Briefly describe and compare internal and external validity. In your answer, be
ID: 3221430 • Letter: B
Question
Briefly describe and compare internal and external validity. In your answer, be sure to include for each type: (a) a definition, (b) the critical issue it addresses, and (c) whether it is higher or lower in experimental versus nonexperimental (field) research List the treats to internal validity. Give a brief explanation of each of the treats Compare random selection and random assignment. In your response, be sure to define each term and an explanation for how they differ Explain the role of inferential statistics in research design and hypothesis testing. Give a definition of statistical Type I and Type II errors, and statistical power In your opinion, should all research be held strictly to the criterion of generalizability? Explain why or why notExplanation / Answer
Internal validity is the extent to which you are able to say that no other variables except the one you are studying caused the result.
Critical issues :- History, Maturation(passage of time), Testing, Instrumentation, Statistical regression, research reactivity, Selection biases, Attrition(Experimental mortality) etc.
External validity is the extent to which the results of a study can be generalised to the world at large.
Critical issues :- Pre-test treatment interaction, Multiple treatment interference, Selection treatment interaction, Specificity of variables, Operational definition of the treatment, Operational definition of the dependent
variable, Treatment diffusion and inconsistencies etc.
If a study has low internal validity, then we must conclude we have little or no evidence of causality. On the other hand, external validity is used for Sampling and survey research.
Random selection refers to how the sample is drawn from the population as a whole, while Random assignment refers to how the participants are then assigned to either the experimental or control groups. It is possible to have both random selection and random assignment in an experiment.
Inferential statistics are used to make generalizations from a sample to a population. For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
A Type I error is a type of error that occurs when a null hypothesis is rejected although it is true. The error accepts the alternative hypothesis, despite it being attributed to chance.
A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false.
The power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. Statistical power is inversely related to the probability of making a Type II error.
In my opinion, all research should not be held strictly to be criterion of generalizability. Because, in some cases, there may be use some biased sample, then generalizability should be avoided. As an example, when boilogical data of a drug will be taken then,dose of the drug will not be same for an old person, a child and an adult.
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