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When different articles from the same cohort reported on different depression measures (eg, continuous vs categorical, different cut-off, different diagnostic tools) or on different diagnostic outcomes (dementia, AD and VaD) estimates from the same cohort were sometimes used in different analyses (but never in combination).
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Significance of differences for categorical variables between different categories was tested using a chi-square test.
A separate meta analysis of sensitivity and specificity using the meta analytic tools for independent binomial samples is problematic when the underlying diagnostic test utilizes a continuous or ordered categorical scale and different cut off values have been used in different diagnostic studies.
Such differences in outcome measures (e.g., continuous versus categorical) result in different assumptions and analytical approaches being used (see for a Discussion [ 36]).
The Kruskal-Wallis (KW) test of differences in medians was performed to assess differences in each of the four constructs between the different categorical variables: gender, race/ethnicity, and age.
Differences between groups were compared with X2 test for categorical variables within different age groupings; where appropriate, Fisher's exact test was used.
Chi-square test was performed to test the association of the occurrence of screw misplacement with different categorical variables (sex, side, level of scoliotic apex, and scoliotic convexity/convexity). Differences with p-values of < 0.05 were considered statistically significant.
"There are different categorical variants for resilience.
Kant puts the categorical imperative four different ways for a number of reasons.
Scoring was based on observation and examination of indicated parameters to assess the different categorical aspects of the health status.
Categorical data is different from continuous data, where the values of attribute do not follow any natural ordering.
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