research is the search for knowledge, using objective
and systematic methods to find solution to a problem.
 Research in simple terms refers to search for knowledge. It is a
scientific and systematic search for information on a particular topic
or issue in order to find out particular solution.

 Research is a systematic and critical investigation into phenomenon.
 It adopts scientific method i.e. based on observable experience or
empirical evidence.
 Research is directed towards finding answers to the pertinent
questions and solutions to problems.
 It emphasizes the development of generalization, principles or
theories.
 It is not mere compilation but a purposive investigation.
 It aims at describing, interpreting and explaining a phenomenon.
 It is objective and logical. It applies possible tests to validate the
measuring tools and the conclusions obtained.
 The selection of techniques of collecting information, sampling plans
and data analysis techniques must be supported by a logical
justification.

 Review or synthesize existing knowledge .
 Investigate existing situations or problems .
 Provide solutions to problems .
 Explore and analyze more general issues .
 Construct or create new procedures or systems .
 Explain new phenomenon .
 Generate new knowledge .
 …or a combination of any of the above !
(Collis & Hussey, 2003)

 Pure Research: Pure research (also known as
“basic” or “fundamental” research) is exploratory in
nature and is conducted without any practical end-
use in mind. It is driven by gut instinct, interest,
curiosity or intuition, and simply aims to advance
knowledge and to identify/explain relationships
between variables. However, as the term
“fundamental” suggests, pure research may provide a
foundation for further, sometimes applied research.
Research Methodology by- Amol Narayane
6

 Applied Research : In general, applied research
is not carried out for its own sake but in order to
solve specific, practical questions or problems. It
tends to be descriptive, rather than exploratory
and is often based upon pure research.
 Quantitative Research: It reflects the
philosophy that everything can be described
according to some type of numerical system. It is
usually based upon numerical measurements and
thus tends to use numbers and statistical methods
as key research indicators and tools. Validity and
reliability can be measured numerically using
statistical tests.

 Qualitative Research: It is primarily an exploratory
research. It is used to gain an understanding of
underlying reasons, opinions, and motivations. It
provides insights into the problem or helps to develop
ideas or hypotheses for potential quantitative
research. Qualitative Research is also used to
uncover trends in thought and opinions, and dive
deeper into the problem.
 Exploratory Research: Conducted in order to
determine the nature of the problem, this type of
research is not intended to provide conclusive
evidence, but helps us to have a better understanding
of the problem. When conducting exploratory
research, the researcher ought to be willing to change
his/her direction as a result of revelation of new data
& new insights.

 Descriptive Research: Descriptive research is a
study designed to depict the participants in an
accurate way. More simply put, descriptive research
is all about describing people who take part in the
study.
 Causal Research: The investigation into an issue or
topic that looks at the effect of one thing or variable
on another. For example, causal research might be
used in a business environment to quantify the effect
that a change to its present operations will have on its
future production levels to assist in the business
planning process.

 Longitudinal Research: In a longitudinal study,
researchers conduct several observations of the
same subjects over a period of time, sometimes
lasting many years. The benefit of a longitudinal study
is that researchers are able to detect developments
or changes in the characteristics of the target
population at both the group and the individual level.
 Cross-Sectional Research: Cross-sectional studies
make comparisons at a single point in time, it can
compare different population groups at a single point
in time. Example: Measure cholesterol levels in daily
walkers across two age groups, over 40 and under
40, and compare these to cholesterol levels among
non-walkers in the same age groups.

 Inductive Approach : Inductive research moves
from particular situations to make or infer broad
general ideas/theories.
 Deductive Approach: Deductive research moves
from general ideas/theories to specific, particular
situations. (Image source: toknow-11.wikispace.com)

 Basic/Applied Research Approach: The primary
aim of Basic Research is to improve knowledge
generally, without any particular applied purpose in
mind at the outset. Applied Research is designed
from the start to apply its findings to a particular
situation.
 Quantitative/Qualitative research approach:
The emphasis of Quantitative research is on collecting
and analyzing numerical data; it concentrates on
measuring the scale, range, frequency etc. of
phenomena. Qualitative research is more subjective in
nature than Quantitative research and involves
examining and reflecting on the less tangible aspects of
a research subject, e.g. values, attitudes, perceptions.

