Statistical Conclusion Validity, Study notes of Statistics

Statistical Conclusion Validity. Table 1.3 Threats to Construct Validity. Threat. Explanation. Attention and contact with participants.

Typology: Study notes

2022/2023

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Statistical Conclusion Validity
Table 1.3 Threats to Construct Validity
Threat Explanation
Attention and
contact with
participants
Similar to special treatment; the level of attention
(differentiated attention) from the experimenter varies
between the groups (e.g., the researcher spends more time
with Group 1 than Group 2, and the differences observed in
the outcome can be explained by the increased amount of
attention and not due to the intervention)
Single
operations
and narrow
stimulus
sampling
The impact the researcher has on the development and
implementation of the treatment (i.e., researchers deliver
treatments differently based on experiences and expertise;
therefore, it is difficult to measure the impact the researcher
has on the treatment itself)
Experimenter
expectancies
The researchers’ expectancies, beliefs, and biases about
the results (e.g., if a researcher strongly believes anxiety
reduces test performance, then the interaction between the
researcher and the participant may influence the outcome
because the delivery of instructions and adherence to
protocols may change)
Cues of the
experimental
situation
Sources of influence conveyed to prospective participants
(e.g., rumors, information passed along from previous
participants)
Novelty effects The novelty of being in a new or innovative context
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Statistical Conclusion Validity

Table 1.3 Threats to Construct Validity Threat Explanation Attention and contact with participants Similar to special treatment; the level of attention (differentiated attention) from the experimenter varies between the groups (e.g., the researcher spends more time with Group 1 than Group 2, and the differences observed in the outcome can be explained by the increased amount of attention and not due to the intervention) Single operations and narrow stimulus sampling The impact the researcher has on the development and implementation of the treatment (i.e., researchers deliver treatments differently based on experiences and expertise; therefore, it is difficult to measure the impact the researcher has on the treatment itself) Experimenter expectancies The researchers’ expectancies, beliefs, and biases about the results (e.g., if a researcher strongly believes anxiety reduces test performance, then the interaction between the researcher and the participant may influence the outcome because the delivery of instructions and adherence to protocols may change) Cues of the experimental situation Sources of influence conveyed to prospective participants (e.g., rumors, information passed along from previous participants) Novelty effects The novelty of being in a new or innovative context

Inadequate explication of constructs The construct under investigation is not appropriately defined conceptually, leading to inadequate measurement (i.e., operationalization) Construct confounding Multiple constructs not clearly identified and accounted for operationally Mono- operation bias An operationalization (i.e., measurement) does not appropriately represent the construct under investigation, leading to measuring unintended constructs Mono-method bias All measurement techniques are the same as a means to measure the construct under investigation Confounding constructs with levels of constructs All the levels of a construct are not fully accounted for through the appropriate measurement and reporting tools Treatment sensitive factorial structure The interpretation and structure of a measure change as a result of the treatment Reactivity to assessment The participants’ awareness of being studied may influence the outcome; also known as acquiescence bias, social desirability, and the Hawthorne or observer effect; also an unnatural reaction to any particular form of assessment Page 2 of 3