Discriminant Validity: A Comparative Analysis

Values and Motives Questionnaire: The Technical Manual (n.d.). Retrieved from the Liberty COUN 521 website: Psytech International.

The internal consistency of this measurement used the coefficient alpha. The coefficients were all over .5, but they each had various ranges. Because the scale had such varying ranges, one could wonder if this could indicate a problem with errors? The VMQ shows an overall internal dependability and a low level of SEM. The internal consistency does surpass the requirements for a reliable instrument. According to authors of the VMQ (n.d), “…the scales approximate or exceed acceptable levels of internal consistency” (pg. 16). However, it is important to note that the scores of this test are not normally distributed, which impacts the standard deviations of the scores. While the deviation of the scores is acceptable, the test results did not have an extremely high correlation. The VMQ also demonstrated the validity scales having lower correlations (Values and Motives Questionnaire, n.d). One weakness of the reliability to consider is that the test was only compared to other tests that examined values. It did not compare values to those of other countries/cultures. Specific cultures and/or family systems have specific values that are instilled in them throughout the years. It would be beneficial to use this instrument in comparison to different demographic backgrounds. In doing this, one will be able to gain insight into how these differences can affect the results

Psychological determinants are an important aspect in community nursing. This determinant enables the nurse to understand how health of a population is being affected psychologically. Psychological determinants embody in internal and external factors that influence the health of a population (Clark, 2015). A school level, psychological determinants can help determine factors that could contribute to mental illnesses and absenteeism in schools. The purpose of this paper is to exhibit how psychological determinants influence the health and well-being of students in a school setting.

In the surveys and randomized controlled trials, validity, reliability and generalizability are three key factors.

In order to test the construct validity of each scale, bivariate correlation will be used, because this test is to find out the association among those different variables and how strong the association is. (Pallant,2010). At the same time, checking the association among the subscales is regarded as way to assess scale’s construct validity (ibid). In addition, most of the researchers believe that the dividing point of the statistical significance (p) to ensure the relationship between variables is .05. Namely, in 100 samples, there are 5 times chances to get relationship that are not appeared in the whole population. Specifically, p < 0.05 is set to ensure the expected relationship (Bryman and Bell, 2007). And all the

Internal consistency between the three versions of the ABAS-II displayed high coefficients for the GAC scores. The U.S. and Romania both acquired a coefficient of .98, while Taiwan attained a coefficient of .99. These strong coefficients provided evidence for the ability for the ABAS-II to measure adaptability across different cultures. Oakland et al. (2004) also discovered adequate correlations for construct validity. This supported the ABAS-II to measure adaptability, even in the alternate cultures. Reliability estimates revealed high coefficients between all three cultural versions of the test, within the .90 and higher range.

that of what is expected to see if the answers compare to that of what is expected. This allows for individuals to answer, as they desire to see if they are fit for the position. The aptitude test has various different types of categories that would be tested ensure that it is not being subjective. (“Aptitude Tests- Introduction” Psychometric success)

Data were presented as mean ± SEM. The data were analyzed by SPSS Version 17.0 statistic software package (Chicago, IL, USA). Two-tailed t tests were used to determine significance between groups of sham and SCI groups. Values of P< 0.05 were considered

Edelen and Reeve used handful psychometric techniques to assess the data and reduce the form from 19-item to 10-item. They used CTT to reveal the basic statistics for the 19 items. Then, they calculated the Cronbach 's alphas and the Fisher’s z-transformed average interitem correlations to elect a 3-category version of the scale to be analyzed. Moreover, they assessed the dimensionality and local independence using EFA and CFA. For the major IRT analysis, the authors utilized the GRM to estimate slope and step parameters, construct ICCs and IIFs, and evaluate fit at the item level. In the end, they were able to select the short 10-item form.

Eigenvalues larger than one and scree plots were analyzed to assist in determining the number of factors for each construct. Additionally, the strength of factor loadings as well as face validity were analyzed to further assist in determining the items to be included for each exogenous and endogenous variables.

1. How would you differentiate among multiple discriminant analysis, regression analysis, logistic regression analysis, and analysis of variance?

s we have seen, bankruptcy—or even the possibility of bankruptcy—can cause significant trauma for a firm’s managers, investors, suppliers, customers, and community. Thus, it would be beneficial to be able to predict the likelihood of bankruptcy so that steps could be taken to avoid it or at least to reduce its impact. One approach to bankruptcy prediction is multiple discriminant analysis (MDA), a statistical technique similar to regression analysis. In this extension, we discuss MDA in detail and illustrate its application to bankruptcy prediction.1 Suppose a bank loan officer wants to segregate corporate loan applications into those likely to default and those unlikely to default.

While describing the independent variables chosen, we have to consider that some are latent nature, meaning that the underlying concepts cannot be measured directly. In order to create a meaningful value for said variables, we carried out a factor analysis with the aim of extracting two factors. In our analysis we also included 3 dummy variables, one single variable and the usual control variables. A brief

The internal reliability can be tested using Cronbach’s alpha (Fornell & Larcker, 1981). Table 2 shows the reliability assessments for independent variables, moderating variable and dependent variable to test the internal consistency. The reliability analysis indicates the degree to which items in each set correlate with one another. The Cronbach alpha should be greater than 0.7 to indicate a strong reliability for a questionnaire content (Nunnally, 1978). In this study, the Cronbach of the majority of constructs was greater than 0.7, except formalization. The smallest Cronbach’s alpha was 0.624 which indicates a reliability (may not be stronger reliability) for our

The traditional approach not only contrasts with the PF approach with relation to how validity is hypothesized; however, with reference to practical emphasis. In contrary, the traditional approach where correlational methods used to measure the relationship between test score and decisive factor. The PF model manipulations experimental methods to control the extreme test scores variables that measure correlations(Bornstein et al., 1996a). The