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Books on the topic 'Validity'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Howard, Wainer, Braun Henry I. 1949-, and Educational Testing Service, eds. Test validity. Hillsdale, N.J: L. Erlbaum Associates, 1988.

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2

1941-, Bickman Leonard, ed. Validity & social experimentation. Thousand Oaks, Calif: Sage Publications, 2000.

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3

Jansen, Paulus Gerardus Wilhelmus, 1954-, ed. Validity generalization revisited. Delft, The Netherlands: Delft University Press, 1986.

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4

Kulisch, Ulrich. Computer arithmetic and validity. Berlin: De Gruyter, 2013.

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5

Riezler, Stefan, and Michael Hagmann. Validity, Reliability, and Significance. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02183-1.

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6

Belbin, Stephen. The validity of sampling. London: Chelsea College of Art and Design, 2002.

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7

Riezler, Stefan, and Michael Hagmann. Validity, Reliability, and Significance. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57065-0.

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8

Westerman, Pauline, Jaap Hage, Stephan Kirste, and Anne Ruth Mackor, eds. Legal Validity and Soft Law. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77522-7.

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9

Tyler, Forrest B., Deborah Ridley Brome, and Janice E. Williams. Ethnic Validity, Ecology, and Psychotherapy. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-0603-8.

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10

Roussos, Louis A. LSAT item-type validity study. Newtown, PA: Law School Admission Council, 1998.

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11

N, Robins Lee, and Barrett James E. 1934-, eds. The Validity of psychiatric diagnosis. New York: Raven Press, 1989.

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12

Indian Institute of Comparative Law., ed. Legal validity and civil disobedience. Jaipur: Indian Institute of Comparative Law, 2000.

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13

Weekes, Tom. Property notices: Validity and service. England: Jordan Pub., 2008.

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14

Tyler, Forrest B. Ethnic validity, ecology, and psychotherapy. New York: Plenum, 1991.

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15

Weekes, Tom. Property notices: Validity and service. 2nd ed. Bristol, England: Jordan Pub., 2011.

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16

Uebersax, John. Validity inferences from interobserver agreement. Santa Monica, CA: Rand, 1989.

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17

Weekes, Tom. Property notices: Validity and service. 2nd ed. Bristol, England: Jordan Pub., 2011.

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18

Hull, Ian M. Challenging the validity of wills. Scarborough, Ont: Carswell, 1996.

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19

Roussos, Louis A. LSAT item-type validity study. Newtown, PA: Law School Admission Council, 1998.

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20

1927-, McGrath Joseph Edward, ed. Validity and the research process. Beverly Hills: Sage Publications, 1985.

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21

J, Sbordone Robert, and Long Charles J. 1935-, eds. Ecological validity of neuropsychological testing. Delray Beach, Fla: GR Press/St. Lucie Press, 1996.

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22

1952-, Murphy Kevin R., ed. Validity generalization: A critical review. Mahwah, NJ: Lawrence Erlbaum Associates, 2003.

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23

Nancy, Nihan, Washington State Transportation Center, Washington (State). Dept. of Transportation., and United States. Federal Highway Administration., eds. Detector data validity: Final report, Research Project GC 8286, Task 24, Detector Data Validity. [Olympia, Wash.]: Washington State Dept. of Transportation, Planning, Research and Public Transportation Division, in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1990.

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24

Franzen, Michael D. Reliability and validity in neuropsychological assessment. 2nd ed. New York: Kluwer Academic/Plenum Publishers, 2000.

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25

W, Lissitz Robert, ed. The concept of validity: Revisions, new directions, and applications. Charlotte, NC: Information Age Pub., 2009.

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26

Gamache, LeAnn M. A procedure to adjust individual scores for construct invalidity. Iowa City, Iowa: American College Testing Program, 1988.

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27

W, Lissitz Robert, ed. The concept of validity: Revisions, new directions, and applications. Charlotte, NC: Information Age Pub., 2009.

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28

Franzen, Michael D. Reliability and Validity in Neuropsychological Assessment. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3224-5.

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29

Marty-Nelson, Elena. Testamentary capacity and validity of wills. [Washington, D.C.]: Tax Management Inc., 2000.

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30

Cizek, Gregory J. Validity. Routledge, 2020. http://dx.doi.org/10.4324/9780429291661.

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31

Streiner, David L., Geoffrey R. Norman, and John Cairney. Validity. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199685219.003.0010.

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In order for a scale to be useful, the user must be able to draw accurate conclusions about the presence or absence of the attribute being measured. This is the domain of validity. What validity is and how it is assessed has changed greatly over the past 40 years, although many who develop or validate scales are unaware of this. This chapter discusses what is meant by validity and how it is assessed. The major points are that: (1) validity is not a property of the test, but may change depending on the sample and the conditions under which the test is given, and (2) there are not different ‘types’ of validity—they are all various aspects of construct validity. The chapter also describes different types of studies that can establish construct validity.

