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Claims and Validity in Research

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This document from Chapter 3 outlines how to distinguish among the three main types of research claims—frequency, association, and causal—and emphasizes evaluating them using the four big validities: construct, statistical, external, and internal. It explains that while frequency and association claims rely mostly on measurement and correlations, causal claims require experimental design, including control of variables, temporal precedence, and ruling out confounds. The chapter encourages critical thinking about how research conclusions are formed, clarifying that no study can achieve perfect validity across all four types, so researchers must prioritize depending on the claim.

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Geüpload op
2 juni 2025
Aantal pagina's
6
Geschreven in
2024/2025
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College aantekeningen
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Benjamin blankenship
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Chapter Objectives
1.​ Differentiate the three types of claims: frequency, association, and causal
2.​ Ask appropriate questions to help you interrogate each of the four big validities: construct
validity, statistical validity, external validity,and internal validity.
3.​ Explain which validities are most relevant for each of the three types of claims.
4.​ State which kind of research study is required to support a causal claim

Variables
●​ Measured Variables: A variable in a study whose levels (values) are observed and
recorded
○​ Ex. Variables such as height, weight, hair color, gender, and IQ
●​ Manipulated Variables: A variable in an experiment that a researcher controls, such as
by assigning participants to its different levels (values).
○​ Ex. A researcher may assign different doses of medications to participants
●​ Conceptual Variables: A variable of interest, stated at an abstract, or conversational,
level
●​ Operational Definitions: The specific way in which a concept of interest is measured or
manipulated as a variable in a study

Three Claims
●​ Claim: An argument that someone is trying to make
●​ Frequency Claims: A claim that describes a particular rate or degree of a single variable
○​ These are claims that only focus on one variable at a time such as level of food
insecurity or rate of smiling
●​ Association Claims: Argues that one level of a variable is likely to be associated with a
particular level of another variable
○​ These sometimes correlate
○​ Ex. Higher coffee consumption is associated with lower levels of depression
○​ Positive Association: An association in which high levels of one variable go with
high levels of the other variable, and low levels with low levels
○​ Negative Association: An association with high to low and low to high
○​ Zero Association: The lack of systematic association between two variables
●​ Causal Claims: A claim arguing that a specific change in one variable is responsible for
influencing the value of another variable
○​ Association vs. causal
■​ Association verbs: is liked to, is at higher risk for, is associated with
■​ Causal Verbs: Causes, affects, changes, exacerbates

Not All Claims Are Based on Research

, Four Big Validities
●​ Construct Validity: How well the variables in a study are measured or manipulated.
○​ The extent to which the operational variables in a study are a good approximation
of the conceptual variables
●​ External Validity: The extent to which the results of a study generalize to some larger
population, as well as to other times or situations
●​ Statistical Validity: How well the numbers support the claim - that is, how strong the
effect is and the precision of the estimate
○​ Also takes into account whether the study has been replicated
●​ Internal Validity: In a relationship between one variable (A) and another (B), the extent
to which A, rather than some other variable (C), is responsible for changes in B
●​ Frequency Claims
○​ Construct Validity: Refers to how well a conceptual variable is operationalized
○​ Generalizability: How well participants represent the population being studied
○​ External Validity: The indication of how well the population is represented
○​ Statistical Validity: The extent to which a study’s statistical conclusions are
precise
■​ Basically how well do the numbers support the claim
●​ Association Claims
○​ Construct Validity
■​ Measure each variable accurately
○​ Statistical validity
■​ When applied to an association claim, statistical validity considers how
strong the estimated association is and how precise that estimate is, and it
considers other estimates of the same association


Type of Validity Frequency Claims Association Claims Causal Claims
(“4 in 10 teens admit (“study links exercise (“pretending to be
to texting while to higher pay”) batman helps kids
driving”) stay on task”)

Usually based on a Usually supported by Must be supported by
survey or poll, but a correlational study an experimental study
can come from other
types of studies

Construct Validity How well has the How well has the How well has the
researcher measured researcher measured researcher measured
the variable in each of the two or manipulated the
question? variables in the variables in the
association? study?
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