The chi-square test of independence.

The Chi-square statistic is a non-parametric (distribution free) tool designed to analyze group differences when the dependent variable is measured at a nominal level. Like all non-parametric statistics, the Chi-square is robust with respect to the distribution of the data. Specifically, it does not require equality of variances among the study groups or homoscedasticity in the data. It permits evaluation of both dichotomous independent variables, and of multiple group studies. Unlike many other non-parametric and some parametric statistics, the calculations needed to compute the Chi-square provide considerable information about how each of the groups performed in the study. This richness of detail allows the researcher to understand the results and thus to derive more detailed information from this statistic than from many others. The Chi-square is a significance statistic, and should be followed with a strength statistic. The Cramer's V is the most common strength test used to test the data when a significant Chi-square result has been obtained. Advantages of the Chi-square include its robustness with respect to distribution of the data, its ease of computation, the detailed information that can be derived from the test, its use in studies for which parametric assumptions cannot be met, and its flexibility in handling data from both two group and multiple group studies. Limitations include its sample size requirements, difficulty of interpretation when there are large numbers of categories (20 or more) in the independent or dependent variables, and tendency of the Cramer's V to produce relative low correlation measures, even for highly significant results.

Interrater reliability: the kappa statistic.

The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen's kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from -1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen's suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.

Multiple comparison analysis testing in ANOVA.

The Analysis of Variance (ANOVA) test has long been an important tool for researchers conducting studies on multiple experimental groups and one or more control groups. However, ANOVA cannot provide detailed information on differences among the various study groups, or on complex combinations of study groups. To fully understand group differences in an ANOVA, researchers must conduct tests of the differences between particular pairs of experimental and control groups. Tests conducted on subsets of data tested previously in another analysis are called post hoc tests. A class of post hoc tests that provide this type of detailed information for ANOVA results are called "multiple comparison analysis" tests. The most commonly used multiple comparison analysis statistics include the following tests: Tukey, Newman-Keuls, Scheffee, Bonferroni and Dunnett. These statistical tools each have specific uses, advantages and disadvantages. Some are best used for testing theory while others are useful in generating new theory. Selection of the appropriate post hoc test will provide researchers with the most detailed information while limiting Type 1 errors due to alpha inflation.

Psychometric tests of Expectations of Filial Piety Scale in a Mexican-American population.

AIMS AND OBJECTIVES: This paper reports the development of the Expectations of Filial Piety Scale for use with Mexican-American parents regarding expectations they have of their adult children for care and support. BACKGROUND: Earlier work by the authors demonstrated that filial piety is a cross-cultural construct that can be used with Hispanic/Latino populations. More refined development of the construct required testing with more homogeneous subsets (i.e. Mexican-Americans) within the broad designation of Hispanic/Latino adults. DESIGN: Non-experimental methodological design for field testing of the instrument's psychometric properties. METHODS: A convenient sample of 80 Mexican-American adults in California and Texas completed a brief biographical survey and field tested the Expectations of Filial Piety Scale. RESULTS: Common factor analysis with orthogonal rotation was used to extract three factors, which accounted for 58% of the variance in scale scores. These factors included: I: respect for parents (24.05%); II: honouring parents (12.5%); and III: family unity (16.56%). Overall scale reliability was 0.87 with individual factor reliability coefficients ranging from 0.74 to 0.87 and test-retest correlation was 0.73. CONCLUSIONS: The results show that the Expectations of Filial Piety Scale is an internally consistent and reliable tool for use in studies of the Mexican-American population. Mexican elders historically underuse formal services; a large portion of this population will most likely depend on support from their family members when they reach advanced ages. There is a lack of culturally sensitive instruments to measure family values in caring for older adults in Mexican-Americans. RELEVANCE TO CLINICAL PRACTICE: This scale can enable case workers and nurses in long-term care settings to assess the elder's expectations for family support accurately and compare these expectations with available family support, children's intentions to care for a dependent parent or other family member and the need for supplemental care in Mexican-American families.

The role of caregiver gender and caregiver burden in nursing home placements for elderly Taiwanese survivors of stroke.

The role of caregiver gender in caregiver burden and the association between the level of caregiver burden and institutionalization of elderly stroke survivors in Taiwan were explored using a correlational, descriptive design. The convenience sample was composed of 78 male and 69 female primary caregivers of stroke survivors. Simple multiple regression and t tests were used to test the research questions. Women perceived caregiving as more burdensome than men did. Caregiver burden was most strongly associated with the characteristics of the care recipients and with institutionalization. The proposed model explained 45% and 28% of the variance in caregiver burden for male and female caregivers, respectively. It is recommended that the professional nursing role in Taiwan be expanded to include postdischarge care, respite, and home-care services to allow families to keep their elderly at home as long as possible and to provide culturally sensitive care to families that might be traumatized by having to violate ethnic Chinese cultural norms by institutionalizing family members.

Descriptive statistics, Part II: Most commonly used descriptive statistics.

Descriptive measures can reveal a great deal of information about any variable of interest, whether the data be clinical, administrative, educational, or research data. To make best use of a descriptive statistic, it is important to know what levels of measurement should be used with the statistic, and what information the statistic can provide. To find out about the most typical case, measures of central tendency are appropriate. To discover whether the variable has a normal distribution, measures of shape should be applied. And to discover the variability about the mean of the variable, measures of dispersion should be used. Finally, percentiles and quartiles are useful for describing the placement of a single case in a population.