Formulation of the Research Problem
Extensive Literature Survey(Review)
Development of Working Hypothesis
Preparing the Research Design
Determination of Sampling Design
Collection of Data
Analysis of Data
Hypothesis –Testing
Generalization and Interpretation
Preparation of the Research Report

 Research Design : It is a detailed outline of how
an investigation will take place. A research design
will typically include how data is to be collected,
what instruments will be employed, how the
instruments will be used and the intended means
for analyzing data collected. It is also called as
blueprint of all research activities.
(Reference http://www.businessdictionary.com/definition/research-design.html)

 Clear statement of research design
 Methods and techniques to be utilized for
gathering information
 Approach to be applied for processing and
analyzing the data
 Purpose of the study
 Type of investigation
 Researcher interference
 Study setting
 Time horizon
 Unit of analysis.

Research
Design
Conclusive
Descriptive
Cross
Sectional
Longitudinal
Causal
Exploratory

 Exploratory Research Design: As the name
suggests, the basic objective of the study is to
explore & obtain clarity about the problem
situation. The essential purpose of the study is to
explore and evaluate the diverse and multiple
research opportunities.
 Conclusive Research Design: As the name
suggests, it provides solution to the particular
situation.

 Descriptive Design: It provide a comprehensive
and detailed explanation of the phenomena under
study. Descriptive research design is further
subdivided into two categories.
1. Cross-Sectional: it is carried out at a single
moment in time with different section of
respondents from the population.
2. Longitudinal: a single sample of the identified
population that is studied over a stretched period
of time.

 Causal Research Design: The investigation into
an issue or topic that looks at the effect of one
thing or variable on another.

• Objectivity: – The findings obtained by the research
should be objective. It is possible by allowing more than
one person to agree between the final scores/ conclusion
of the research.
• Reliability: – If the similar research is carried out time
and again in a similar setting it must give similar result. So
the researcher must frame the research questions to
make it reliable and provide similar outcomes.
• Validity: – Any measuring device can be said to be valid
if it measures what it is expected to measure and nothing
else. To make a research valid the questionnaire framed
before research must be framed accordingly.
• Generalization: – The information collected from given
sample must be utilized for providing a general application
to the large group of which the sample is drawn.

The Problem
Objective of the study
Nature of the study
Data sources
Techniques of data collection
Social cultural context
Geographical limit
Basis of selection

 The Problem – The first step involves the proper
selection and then carefully defining the problem. By
this researcher will be enabled to know about what he
has to search, but it should be kept in mind that the
problems selected should not be unmanageable in
nature and also should not be based on the desires.
 Objective of the study – The objective should be
very clear in the mind of the researcher as this will
lead to the clarity of the design and proper response
from the respondents.
 Nature of the study – The research design should
be very much in relation with the nature of the study,
which is to be carried out.

 Data sources – The various sources of the data or
the information should be very clearly stated by the
researcher.
 Techniques of data collection – For the collection of
the required information, it sometimes becomes very
necessary to use some especial techniques.
 Social cultural context – Research design based on
the social cultural concept is prepared in order to
avoid the various study variations.
 Geographical limit – This step becomes a necessity
at this point of time as with the help of this step,
research linked to the hypothesis applies only to
certain number of social groups.

 Basis of selection – Selecting a proper sample
acts as a very important and critical step and this
is done with the help of some mechanics like
drawing a random stratified, deliberate, double
cluster or quota sample etc.

 Measurement means assigning numbers or other
symbols to characteristics of objects according to
certain prespecified rules.
 Scaling : It involves creating a continuum upon
which measured objects are located.
 Scale: A device providing a range of values that
correspond to different values in a concept being
measured.

The four levels or types of scale measurement are nominal,
ordinal, interval, and ratio level scales.
 Nominal Scale: Nominal variables allow for only qualitative
classification.
 That is, they can be measured only in terms of whether the
individual items belong to some distinctively different
categories, but we cannot quantify or even rank order those
categories.
 Typical examples of nominal variables are gender, race, color,
city, etc.

 Ordinal variables allow us to rank order the items
we measure in terms of which has less and which
has more of the quality represented by the
variable, but still they do not allow us to say "how
much more."
 For example, we know that upper-middle is higher
than middle but we cannot say that it is, for
example, 18% higher.

