chi-square test

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chi-square test

 
a statistical procedure for determining significant differences between frequencies observed within the data and frequencies that were expected. There are two chi-squared tests: the chi-square test of independence, which tests whether two or more series of frequencies are independent of one another; and the chi-square test of goodness of fit, which tests whether an observed frequency distribution fits a specified theoretical model. Written also χ2-test.

chi-square test

a statistical method of assessing the significance of a difference, as when the data from two or more samples, such as the numbers of females and males attending each of two colleges, are represented by a discrete number.
Synonym(s): χ2 test
References in periodicals archive ?
Hyperuricaemic patients significantly developed more cardiac complications in comparison to normouricaemic patients (* p=0.006; ** chi square test).
A B C D E Dentist 23 23 3 0 1 Orthodontist 10 39 0 0 0 Lay person 13 12 12 8 5 Chi square test was also performed to find out the presence of statistical significance among the two groups (Dentists and Orthodontists).
Treatment with PS was associated with a significant reduction in asthma attack frequency (p <0.05, Chi square test, Table 2).
One-way chi square tests were conducted to determine the statistical significance of the findings for each closed-ended question or statement while two way chi square tests will be used to control for the possible effects of gender and age on responses.
Although one might expect those planners selling long-term care insurance to be better informed or to have stronger feelings concerning the seriousness of the catastrophic illness problem than those not selling long-term care insurance, a chi square test failed to show a statistically significant
Willingness score between male and female students and pre-clinical and clinical students was compared by chi square test and p-values calculated.
The authors cover measures of central tendency, correlation, regression, chi square test of significance, analysis of variance, group comparisons, and split block design.
Only nine of the 52 (17 percent) revealed by their ratings on the depressed/happy scale that they were depressed: A chi square test (Siegel, 1956) indicated that the probability of this outcome being due to chance is less than 0.01.
TABLE 1: Crosstab GROUP Peri Pera Total G1 Count 56 58 114 % of GROUP 56.0% 58.0% 57.0% G2 Count 25 32 57 T1 % of GROUP 25.0% 32.0% 28.5% G3 Count 14 8 22 % of GROUP 14.0% 8.0% 11.0% G4 Count 5 2 7 % of GROUP 5.0% 2.0% 3.5% Total Count 100 100 200 % of GROUP 100.0% 100.0% 100.0% Value df Asymptotic Significance Pearson 3.817 3 .282 Chi-Square Chi-Square Tests Value Approximate Significance Nominal by Nominal Contingency .137 .282 Coefficient N of Valid Cases 200 Symmetric Measures Chi square test shows that there is no significant difference between both the groups with regards to pain on administration of the anaesthesia.
They were statistically analyzed using chi square test. In addition, epidemiological parameters like sensitivity, specificity, positive predictive value, negative predictive value were used at required observations.
This study used the Simultaneous Item Bias Test (SIBTEST), which assesses item dimensionality, and the Mantel-Haenszel Chi Square Test to detect statistically significant DIF in the individual items.
The overall pattern of NNAAP data indicates a reasonable fit even when the chi square test suggests rejection of factor models when sample sizes are large.