statcheck extracts Null Hypothesis Significance (NHST) results from
strings and returns the extracted values, reported p-values and recomputed
statcheck( texts, stat = c("t", "F", "cor", "chisq", "Z", "Q"), OneTailedTests = FALSE, alpha = 0.05, pEqualAlphaSig = TRUE, pZeroError = TRUE, OneTailedTxt = FALSE, AllPValues = FALSE, messages = TRUE )
A vector of strings.
Specify which test types you want to extract. "t" to extract
t-values, "F" to extract F-values, "cor" to extract correlations, "chisq"to
extract χ2 values, "Z" to extract Z-values, and "Q" to extract
Logical. Do you want to assume that all reported tests are one-tailed (TRUE) or two-tailed (FALSE, default)?
Assumed level of significance in the scanned texts. Defaults to .05.
Logical. If TRUE, statcheck counts p <= alpha as significant (default), if FALSE, statcheck counts p < alpha as significant.
Logical. If TRUE, statcheck counts p = .000 as an error (because a p-value is never exactly zero, and should be reported as < .001), if FALSE, statcheck does not count p = .000 automatically as an error.
Logical. If TRUE, statcheck searches the text for "one-sided", "one-tailed", and "directional" to identify the possible use of one-sided tests. If one or more of these strings is found in the text AND the result would have been correct if it was a one-sided test, the result is assumed to be indeed one-sided and is counted as correct.
Logical. If TRUE, the output will consist of a dataframe with all detected p values, also the ones that were not part of the full results in APA format.
Logical. If TRUE, statcheck will print a progress bar while it's extracting statistics from text.
statcheck roughly works in three steps.
1. Scan text for statistical results
statcheck uses regular expressions to recognizes statistical results
from t-tests, F-tests, χ2-tests, Z-tests, Q-tests, and correlations.
statcheck can only recognize these results if the results are reported
exactly according to the APA guidelines:
t(df) = value, p = value
F(df1, df2) = value, p = value
r(df) = value, p = value
χ2 (df, N = value) = value, p = value (N is optional)
Z = value, p = value
Q(df) = value, p = value (statcheck can distinguish between Q, Qw / Q-within, and Qb / Q-between)
statcheck takes into account that test statistics and p values may be
exactly (=) or inexactly (< or >) reported. Different spacing has also been
taken into account.
2. Recompute p-value
statcheck uses the reported test statistic and degrees of freedom to
recompute the p-value. By default, the recomputed p-value is two-sided
3. Compare reported and recomputed p-value
This comparison takes into account how the results were reported, e.g.,
p < .05 is treated differently than p = .05. Incongruent p values are marked
error. If the reported result is significant and the recomputed
result is not, or vice versa, the result is marked as a
Correct rounding is taken into account. For instance, a reported t-value of
2.35 could correspond to an actual value of 2.345 to 2.354 with a range of
p-values that can slightly deviate from the recomputed p-value.
statcheck will not count cases like this as errors.
Note that when
statcheck flags an
decision_error, it implicitly assumes that the p-value is the
inconsistent value, but it could just as well be the case that the test
statistic or degrees of freedom contain a reporting error.
merely detects wether a set of numbers is consistent with each other.
A data frame containing for each extracted statistic:
Name of the file of which the statistic is extracted
Character indicating the statistic that is extracted
First degree of freedom (if applicable)
Second degree of freedom
Reported comparison of the test statistic, when importing from pdf this will often not be converted properly
Reported value of the statistic
Reported comparison, when importing from pdf this might not be converted properly
The reported p-value, or NA if the reported value was n.s.
The recomputed p-value
Raw string of the statistical reference that is extracted
The computed p value is not congruent with the reported p-value
The reported result is significant whereas the recomputed result is not, or vice versa.
Logical. Does the text contain the string "sided", "tailed", and/or "directional"?
What proportion of all detected p-values was part of a fully APA reported result?
For more details, see the online manual.
txt <- "blablabla the effect was very significant (t(100)=1, p < 0.001)" statcheck(txt)
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