Select a quantitative research study of interest from the literature and address the following components:
Describe the purpose of the study.
Describe the type of research design used (e.g., experimental, quasi-experimental, correlational, causal-comparative).
Describe how reliability and validity were assessed.
Evaluate the reliability of the study.
Evaluate the internal validity of the study.
Evaluate the statistical conclusion validity of the study.
List potential biases or limitations of the study.
Length: 6 pages, not including title and reference pages
References: Include a minimum of 6 scholarly resources
The completed assignment should address all of the assignment requirements, exhibit evidence of concept knowledge, and demonstrate thoughtful consideration of the content presented in the course. The writing should integrate scholarly resources, reflect academic expectations and current APA standards (as required), and include a plagiarism report.ORIGINAL RESEARCH ARTICLE
published: 29 August 2012
Statistical conclusion validity: some common threats and
Miguel A. García-Pérez*
Facultad de Psicología, Departamento de Metodología, Universidad Complutense, Madrid, Spain
Jason W. Osborne, Old Dominion
Megan Welsh, University of
David Flora, York University, Canada
Miguel A. García-Pérez, Facultad de
Psicología, Departamento de
Metodología, Campus de
Complutense, 28223 Madrid, Spain.
e-mail: [email protected]
The ultimate goal of research is to produce dependable knowledge or to provide the evi-
dence that may guide practical decisions. Statistical conclusion validity (SCV) holds when
the conclusions of a research study are founded on an adequate analysis of the data, gen-
erally meaning that adequate statistical methods are used whose small-sample behavior
is accurate, besides being logically capable of providing an answer to the research ques-
tion. Compared to the three other traditional aspects of research validity (external validity,
internal validity, and construct validity), interest in SCV has recently grown on evidence
that inadequate data analyses are sometimes carried out which yield conclusions that a
proper analysis of the data would not have supported. This paper discusses evidence of
three common threats to SCV that arise from widespread recommendations or practices
in data analysis, namely, the use of repeated testing and optional stopping without control
of Type-I error rates, the recommendation to check the assumptions of statistical tests,
and the use of regression whenever a bivariate relation or the equivalence between two
variables is studied. For each of these threats, examples are presented and alternative
practices that safeguard SCV are discussed. Educational and editorial changes that may
improve the SCV of published research are also discussed.
Keywords: data analysis, validity of research, regression, stopping rules, preliminary tests
Psychologists are well aware of the traditional aspects of research
validity introduced by Campbell and Stanley (1966) and fur-
ther subdivided and discussed by Cook and Campbell (1979).
Despite initial criticisms of the practically oriented and some-
what fuzzy distinctions among the various aspects (see Cook
and Campbell, 1979, pp. 85–91; see also Shadish et al., 2002,
pp. 462–484), the four facets of research validity have gained
recognition and they are currently covered in many textbooks
on research methods in psychology (e.g., Beins, 2009; Good-
win, 2010; Girden and Kabacoff, 2011). Methods and strate-
gies aimed at securing research validity are also discussed in
these and other sources. To simplify the description, construct
validity is sought by using well-established definitions and mea-
surement procedures for variables, internal validity is sought by
ensuring that extraneThreats to the VALIDITY OF RESEARCH
About 2 1/2 years ago I was asked by J. Lee Wiederholt, the editor of the Journal of Learning Disabilities,
to review three studies and comment on their relative methodological merits and flaws (Parker, 1990).
The studies all attempted to assess the effects of individually prescribed, tinted lenses (called Irlen
lenses after the founder, Helen Irlen) on the reading performance of children with dyslexia and other
reading difficulties. The approach I took was to review the articles using the concepts provided by Cook
and Campbell (1979, 1983). These concepts, which are referred to as threats to the validity of research,
may be viewed as tools with which to ferret out weaknesses in research designs. With permission of the
JLD editor, I present here an updated description of these threats, based largely on a more recent
publication by Cook, Campbell, and Peracchio (1990).
It is a widely accepted truism that all published research is flawed to some extent. Because the research
enterprise is fraught with many pitfalls, researchers must become well versed in recognizing and, when
possible, avoiding design shortcomings. An obvious strategy when planning research is to assess the
likely flaws and develop approaches to overcome them. To accomplish this assessment, researchers
require a set of concepts to evaluate the quality of their designs. Such a set of concepts has been
detailed extensively in the literature (Campbell & Stanley, 1966; Cook & Campbell, 1979, 1983; Cook,
Campbell, & Peracchio, 1990). Cook, Campbell, and Peracchio (1990) presented 32 threats organized
according to four types of validity, namely internal, external, statistical conclusion, and construct
validity. The purpose of this editorial is to describe the four types of research validity and the 32 threats
to research validity.
Internal validity, the most important type of research validity, refers to the extent to which extraneous
variables (that is, sources of error variance) are controlled. Failure to control extraneous variables
prevents the researcher from concluding that observed outcomes are due to the independent
variable(s). Experimental control may be attained by using several techniques, including (a) assigning
participants randomly to treatment and control groups, (b) holding extraneous variables constant or
restricting their range, (c) including extraneous variables in the design to isolate their effects, (d)
employing methods of statistical control (e.g., analysis of covariance), and (e) matching participants in
the treatment and control groups on contaminating, extraneous variables (Bolton & Parker, 1992;
Christensen, 1980). Failure to use adequate methods to control error variance results in nine possible
threats to the internal validity of the research.
Threats to Internal Validity
Threats to internal validity include the following:
History. History refers to
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