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Original research article, statistical conclusion validity: some common threats and simple remedies.

## Stopping Rules for Data Collection without Control of Type-I Error Rates

## Preliminary Tests of Assumptions

## Regression as a Means to Investigate Bivariate Relations of all Types

## The Bayesian Approach

## Improving the SCV of Research

## Conflict of Interest Statement

## Acknowledgments

This research was supported by grant PSI2009-08800 (Ministerio de Ciencia e Innovación, Spain).

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Keywords: data analysis, validity of research, regression, stopping rules, preliminary tests

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## Statistical Validity

## This article is a part of the guide:

## Browse Full Outline

- 1 Inferential Statistics
- 2.1 Bayesian Probability
- 3.1.1 Significance 2
- 3.2 Significant Results
- 3.3 Sample Size
- 3.4 Margin of Error
- 3.5.1 Random Error
- 3.5.2 Systematic Error
- 3.5.3 Data Dredging
- 3.5.4 Ad Hoc Analysis
- 3.5.5 Regression Toward the Mean
- 4.1 P-Value
- 4.2 Effect Size
- 5.1 Philosophy of Statistics
- 6.1.1 Reliability 2
- 6.2 Cronbach’s Alpha

- Convergent validity - this validity ensures that if the required theory predicts that one measure be correlated with the other, then the statistics confirm this.
- Divergent or Discriminant validity - this validity ensures that if the required theory predicts that one variable doesn't correlate with others, then statistics need to conform this.
- Content validity : This type of validity is important to make sure that the test or questionnaire that is prepared actually covers all aspects of the variable that is being studied. If the test is too narrow, then it will not predict what it claims.
- Face validity : This is related to content validity and is a quick starting estimate of whether the given experiment actually mimics the claims that are being verified. In other words, face validity measures whether or not the survey has the right questions in order to answer the research questions that it aims to answer.
- Conclusion validity: this type of validity ensures that the conclusion that is being reached from the data sets obtained from the experiment are actually right and justified. For example, the sample size should be large enough to predict any meaningful relationships between the variables being studied. If not, then conclusion validity is being violated.
- Internal validity : internal validity is a measure of the inherent relationship between cause and effect that are being studied in the experiment. For example, the controls used in the experiment must be meaningful and strict if the effect of one variable is being studied on another.
- External validity : external validity is all about how to apply the results from this particular experiment to more general populations . External validity tells us whether or not we can generalize the results of this experiment to all other populations or to some populations with particular characteristics.

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Rosenbaum, P. R. (1989). Criterion-related construct validity. Psychometrika, 54 (4), 625-659.

Shepard, L. A. (1993). Evaluating test validity. Review of Research in Education, 19 , 405-450.

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Conclusion validity is essentially whether that relationship is a reasonable one or not, given the data. But it is possible that we will conclude that, while

Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data

Statistical Conclusion Validity(SCV), or just Conclusion Validity is a measure of how reasonable a research or experimental conclusion is. For example

Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable".

For example, the sample size should be large enough to predict any meaningful relationships between the variables being studied. If not, then conclusion

3.Statistical conclusion validity: The conclusion reached or inference drawn about the extent of the relationship between the two variables. For instance, it

External Validity: External validity addresses the issue of being able to generalize the results of your study to other times, places, and persons. For example

In other words, statistical conclusion validity addresses whether inferences about relationships (i.e., whether the independent variable and dependent variable

For example, xy = 0. is the Greek letter 'rho,' which is commonly used to stand for the value of a Pearson correlation coefficient in a population.

What Makes an Experiment Valid? •. Scroll for details. New! Watch ads now so you can enjoy fewer interruptions.