Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

Medical educators attempt to create reliable and valid tests and questionnaires in order to enhance the accuracy of their assessment and evaluations. Validity and reliability are two fundamental elements in the evaluation of a measurement instrument. Instruments can be conventional knowledge, skill or attitude tests, clinical simulations or survey questionnaires. Instruments can measure concepts, psychomotor skills or affective values. Validity is concerned with the extent to which an instrument measures what it is intended to measure. Reliability is concerned with the ability of an instrument to measure consistently.1 It should be noted that the reliability of an instrument is closely associated with its validity. An instrument cannot be valid unless it is reliable. However, the reliability of an instrument does not depend on its validity.2 It is possible to objectively measure the reliability of an instrument and in this paper we explain the meaning of Cronbach’s alpha, the most widely used objective measure of reliability.

Calculating alpha has become common practice in medical education research when multiple-item measures of a concept or construct are employed. This is because it is easier to use in comparison to other estimates (e.g. test-retest reliability estimates)3 as it only requires one test administration. However, in spite of the widespread use of alpha in the literature the meaning, proper use and interpretation of alpha is not clearly understood. 2, 4, 5 We feel it is important, therefore, to further explain the underlying assumptions behind alpha in order to promote its more effective use. It should be emphasised that the purpose of this brief overview is just to focus on Cronbach’s alpha as an index of reliability. Alternative methods of measuring reliability based on other psychometric methods, such as generalisability theory or item-response theory, can be used for monitoring and improving the quality of OSCE examinations 6-10, but will not be discussed here.

What is Cronbach alpha?
Alpha was developed by Lee Cronbach in 195111 to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1. Internal consistency describes the extent to which all the items in a test measure the same concept or construct and hence it is connected to the inter-relatedness of the items within the test. Internal consistency should be determined before a test can be employed for research or examination purposes to ensure validity. In addition, reliability estimates show the amount of measurement error in a test. Put simply, this interpretation of reliability is the correlation of test with itself. Squaring this correlation and subtracting from 1.00 produces the index of measurement error. For example, if a test has a reliability of 0.80, there is 0.36 error variance (random error) in the scores (0.80×0.80 = 0.64; 1.00 – 0.64 = 0.36).12 As the estimate of reliability increases, the fraction of a test score that is attributable to error will decrease.2 It is of note that the reliability of a test reveals the effect of measurement error on the observed score of a student cohort rather than on an individual student. To calculate the effect of measurement error on the observed score of an individual student, the standard error of measurement must be calculated (SEM).13

If the items in a test are correlated to each other, the value of alpha is increased. However, a high coefficient alpha does not always mean a high degree of internal consistency. This is because alpha is also affected by the length of the test. If the test length is too short, the value of alpha is reduced.2, 14 Thus, to increase alpha, more related items testing the same concept should be added to the test. It is also important to note that alpha is a property of the scores on a test from a specific sample of testees. Therefore investigators should not rely on published alpha estimates and should measure alpha each time the test is administered.14


Use of Cronbach’s alpha
Improper use of alpha can lead to situations in which either a test or scale is wrongly discarded or the test is criticised for not generating trustworthy results. To avoid this situation an understanding of the associated concepts of internal consistency, homogeneity or unidimensionality can help to improve the use of alpha. Internal consistency is concerned with the interrelatedness of a sample of test items, whereas homogeneity refers to unidimensionality. A measure is said to be unidimensional if its items measure a single latent trait or construct. Internal consistency is a necessary but not sufficient condition for measuring homogeneity or unidimensionality in a sample of test items. 5, 15 Fundamentally, the concept of reliability assumes that unidimensionality exists in a sample of test items16 and if this assumption is violated it does cause a major underestimate of reliability. It has been well documented that a multidimensional test does not necessary have a lower alpha than a unidimensional test. Thus a more rigorous view of alpha is that it cannot simply be interpreted as an index for the internal consistency of a test. 5, 15, 17

Factor Analysis can be used to identify the dimensions of a test.18 Other reliable techniques have been used and we encourage the reader to consult the paper “Applied Dimensionality and Test Structure Assessment with the START-M Mathematics Test” and to compare methods for assessing the dimensionality and underlying structure of a test.19

Alpha, therefore, does not simply measure the unidimensionality of a set of items, but can be used to confirm whether or not a sample of items is actually unidimensional.5 On the other hand if a test has more than one concept or construct, it may not make sense to report alpha for the test as a whole as the larger number of questions will inevitable inflate the value of alpha. In principle therefore, alpha should be calculated for each of the concepts rather than for the entire test or scale. 2, 3 The implication for a summative examination containing heterogeneous, case-based questions is that alpha should be calculated for each case.

More importantly, alpha is grounded in the ‘tau equivalent model’ which assumes that each test item measures the same latent trait on the same scale. Therefore, if multiple factors/traits underlie the items on a scale, as revealed by Factor Analysis, this assumption is violated and alpha underestimates the reliability of the test.17 If the number of test items is too small it will also violate the assumption of tau-equivalence and will underestimate reliability.20 When test items meet the assumptions of the tau-equivalent model, alpha approaches a better estimate of reliability. In practice, Cronbach’s alpha is a lower-bound estimate of reliability because heterogeneous test items would violate the assumptions of the tau-equivalent model.5 If the calculation of “standardised item alpha” in SPSS is higher than “Cronbach’s alpha”, a further examination of the tau-equivalent measurement in the data may be essential.


