An experiment is used to infer causality by manipulating independent variables and measuring their effects on dependent variables while controlling for extraneous variables. Key elements of an experiment include independent variables, dependent variables, test units, and controlling or accounting for extraneous variables. Experiments aim to establish internal validity by ensuring the independent variable is truly causing changes in the dependent variable, and external validity by generalizing results beyond the experimental conditions. Experimental designs include pre-experimental, quasi-experimental, true experimental, and statistical designs which aim to reduce threats to validity through techniques like randomization and controlling extraneous variables.

1. What is an Experiment?
 An experiment is generally used to
infer a causality. In an experiment, a
researcher actively manipulates one
or more causal variables and
measures their effects on the
dependent variable of interest.

2. Necessary Conditions for Making
Causal Inferences
 Concomitant variation
 Time order of occurrence of variables
 Absence of other possible causal factors

3. Concepts Used in Experiments
 Independent variables: Independent variables
are also known as explanatory variables or
treatments. The levels of these variables are
manipulated (changed) by researchers to
measure their effect on the dependent variable.
 Test units: Test units are those entities on
which treatments are applied.
 Dependent variables: These variables
measures the effect of treatments (independent
variable) on the test units.

4. Concepts Used In Experiments
 Experiment: An experiment is executed when the
researcher manipulates one or more independent
variables and measures their effect on the
dependent variables while controlling the effect of
the extraneous variables.
 Extraneous variables: These are the variables
other than the independent variables which
influence the response of test units to treatments.
Examples: Store size, government policies,
temperature, food intake, geographical location, etc.

5. Validity in Experimentation
 Internal validity: Internal validity tries to
examine whether the observed effect on a
dependent variable is actually caused by the
treatments (independent variables) in question.
 External validity: External validity refers to the
generalization of the results of an experiment.
The concern is whether the result of an
experiment can be generalized beyond the
experimental situations.

6. Factors Affecting Internal Validity of
the Experiment
 History
 Maturation
 Testing
 Instrumentation
 Statistical regression
 Selection bias
 Test unit mortality

7. Factors Affecting External Validity
 The environment at the time of test may be
different from the environment of the real
world where these results are to be generalized.
 Population used for experimentation of the test
may not be similar to the population where the
results of the experiments are to be applied.
 Results obtained in a 5–6 week test may not
hold in an application of 12 months.
 Treatment at the time of the test may be
different from the treatment of the real world.

8. Methods to Control Extraneous
Variables
 Randomization
 Matching
 Use of experimental designs
 Statistical control

9. Environments of Conducting Experiments
 Laboratory Environment - In a laboratory
experiment, the researcher conducts the
experiment in an artificial environment
constructed exclusively for the experiment.
 Field Environment - The field experiment is
conducted in actual market conditions. There is
no attempt to change the real-life nature of the
environment.

10. A Classification of Experimental Designs

11. Pre-experimental design
Pre-experimental designs do not make use of any
randomization procedures to control the extraneous variables.
Therefore, the internal validity of such designs is questionable.
 One-shot case study:
X O
 One-group pre-test–post-test design:
O1 X O2
 Static group comparison:
Group 1 - X O1
Group 2 - O2

12. Quasi-experimental designs
In quasi-experimental design, the researcher can
control when measurements are taken and on
whom they are taken. However, this design lacks
complete control of scheduling of treatment and
also lacks the ability to randomize test units’
exposure to treatments.
 Time series design:
O1 O2 O3 O4 X O5 O6 O7 O8
Contd…..

13. Quasi-experimental designs
 Multiple time series design:
Experimental Group: O1 O2 O3 O4 X O5 O6 O7 O8
Control Group: O′1 O′2 O′3 O′4 O′5 O′6 O′7 O′8

14. True experimental designs
In true experimental designs, researchers can
randomly assign test units and treatments to an
experimental group. Here, the researcher is able
to eliminate the effect of extraneous variables
from both the experimental and control group.
 Pre-test–post-test control group:
Experimental Group: R O1 X O2
Control Group: R O3 O4

15. True experimental designs
 Post-test – only control group design:
Experimental Group: R X O1
Control Group: R O2
 Solomon four-group design:
Experimental Group 1 : R O1 X O2
Control Group 1: R O3 O4
Experimental Group 2: R X O5
Control Group 2: R O6

16. Statistical designs
Statistical designs allow for statistical control and
analysis of external variables.
 Completely randomized design
 Randomized block design
 Latin square design
 Factorial design

17. Thanks