Counter Balancing

Counterbalancing is a procedure that allows a researcher to control the effects of

nuisance variables in designs where the same participants are repeatedly subjected
to conditions, treatments, or stimuli (e.g., within-subjects or repeated-measures
designs).

Counterbalancing refers to the systematic variation of the order of conditions in a

study, which enhances the study’s interval validity. In the context of experimental
designs, the most common nuisance factors (confounds) to be counterbalanced
are procedural variables (i.e., temporal or spatial position) that can create order
and sequence effects. In quasi-experimental designs, blocking variables (e.g., age,
gender) can also be counterbalanced to control their effects on the dependent
variable of interest, thus compensating for the lack of random assignment and the
potential confounds due to systematic selection bias.

Categorizing a continuous variable

Continuous variables are often encountered in life. We measure age, blood pressure and many other
things. In medicine, such measurements are often used to assess risk or prognosis or to select a therapy.
However, the question of how best to use information from continuous variables is relevant in many
areas.

In many research fields, particularly those that mostly use ANOVA, a common practice is to
categorize continuous predictor variables so they work in an ANOVA. This is often done with median
splits—splitting the sample into two categories—the “high” values above the median and the “low”
values below the median.

To relate an outcome variable to a single continuous variable, a suitable regression model is required. A
simple and popular approach is to assume a linear effect, but the linearity assumption may be violated.
Alternatively, researchers typically apply cutpoints to categorize the variable, implying regression
models with step functions.
Longitudinal Vs Cross sectional research

A cross-sectional study is defined as an observational research type that analyzes data of

variables collected at one given point of time across a sample population. ... This variable
remains constant throughout the cross-sectional study.

A longitudinal study is an observational research method in which data is gathered for the
same subjects repeatedly over a period of time. Longitudinal research projects can extend
over years or even decades. In a longitudinal cohort study, the same individuals are observed
over the study period.

Longitudinal studies differ from one-off, or cross-sectional, studies. The main difference is
that cross-sectional studies interview a fresh sample of people each time they are carried out,
whereas longitudinal studies follow the same sample of people over time.
4a) Various methods f selection of samples
 Nominal Scale

Nominal scales are adopted for non-quantitative (containing no numerical

implication) labelling variables which are unique and different from one another.

Types of Nominal Scales:

1. Dichotomous: A nominal scale that has only two labels is called

‘dichotomous’; for example, Yes/No.
2. Nominal with Order: The labels on a nominal scale arranged in an
ascending or descending order is termed as ‘nominal with
order’; for example, Excellent, Good, Average, Poor, Worst.
3. Nominal without Order: Such nominal scale which has no sequence, is
called ‘nominal without order’; for example, Black, White.

Ordinal Scale

The ordinal scale functions on the concept of the relative position of the objects
or labels based on the individual’s choice or preference.

For example, At Amazon.in, every product has a customer review section where
the buyers rate the listed product according to their buying experience, product
features, quality, usage, etc.

The ratings so provided are as follows:

1. 5 Star – Excellent
2. 4 Star – Good
3. 3 Star – Average
4. 2 Star – Poor
5. 1 Star – Worst

Interval Scale

An interval scale is also called a cardinal scale which is the numerical labelling
with the same difference among the consecutive measurement units. With the
help of this scaling technique, researchers can obtain a better comparison
between the objects.

For example; A survey conducted by an automobile company to know the

number of vehicles owned by the people living in a particular area who can be its
prospective customers in future. It adopted the interval scaling technique for the
purpose and provided the units as 1, 2, 3, 4, 5, 6 to select from.
 In the scale mentioned above, every unit has the same difference, i.e., 1, whether
it is between 2 and 3 or between 4 and 5.

Ratio Scale

One of the most superior measurement technique is the ratio scale. Similar to an
interval scale, a ratio scale is an abstract number system. It allows measurement
at proper intervals, order, categorization and distance, with an added property of
originating from a fixed zero point. Here, the comparison can be made in terms of
the acquired ratio.