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32

Cizek, Gregory J. Validity. Taylor & Francis Group, 2020.

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33

Tisha, Author. #Validity. Independently Published, 2020.

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34

Woods, Brian. Validity. Independently Published, 2019.

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35

Cizek, Gregory J. Validity. Taylor & Francis Group, 2020.

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36

Validity. Outskirts Press, Incorporated, 2014.

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37

McCleary, Richard, David McDowall, and Bradley J. Bartos. Internal Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0007.

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Chapter 7 begins with an outline and description of five threats to internal validity common to time series designs: history, maturation, instrumentation, regression, and selection. Given the fundamental role of prediction in the modern scientific method, scientific hypotheses are necessarily causal. After an outline of the evolving definition of “causality” in the social sciences, contemporary Rubin causality or counterfactual causality is introduced. Under the assumption that subjects were randomly assigned to the treatment and control groups, Rubin’s causal model allows one to estimate the unobserved causal parameter from observed data. Control time series are chosen so as to render plausible threats to internal validity implausible. An appropriate control time series may not exist, however, an ideal time series may be possible to construct. Synthetic control group models construct a control time series that optimally recreates the treated unit’s preintervention trend using a combination of untreated donor pool units.

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38

McCleary, Richard, David McDowall, and Bradley J. Bartos. Construct Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0008.

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Chapter 8 focuses on threats to construct validity arising from the left-hand side time series and the right-hand side intervention model. Construct validity is limited to questions of whether an observed effect can be generalized to alternative cause and effect measures. The “talking out” self-injurious behavior time series, shown in Chapter 5, are examples of primary data. Researchers often have no choice but to use secondary data that were collected by third parties for purposes unrelated to any hypothesis test. Even in those less-than-ideal instances, however, an optimal time series can be constructed by limiting the time frame and otherwise paying attention to regime changes. Threats to construct validity that arise from the right-hand side intervention model, such as fuzzy or unclear onset and responses, are controlled by paying close attention to the underlying theory. Even a minimal theory should specify the onset and duration of an impact.

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39

McCleary, Richard, David McDowall, and Bradley J. Bartos. External Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0009.

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A threat to external validity is any factor that limits the generalizability of an observed result. Unlike all threats to statistical conclusion and internal validities and some threats to construct validity, threats to external validity cannot ordinarily be controlled by design. Nor is there any disagreement on how threats to external validity should be controlled. In most instances, it can only be controlled by replication?—across subjects, situations and time frames. This seldom happens, unfortunately, because the academic incentive structure discourages replication. The contemporary “reproducibility crisis” was spurred by a collaborative group of social scientists attempting to replicate one hundred experimental and correlational studies published in three mainstream psychology journals. Sixty percent of replications failed to reproduce the published effect. Failures to control for threats to external validity that stem from uncontrolled variations in persons, situations, and time frames, parsimosniously explain the failure rate in this replication study.

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40

Munzer, Stephen. Legal Validity. Springer London, Limited, 2012.

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41

Wainer, Howard, and Henry I. Braun, eds. Test Validity. Routledge, 2013. http://dx.doi.org/10.4324/9780203056905.

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42

Test Validity. Routledge, 2013.

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43

Wainer, Howard, and Henry I. Braun. Test Validity. Taylor & Francis Group, 2013.

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44

Pavin. Final Validity. America Star Books, 2009.

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45

Wainer, Howard, and Henry I. Braun. Test Validity. Taylor & Francis Group, 2013.

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46

Legal Validity. Springer, 2012.

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47

Ingeborg, Schwenzer, Hachem Pascal, and Kee Christopher. Part IV Validity, 15 General Remarks on Validity. Oxford University Press, 2012. http://dx.doi.org/10.1093/law/9780199572984.003.0015.

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48

Spence, Sarah, ed. Validity in Interpretation. Yale University Press, 2017. http://dx.doi.org/10.12987/9780300157390.

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49

Jay, Martin. Genesis and Validity. University of Pennsylvania Press, 2021. http://dx.doi.org/10.9783/9780812299991.

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50

McCleary, Richard, David McDowall, and Bradley J. Bartos. Statistical Conclusion Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0006.

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Chapter 6 addresses the sub-category of internal validity defined by Shadish et al., as statistical conclusion validity, or “validity of inferences about the correlation (covariance) between treatment and outcome.” The common threats to statistical conclusion validity can arise, or become plausible through either model misspecification or through hypothesis testing. The risk of a serious model misspecification is inversely proportional to the length of the time series, for example, and so is the risk of mistating the Type I and Type II error rates. Threats to statistical conclusion validity arise from the classical and modern hybrid significance testing structures, the serious threats that weigh heavily in p-value tests are shown to be undefined in Beyesian tests. While the particularly vexing threats raised by modern null hypothesis testing are resolved through the elimination of the modern null hypothesis test, threats to statistical conclusion validity would inevitably persist and new threats would arise.

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