Function estimating the consequences of reliability for subsequent analysis.
Usage
get_consequences(
measure_typ = "dynamic_iota_index",
measure_1_val,
measure_2_val = NULL,
level = 0.95,
strength = NULL,
data_type,
sample_size
)Arguments
- measure_typ
Type of measure used for estimation. Set "iota_index" for the original Iota Index, "static_iota_index" for the static transformation of the Iota Index with d=4 or "dynamic_iota_index" for the dynamic transformation of the Iota Index with d=2.
- measure_1_val
Reliability value for the independent variable.
- measure_2_val
Reliability value for the dependent variable. If not set, the function uses the same value as for the independent variable.
- level
Level of certainty for calculating the prediction intervals.
- strength
True strength of the relationship between the independent and dependent variable. Possible values are "no", "weak", "medium" and "strong". If no value is supplied, a strong relationship is assumed for deviation and a weak relationship for all others. They represent the most demanding situations for the reliability.
- data_type
Type of data. Possible values are "nominal" or "ordinal".
- sample_size
Size of the sample in the study.
Value
Returns a data.frame which contains the prediction intervals
for the deviation between true and estimated sample association/correlation,
risk of Type I errors and chance to correctly classify the effect size.
Additionally, the probability is estimated so that the statistics of the sample
deviate from an error free sample with no or only a weak effect .
Note
The classification of effect sizes uses the work of Cohen (1988), who differentiates effect sizes by their relevance for practice.
For nominal data, all statistics refer to Cramer's V. For ordinal data, all statistics refer to Kendall's Tau.
The models for calculating the consequences are taken from Berding and Pargmann (2022).
References
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd Ed.). Taylor & Francis.
Berding, Florian, and Pargmann, Julia (2022).Iota Reliability Concept of the Second Generation.Measures for Content Analysis Done by Humans or Artificial Intelligences. Berlin:Logos. https://doi.org/10.30819/5581