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Validity

Introduction to Validity. Validity: the best available approximation to the truth of a given proposition, inference, or conclusion.Only propositions can be said to be valid.It is a proposition, inference or conclusion that can have validity.. . Cause-effect construct. Program-outcomes relationship

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Validity

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    1. Validity In Research

    2. Introduction to Validity Validity: the best available approximation to the truth of a given proposition, inference, or conclusion. Only propositions can be said to be valid. It is a proposition, inference or conclusion that can have validity.

    4. Four Types of Validity External Internal Construct Conclusion

    5. External Validity The degree to which the conclusions in your study would hold for other persons in other places and at other times.

    6. Two approaches to evidence for generalization Sampling Model Proximal Similarity

    7. Sampling Model Identify the population to generalize to. Draw a fair sample from that population and conduct your research with the sample. Generalize your results back to the population.

    8. Proximal Similarity Think about different generalizability contexts and develop a theory about which contexts are like our study and which are less so. Generalize

    9. Threats to External Validity An explanation of how you might be wrong in making a generalization.

    10. Three majors Threats people places times

    11. Improving External Validity Do a good job of drawing a sample from a population. Use random selection assure that respondents participate in your study and that you keep your dropout rates low

    12. Improving External Validity Use the theory of proximal similarity more effectively. Do your study in a variety of places, with different people and at different times.

    13. Internal Validity The approximate truth about inferences regarding cause-effect or causal relationships.

    14. Internal Validity Relevant only in studies that try to establish a causal relationship. Relevant to the specific study in question.

    15. Key issue in Internal Validity Establish a causal relationship Temporal precedence-show your cause happened before your effect. Covariation of the cause and effect-show that you have some type of relationship. No plausible alternative explanations-”rule out” the plausible alternative explanations.

    16. Threats to Internal Validity Single Group Threats Multiple-Group Threats Social Interaction Threats

    17. Construct Validity Refers to the degree to which inferences can legitimately be made from the operationalizations in your study to the theorectical constructs on which those operationalizations were based. An assessment of how well you translated your ideas or theories into actual programs or measures. An assessment of how well you translated your ideas or theories into actual programs or measures.An assessment of how well you translated your ideas or theories into actual programs or measures.

    18. Threats to Construct Validity Inadequate Preoperational Explication of Constructs Mono-Operation Bias Mono-Method Bias Interaction of Different Treatments Interaction of Testing and Treatment Restricted Generalizability Across Constructs Confounding Constructs and Levels of ConstructsConfounding Constructs and Levels of Constructs

    19. Threats to Construct Validity Confounding Constructs and Levels of Constructs

    20. Social Threats to Construct Validity Hypothesis Guessing Evaluation Apprehension Experimenter Expectancies

    21. Conclusion Validiy The degree to which conclusions we reach about relationships in our data are reasonable.

    22. Two possible conclusions there is a relationship in your data there is no relationship in your data

    23. Difference between Internal and Conclusion Both pertain to causal relationships, but conclusion is only concerned with whether there is a relationship.

    24. Threats to Conclusion Validity measures of observations have low reliability the relationship is a weak one did not collect enough information to see the relationship even if it is there.

    25. Improving Conclusion Validity Good Statistical Power-at least 80 chances out of 100 of finding a relationship when there is one.(0.8 in value) Good Reliability-do a good job when constructing measurement instruments. Good Implementation-train program operators and standardize the protocols for administering the program.

    26. Four Components of Statistical Power Sample size - number of units accessible to study effect size - salience of the treatment relative to the noise in measurement alpha level - odds that the observed result is due to chance power - the odds that you will observe a treatment effect when it occurs

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