 Interval variables allow us not only to rank order
the items that are measured, but also to quantify
and compare the sizes of differences between
them.
 For example, temperature, as measured in
degrees Fahrenheit or Celsius, constitutes an
interval scale. We can say that a temperature of
40 degrees is higher than a temperature of 30
degrees.

 Ratio variables are very similar to interval
variables; in addition to all the properties of
interval variables, they feature an identifiable
absolute zero point, thus, they allow for
statements such as x is two times more than y.
 Typical examples of ratio scales are measures of
time or space.
 For example, as the Kelvin temperature scale is a
ratio scale, not only can we say that a temperature
of 200 degrees is higher than one of 100 degrees,
we can correctly state that it is twice as high.

The scaling techniques employed in research could be
broadly classified into comparative and non comparative
scale. Comparative scales as its name indicate derive
their name from the fact that all ratings are comparisons
involving relative judgments. It involves direct
comparison of stimulus objects. It contains only ordinal
or rank order properties. It is also otherwise called non
metric scales in that it does not allow any numerical
operations on it against all that could be applied on
interval and ratio scales.

CLASSIFICATIONOF SCALING TECHNIQUES
(Reference: Research methodology by Deepak Chawla. pp. 152)
SCALING
TECHNIQUE
COMPARATIV
E SCALES
PAIRED
COMPARISON
CONSTANT
SUM
RANK ORDER
Q-SORT
Non-
Comparative
Scale
Graphic
Rating Scale
Itemized
Rating Scale
Likert Scale
Semantic
Differential
Stapel

 In comparative scales it is assumed that respondents makes use of a
standard frame of reference before answering the question.
Example: Please rate dominos in comparison to pizza hut on the basis of your
satisfaction level on an 11 point scale.( 1= extremely poor, 11= extremely
good)
1 Variety of menu 1 2 3 4 5 6 7 8 9 10 11
2 Value for money 1 2 3 4 5 6 7 8 9 10 11
3 Delivery time 1 2 3 4 5 6 7 8 9 10 11
4 Food quality 1 2 3 4 5 6 7 8 9 10 11
5 Brand name 1 2 3 4 5 6 7 8 9 10 11

 In a paired comparison scale, a respondent is
presented with two objects and is asked to select
one according to whatever criteria he/she want to
use.
 Example: Suppose a parent wants to offer one of the four
items to the child- chocolate, ice-cream, burger & pizza.
The child is offered to choose one out of two from the six
possible pairs. i.e.., chocolate or burger, chocolate or ice-
cream, chocolate or pizza, burger or ice-cream, burger or
pizza, ice-cream or pizza.
 In general, if there are n items the number of paired
comparison would be (n(n-1)/2).

 In constant sum rating scale, the respondents are
asked to allocate a total of 100 points between
various objects and brands.
 Example: Allocate a total of 100 points among the
various school into which you would like to admit
your child.
Schools Points
Moderns School
Public School
Govt. School
St. Anthony School

 Suppose a modern school is awarded 30
points, whereas public school is awarded 15
points , one can make a statement that
respondent rate modern school twice as high
as public school.
 This type of data is not only in comparative
nature but could also result in ratio scale
measurement.

 In this technique, a rank order procedure is used
in which objects are sorted into different piles
based on their similarity with respect to certain
criteria.
 Suppose there are 100 statements & an individual
is asked to pile them into 5 groups, in such a way,
that the strongly agree statement could be put
into 1 pile, disagree statement could be put into
other pile & so on.

 In the non comparative scales, the
respondents do not make use of any frame of
reference before answering the questions.
 Example: The respondent may be asked to
evaluate the quality of food in a restaurent on
a 5 point scale( 1= very poor, 2= poor and 5=
very good).