For example, A health product manufacturing company surveyed to identify the

level of obesity in a particular locality. It released the following survey
questionnaire:
Select a category to which your weight belongs to:

Less than 40 kilograms

1. 40-59 Kilograms
2. 60-79 Kilograms
3. 80-99 Kilograms
4. 100-119 Kilograms
5. 120 Kilograms and more

Other Scaling Techniques

Scaling of objects can be used for a comparative study between more than one
objects (products, services, brands, events, etc.). Or can be individually carried
out to understand the consumer’s behaviour and response towards a particular
object.

Following are the two categories under which other scaling techniques are placed
based on their comparability:
Comparative Scales

For comparing two or more variables, a comparative scale is used by the

respondents. Following are the different types of comparative scaling techniques:

Paired Comparison

A paired comparison symbolizes two variables from which the respondent needs
to select one. This technique is mainly used at the time of product testing, to
facilitate the consumers with a comparative analysis of the two major products in
the market.

To compare more than two objects say comparing P, Q and R, one can first
compare P with Q and then the superior one (i.e., one with a higher percentage)
with R.

For example, A market survey was conducted to find out consumer’s preference
for the network service provider brands, A and B. The outcome of the survey was
as follows:
Brand ‘A’ = 57%
Brand ‘B’ = 43%
Thus, it is visible that the consumers prefer brand ‘A’, over brand ‘B’.

Rank Order

In rank order scaling the respondent needs to rank or arrange the given objects
according to his or her preference.

For example, A soap manufacturing company conducted a rank order scaling to

find out the orderly preference of the consumers. It asked the respondents to
rank the following brands in the sequence of their choic

Constant Sum

It is a scaling technique where a continual sum of units like dollars, points, chits,
chips, etc. is given to the features, attributes and importance of a particular
product or service by the respondents.

For example, The respondents belonging to 3 different segments were asked to

allocate 50 points to the following attributes of a cosmetic product ‘P’:

Q-Sort Scaling

Q-sort scaling is a technique used for sorting the most appropriate objects out of
a large number of given variables. It emphasizes on the ranking of the given
objects in a descending order to form similar piles based on specific attributes.

It is suitable in the case where the number of objects is not less than 60 and more
than 140, the most appropriate of all ranging between 60 to 90.

t is a graphical rating scale where the respondents are free to place the object at a
position of their choice. It is done by selecting and marking a point along the
vertical or horizontal line which ranges between two extreme criteria.

For example, A mattress manufacturing company used a continuous rating scale

to find out the level of customer satisfaction for its new comfy bedding. The
response can be taken in the following different ways (stated as versions here):
 Likert Scale: In the Likert scale, the researcher provides some statements and
ask the respondents to mark their level of agreement or disagreement over these
statements by selecting any one of the options from the five given alternatives.
For example, A shoes manufacturing company adopted the Likert scale technique
for its new sports shoe range named Z sports shoes. The purpose is to know the
agreement or disagreement of the respondents.
For this, the researcher asked the respondents to circle a number representing
the most suitable answer according to them, in the following representation:

1. 1 – Strongly Disagree
2. 2 – Disagree
3. 3 – Neither Agree Nor Disagree
4. 4 – Agree
5. 5 – Strongly Agree
6. Semantic Differential Scale: A bi-polar seven-point non-comparative
rating scale is where the respondent can mark on any of the seven points
for each given attribute of the object as per personal choice. Thus,
depicting the respondent’s attitude or perception towards the object.

For example, A well-known brand for watches, carried out semantic

differential scaling to understand the customer’s attitude towards its
product. The pictorial representation of this technique is as follows:
Stapel Scale: A Stapel scale is that itemized rating scale which measures the
response, perception or attitude of the respondents for a particular object
through a unipolar rating. The range of a Stapel scale is between -5 to +5
eliminating 0, thus confining to 10 units.

For example, A tours and travel company asked the respondent to rank their
holiday package in terms of value for money and user-friendly interface as
follows:

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