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Possible GRIM inconsistencies

Source: R/grim-stats.R
grim-stats.Rd

These functions compute statistics related to GRIM-testing. In general, grim_probability() is the most useful of them, and it is responsible for the probability column in a data frame returned by grim_map().

  • grim_probability() returns the probability that a reported mean or percentage of integer data that is random except for the number of its decimal places is inconsistent with the reported sample size. For example, the mean 1.23 is treated like any other mean with two decimal places.

  • grim_ratio() is equal to grim_probability() unless grim_ratio() is negative, which can occur if the sample size is very large. Strictly speaking, this is more informative than grim_probability(), but it is harder to interpret.

  • grim_total() returns the absolute number of GRIM-inconsistencies that are possible given the mean or percentage's number of decimal places and the corresponding sample size.

For discussion, see vignette("grim"), section GRIM statistics.

Usage

grim_probability(x, n, items = 1, percent = FALSE)

grim_ratio(x, n, items = 1, percent = FALSE)

grim_total(x, n, items = 1, percent = FALSE)

Arguments

x

String (length 1). Mean or percentage value computed from data with integer units, e.g., mean scores on a Likert scale or percentage of study participants in some condition. It has to be string to capture any trailing zeros.

n

Integer. Sample size corresponding to x.

items

Integer. Number of items composing the mean or percentage value in question. Default is 1.

percent

Logical. Set percent to TRUE if x is expressed as a proportion of 100 rather than 1. The functions will then account for this fact through increasing the decimal count by 2. Default is FALSE.

Value

Integer or double. The number of possible GRIM inconsistencies, or their probability for a random mean or percentage with a given number of decimal places.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

See also

grim() for the GRIM test itself; as well as grim_map() for applying it to many cases at once.

Examples

# Many value sets are inconsistent here:
grim_probability(x = "83.29", n = 21)
#> [1] 0.79
grim_total(x = "83.29", n = 21)
#> [1] 79

# No sets are inconsistent in this case...
grim_probability(x = "5.14", n = 83)
#> [1] 0.17
grim_total(x = "5.14", n = 83)
#> [1] 17

# ... but most would be if `x` was a percentage:
grim_probability(x = "5.14", n = 83, percent = TRUE)
#> [1] 0.9917
grim_total(x = "5.14", n = 83, percent = TRUE)
#> [1] 9917