In the graphic rating scale the respondent is asked to tick
his preference on the graph.
Example:

 In the itemized rating scale, the respondents are provided with
a scale that has a number of brief descriptions associated with
each of the response categories.
 There is no hard & fast rule as to how many categories should
be used in an itemized rating scale.
 Example: How important is price to you in buying a new car ?
Very important
Relatively important
Neutral
Relatively unimportant
Very unimportant

 This is a multiple item agree-disagree scale. The
respondents are given a certain number of
items(Statements) on which they are asked to
express their degree of agreement/disagreement.
 Example1 : How satisfied were you with our college? (Unipolar scale)
[] Extremely satisfied [] very satisfied []Moderately satisfied [] Slightly satisfied [] not satisfied .
 Example 2 : Price affects consumer purchase decision? (Bipolar scale)
[] Strongly Agree [] Agree [] Neutral [] Disagree [] Strongly Disagree

 In a semantic differential scale, a respondent is required
to rate each attitude object on a number of five-or-seven
point rating scale.
 Example:
Exciting ___ : ___ : ___ : ___ : ___ : ___ : ___ : Calm
Interesting ___ : ___ : ___ : ___ : ___ : ___ : ___ : Dull
Simple___ : ___ : ___ : ___ : ___ : ___ : ___ Complex
Passive ___ : ___ : ___ : ___ : ___ : ___ : ___ Active

 The stapel scale is used to measure the direction and intensity of
an attitude.
 The scale generally has 1o categories involving numbering -5 to +5
without a neutral point and is usually presented in a vertical form.
 Example: ( Quality of food of restaurant)
+5
+4
+3
+2
+1
Quality of food
-1
-2
-3
-4
-5

 Hypothesis: It is an assumption or a statement
that may or may not be true.
 Types Of Hypothesis: There are two types of
hypothesis. Null Hypothesis and Alternative
Hypothesis.
Types of
Hypothesis
Null
Hypothesis
Alternative
Hypothesis

 Null Hypotheses (H0): Null hypotheses are proposed with
the intent of receiving a rejection. These are denoted as
H0.
 Example: If we want to show that the sales &
advertisement expenditure are related, we formulate the
null hypothesis that they are not related.
 Alternative Hypothesis: Rejection of null hypotheses leads
to the acceptance of alternative hypotheses. The
alternative hypotheses can cover a whole range of value
rather than a single point. It is apposite to null
hypotheses. These are denoted by H1.
 Example: If we want to show that the sales &
advertisement expenditure are related, we formulate the
alternative hypothesis that they are related.

 There are two types of error. Type-I error( α-error) & Type-II
error( β-error).
 Type-I error( α-error): When the null hypotheses is right
but researcher reject it, type-I error( α-error) occurs.
 Type-II error( β-error) : When the null hypotheses is wrong
but researcher accept it, type-ii error( β-error) occurs.

 Following steps are followed in testing of a
hypothesis:
Setting up of a hypothesis
Setting up of a suitable significance level
Determination of a test statistics
Determination of critical region
Computing the value of test statistic
Making Decision

 Setting up of a hypothesis : First step is to establish the
hypothesis to be tested. Set null and alternative
hypothesis.
 Setting up of a suitable significance level : The level of
significance denotes the probability of rejecting the null
hypothesis when it is true. It is denoted by α. (α = 0.05 is
generally accepted level of significance).
 Determination of a test statistics: The next step is to
determine a suitable test statistics & its distribution.
 Determination of critical region : Before a sample is drawn
from the population, it is very important to specify the
value of test statistic that will lead to acceptance or
rejection of null hypothesis. The one that leads to the
rejection of null hypothesis is called the critical region.

 Computing the value of test statistic: The next
step is to compute the value of the test statistic
based upon a sample of size n. once the value of
test statistic is computed, one need to examine
whether sample results fall in the critical region or
in the acceptance region.
 Making Decision: If the hypothesis is being tested
at α = 0.05 level of significance, it would be
rejected if the observed results have a probability
less than 0.05, otherwise accept it.

 Before we get into the details of various issues
pertaining to sampling, it would be appropriate to
discuss some of the sampling concepts.
 Population: It refers to any group of people or
objects that form the subject of study in a particular
survey. For example , the number of full-time MBA
students in a business school could form one
population.
 Element: An element comprises a single member of
the population. In the example out of 500 full-time
MBA students , 200 MBA students will be an element
of the population.

 Sampling Frame: Sampling frame comprises all the
element of a population with proper identification that
is available to us for selection at any stage of
sampling.
 Sample: It is a subset of the population. It comprises
only some elements of the population.
 Sampling Unit: A single member of a particular
sample is called sampling unit. For example, if a
sample of 50 patients is taken from a hospital to
understand their perception about the services of the
hospital, each of the 50 patients is a sampling unit.

