KWAME NKRUMAH UNIVEERSITY OF SCIECNCE AND
TECHNOLOGY
FACULTY OF PHARMACY AND PHARMACEUTICAL SCIENCES.
DEPARTMENT OF PHARMACY PRACTICE
Name: Kristabel Abotsi
Index number: 1572117
QUESTION
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What are the types of internal validity?
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What are the threats to internal and external validity and how can they be resolved?
ANSWERS
Internal validity is the degree of confidence that the causal relationship you are testing is not
influenced by other factors or variables. There are several types of internal validity that can
impact the conclusions drawn from a research study and they include:
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Content validity:
determines whether the experiment is best suited for your research methodology.
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Face validity:
shows if the research results accurately represent the research’s aims.
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Criterion validity
examines whether the result of one research matches the outcome of another that uses
the same details
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Construct validity: determines whether your
experiment yields the results you expected.
Some threats to international validity include
1. History: History refers to events that occur during the course of a study that are not
related to the independent variable but may influence the dependent variable. For
example, if a study investigating the effect of a new educational program on student
learning coincides with a major teacher strike, the strike may confound the results of the
study.
2. Maturation: Maturation refers to changes that occur naturally in participants over time
that may influence the dependent variable. For example, in a study investigating the
effect of a new nutrition program on weight loss, participants' weight loss may be
influenced by natural changes in their metabolism or body composition over time.
3. Testing: Testing refers to the potential for participants to improve their performance on a
measure as a result of having taken the test before. For example, if a study uses a pretest/post-test design, participants may improve their performance on the post-test simply
because they are more familiar with the test format.
4. Instrumentation: Instrumentation refers to changes in the measurement instrument or
procedure that may impact the results of a study. For example, if a study measuring
anxiety levels uses two different types of scales at different time points, the results may
be confounded by differences in the scales rather than changes in anxiety levels.
5. Selection bias: Selection bias occurs when the groups being compared are not equivalent
at the outset of a study. For example, if a study comparing the effectiveness of two
different interventions for depression assigns participants to groups based on their
availability or willingness to participate, the groups may differ in important ways that
impact the results of the study.
External validity is the extent to which you can generalize the findings of a study to other
situations, people, settings, and measures.
There are two main types of external validity: population validity and ecological validity.
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Population Validity
Population validity refers to whether you can reasonably generalize the findings from
your sample to a larger group of people (the population).
The selection of the population and how closely the study sample reflects that population are
key factors in population validity. Methods of nonprobability sampling are frequently utilized
for convenience. Using this kind of sampling, the results can only be generalized to populati
ons having similar characteristics to the sample.
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Ecological Validity
Ecological validity refers to whether you can reasonably generalize the findings of a study to
other situations and settings in the ‘real world’.
There are also several threats to external validity, including:
1. Experimental setting: The setting in which the study is conducted may be artificial and
not representative of the real-world conditions in which the variables of interest operate.
2. Reactivity: Participants may behave differently in a research setting than they would in
their normal environment, which can limit the generalizability of the findings.
3. Harthwone Effect: The tendency for participants to change their behaviors simply
because they know they are being studied. For example The participants actively avoid
anxiety-inducing situations for the period of the study because they are conscious of their
participation in the research.
4. Time-related factors: The effects of an intervention or treatment may change over time,
which can limit the generalizability of the findings to other times or contexts.
5. Measurement bias: The measurement tools used in a study may not be appropriate or
accurate for the population of interest, which can limit the generalizability of the
findings.
6. Observer Bias: The characteristics or behaviors of the experimenter(s) unintentionally
influence the outcomes, leading to bias and other demand characteristics.
To address these threats to internal and external validity, researchers can take several steps:
1. Control for extraneous variables: Careful control of extraneous variables through
random assignment and other control procedures can increase internal validity.
2. Use appropriate research design: Selecting a research design that minimizes or
eliminates the effects of confounding variables, such as using a randomized controlled
trial, can increase internal validity.
3. Use representative samples: Sampling from a population that is representative of the
target population can increase external validity.
4. Use multiple settings: Conducting the study in multiple settings can help to increase
external validity by ensuring that the results are not specific to one particular
environment.
5. Use naturalistic settings: Using naturalistic settings that are similar to the real-world
environment in which the variables operate can increase external validity.
6. Replications: These are able to counter almost all threats by enhancing generalizability
to other settings, populations and conditions.
7. Probability sampling: This counters selection bias by making sure everyone in a
population has an equal chance of being selected for a study sample.
8. Recalibration or reprocessing also counters selection bias using algorithms to correct
weighting of factors (e.g., age) within study samples.
REFERENCES
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Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs
for research. Houghton Mifflin.
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Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasiexperimental designs for generalized causal inference. Houghton Mifflin.
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Trochim, W. M. (2006). The research methods knowledge base. Atomic Dog.
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Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis
issues for field settings. Rand McNally