 Sampling: It is a process of selecting an adequate
number of elements from the population.
 Census ( or complete enumeration) : An
examination of each and every element of the
population is called census or complete enumeration.

 Sampling design refers to the process of
selecting sample from a population. There are two
types of sampling design- probability sampling
design & non-probability sampling design.
Probability sampling designs are used in
conclusive research. Non-probability sampling
designs are used in exploratory research.

Sampling Design
Probability
Sampling
Simple random sampling
Systematic sampling
Stratified random
sampling
Cluster sampling
Non-probability
Sampling
Convenience
Judgmental
Snowball
Quota

 Probability Sampling design: researcher has a
significant measure of control over who is selected
and on the selection methods for choosing them. In
Probability Sampling, each & every element of the
population has a known chance of being selected in
the sample.
 Simple Random Sampling: selection at random by
the researchers from a choice of subjects.
 Systematic Sampling: selecting by the researchers
at numbered intervals, e.g. every one person in five in
the target group.

 Stratified Sampling: Stratified random sampling
involves dividing the entire population into strata
which are mutually exclusive and collectively
exhaustive. sampling within particular sections of the
target groups.
 Example : you target a specific number of people
based on the percentage of the total group that share
the same characteristics. So, for example, in a study
of an organization that had 50 supervisors & 800
labourers, a10% representative sample of this
population would target 5 supervisors & 80 labourers
to interview.

 Cluster Sampling: In a cluster sampling, the entire
population is divided into various clusters in such a
way that the elements within the clusters are
heterogeneous, but there is a homogeneity between
the clusters.
 Cluster sampling is useful when population under a
survey are widely dispersed and drawing a simple
random sample may be impractical.

 A core characteristic of non-probability sampling
techniques is that samples are selected based on the
subjective judgment of the researcher, rather than
random selection (i.e., probabilistic methods), which
is the cornerstone of probability sampling techniques.
 Advantages:
 Possibility to reflect the descriptive comments about
the sample
 Cost-effectiveness and time-effectiveness compared
to probability sampling
 Effective when it is unfeasible or impractical to
conduct probability sampling

 Convenience sampling is generally known as
careless, unsystematic, accidental or opportunistic
sampling. The sample is selected according to the
convenience of the sample. The researcher selects
certain units convenient to him. It requires no pre-
planning for the selection of items. Convenience
sampling ensures convenience in respect of
availability of source list and accessibility of the units.
Despite being unscientific, a large number of samples
are convenient sampling.

 A convenience sampling is used in the following
situations:
 when the universe is not clearly defined
 where sampling unit is not clear and
 when a complete source list is not available.

 Judgmental sampling: In judgmental sampling , the
judgment of an expert is used to identify a
reprehensive sample.
 Snowball Sampling: It is used when it is difficult to
identify the members of the desired population. Under
this design each respondent, after being interviewed,
is asked to identify one or more respondent in the
field. You start with one person – who then suggests
another & so on
 Example: Deep-sea divers.

 Quota sampling: The Quota Sampling is yet another
non-probability sampling method wherein the
population is divided into a mutually exclusive, sub-
groups from which the sample items are selected on
the basis of a given proportion.
 .For Example, Suppose an interviewer is told to
interview 250 people living in certain geographical
areas. Out of which 100 males, 100 females and 50
children are to be interviewed. Within these quotas,
the interviewer can select any person on the basis of
his personal judgment.

 Data collection is a process of collecting information from all
the relevant sources to find answers to the research problem,
test the hypothesis and evaluate the outcomes. Data collection
methods can be divided into two categories: secondary
methods of data collection and primary methods of data
collection. There are two types of data, primary data &
secondary data.
 Primary Data: This type of information is obtained directly
from first hand sources by means of interview, questionnaire,
observation etc.
 Secondary Data: It refers to the data collected by someone
other than user i.e. the data already available. Common source
of secondary data is published or unpublished data.

Data collection
methods
Primary Data
interview
Questionnaire
& Schedule
Observation
Experimentation
Focus group
Secondary Data

 Interview Methods : It can be conducted in person or
over the telephone. Interviews can be grouped into three
main types.
1. Structured: Structured interviews involve the use of
questionnaires based on a predetermined and identical
set of questions. The questions are usually read out by a
researcher in a neutral tone of voice to avoid influencing
or prompting a particular response from a participant..
2. Semi-structured: The interviewer will have a list of
themes and areas to be covered and there may be some
standardized questions, but the interviewer may omit or
add to some of these questions or areas, depending on
the situation and the flow of the conversation.

3. Unstructured Interviews: These are informal
discussions where the interviewer wants to explore
in-depth a particular topic with another person in a
spontaneous way. However, even in unstructured
interviews it is likely that the researcher would have
a pre-decided range of topics to cover in the
discussion.

 QUESTIONNAIRE : Questionnaire is design to collect the data
from large, diverse and widely scattered group of people. Thus, it is a
device for securing answers to questions by using a form which the
respondent himself fills in. This is usually mailed to the respondents
or is administered to a group of people at the same time by giving
them appropriate forms & collecting them later.
 There are five types of questionnaire approaches:
1. On-line (electronic) 2. Postal (printed) 3. Delivery & collection
(printed) 4. Telephone (electronic/printed) 5. Interview face to
face/group (electronic or printed)
 SCHEDULE : The schedule is referred to as a form filled in
during a personal interview in which both the interviewer as well as the
respondent are present. It contains a set of questions which are asked
and then the columns are filled in by an interviewer in a face to face
situation. A person who conduct the interview and filled the
questionnaire is called as enumerator.

 Observation Method : In this method, respondents are
observed either by mechanical device like camera etc or
by human being. Under this method observation becomes
scientific tool and method of data collection.
 Observation as a data collection method can be
structured or unstructured. In structured or systematic
observation, data collection is conducted using specific
variables and according to a pre-defined schedule.
Unstructured observation, on the other hand, is
conducted in an open and free manner in a sense that
there would be no pre-determined variables or objectives.

 Experimentation Method : These are more effective
in establishing cause-and-effect relations. In an
experiment, data collection is done in such a manner
as to permit relatively unmambigons interpretation.
The data collected by the researchers are
representatives of the actual cause-and effect
relations between the two given variables. Further,
one of the variables can be changed (only in a
controlled experimental setting) and the effects of
such changes can be measured on the other. Thus,
experiments are popular among researchers,
especially those who are in scientific fields.

 A focus group is a group discussion on a particular
topic organized for research purposes. This
discussion is guided, monitored and recorded by a
researcher. Focus groups are used for generating
information on collective views, and the meanings
that lie behind those views. They are also useful in
generating a rich understanding of participants'
experiences and beliefs. e.g. consumer topic; political
topic etc

1. Explain the purpose of the questionnaire to all
participants .
2. Keep your questions as simple as possible .
3. Do not use jargon or specialist language (unless the
recipients really prefer and understand it).
4. Phrase each question so that only one meaning is
possible.
5. Avoid vague, descriptive words, such as ‘large’ and
‘small’.
6. Avoid asking negative questions as these are easy
to misinterpret.

7. Only ask one question at a time.
8. Include relevant questions only.
9. Include, if possible, questions which serve as cross-
checks on the answers to other questions.
10. Avoid questions which require participants to
perform calculations.
11. Avoid leading or value-laden questions which imply
what the required answer might be.
12 . Avoid offensive questions or insensitive questions
which could cause embarrassment .

13. Avoid asking ‘difficult’ questions, e.g. where the
respondent may struggle to answer (people hate to
look stupid by not knowing the ‘answer’).
14. Keep your questionnaire as short as possible, but
include all the questions you need to cover your
purposes.
(source: Collis & Hussey, 2003)

 (Category Question) Have you ever been or are you
a student representative? (Tick which)
Yes (currently) Yes (in the past) Never
 (Multiple Choice ) Do you view the money you have
spent on your higher education as any of the
following? If so, tick which.
A luxury An investment A necessity
A gamble A burden A right None of these

 (Scale Question) How would you describe your
parents attitude to higher education at the time you
applied? Please tick one of the options below.
Very Positive Positive Mixed/Neutral Negative
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