Numerical values of alpha
As pointed out earlier, the number of test items, item inter-relatedness and dimensionality affect the value of alpha.5 There are different reports about the acceptable values of alpha, ranging from 0.70 to 0.95. 2, 21, 22 A low value of alpha could be due to a low number of questions, poor inter-relatedness between items or heterogeneous constructs. For example if a low alpha is due to poor correlation between items then some should be revised or discarded. The easiest method to find them is to compute the correlation of each test item with the total score test; items with low correlations (approaching zero) are deleted. If alpha is too high it may suggest that some items are redundant as they are testing the same question but in a different guise. A maximum alpha value of 0.90 has been recommended.14


Summary
High quality tests are important to evaluate the reliability of data supplied in an examination or a research study. Alpha is a commonly employed index of test reliability. Alpha is affected by the test length and dimensionality. Alpha as an index of reliability should follow the assumptions of the essentially tau-equivalent approach. A low alpha appears if these assumptions are not meet. Alpha does not simply measure test homogeneity or unidimensionality as test reliability is a function of test length. A longer test increases the reliability of a test regardless of whether the test is homogenous or not. A high value of alpha (> 0.90) may suggest redundancies and show that the test length should be shortened.

/pdf/making-sense-of-cronbach-s-alpha-2pncxqblel.pdf

Making sense of Cronbach's alpha

Attachment--and loss.

This volume presents the important concepts required for implementing two disciplines of factor analysis - exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) with an emphasis on EFA/CFA linkages. Modern extensions of older data analysis methods (e.g., ANOVA, regression, MANOVA, and descriptive discriminant analysis) have brought theory-testing procedures to the analytic forefront. Variations of factor analysis, such as the factoring of people or time, have great potential to inform psychological research. Thompson deftly presents highly technical material in an appealing and accessible manner. The book is unique in that it presents both exploratory and confirmatory methods within the single category of the general linear model (GLM). Canons of best factor analytic practice are presented and explained. An actual data set, generated by 100 graduate students and 100 faculty from the United States and Canada, is used throughout the book, allowing readers to replicate reported results.

Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications

Coefficient alpha is the most popular measure of reliability (and certainly of internal consistency reliability) reported in psychological research. This is noteworthy given the numerous deficiencies of coefficient alpha documented in the psychometric literature. This mismatch between theory and practice appears to arise partly because users of psychological scales are unfamiliar with the psychometric literature on coefficient alpha and partly because alternatives to alpha are not widely known. We present a brief review of the psychometric literature on coefficient alpha, followed by a practical alternative in the form of coefficient omega. To facilitate the shift from alpha to omega, we also present a brief guide to the calculation of point and interval estimates of omega using a free, open source software environment.

/pdf/from-alpha-to-omega-a-practical-solution-to-the-pervasive-4m4zcidq56.pdf

From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation

Coefficient alpha, the most commonly used estimate of internal consistency, is often considered a lower bound estimate of reliability, though the extent of its underestimation is not typically known. Many researchers are unaware that coefficient alpha is based on the essentially tau-equivalent measurement model. It is the violation of the assumptions required by this measurement model that are often responsible for coefficient alpha's underestimation of reliability. This article presents a hierarchy of measurement models that can be used to estimate reliability and illustrates a procedure by which structural equation modeling can be used to test the fit of these models to a set of data. Test and data characteristics that can influence the extent to which the assumption of tau-equivalence is violated are discussed. Both heuristic and applied examples are used to augment the discussion.

Congeneric and (Essentially) Tau-Equivalent Estimates of Score Reliability What They Are and How to Use Them

We conducted a reliability generalization meta-analysis to examine the internal consistency of Dyadic Adjustment Scale (DAS; Spanier, 1976) scores across 91 published studies with 128 samples and 25,035 participants. The DAS was found to produce total and Dyadic cohesion, Consensus, and Satisfaction scores of acceptable internal consistency, although lower than those originally reported by Spanier (1976). Reliability estimates of these scores did not differ by the sexual orientation, gender, marital status, or ethnicity of the sample. The Affective Expression subscale was found to produce scores with poor Cronbach’s alpha across studies. Reliability estimates of Affective Expression scores were highly influenced by sample characteristics. The implications of these results are discussed as they relate to the use of the DAS in research.

/pdf/the-dyadic-adjustment-scale-a-reliability-generalization-h8bu3y17hm.pdf

The Dyadic Adjustment Scale: A Reliability Generalization Meta-Analysis

The Cam-Clay model for isothermal mechanical behaviour of clays has been extended to take account of the effects of temperature on stressstrain behaviour. The assumptions used in constructing the ...

Modified cam-clay modelling of temperature effects in clays

We conducted a reliability-generalization meta-analysis of 7 of the most frequently used measures of relationship satisfaction: the Locke-Wallace Marital Adjustment Test (LWMAT), the Kansas Marital Satisfaction Scale (KMS), the Quality of Marriage Index, the Relationship Assessment Scale, the Marital Opinion Questionnaire, Karney and Bradbury's (1997) semantic differential scale, and the Couples Satisfaction Index. Six hundred thirty-nine reliability coefficients from 398 articles and 636,806 individuals provided internal consistency reliability estimates for this meta-analysis. We present the average score reliabilities for each measure, characterize the variance in score reliabilities across studies, and consider sample and study characteristics that are predictive of score reliability. Overall, the KMS and the LWMAT appear to be the strongest and weakest measures, respectively, from a reliability perspective. We discuss the importance of considering reliability invariance when making cross-group comparisons and provide recommendations for researchers when electing a measure of relationship satisfaction.

The reliability of relationship satisfaction: a reliability generalization meta-analysis.

Confirmatory factor analysis (CFA) is a statistical procedure frequently used to test the fit of data to measurement models. Published CFA studies typically report factor pattern coefficients. Few reports, however, also present factor structure coefficients, which can be essential for the accurate interpretation of CFA results. The interpretation errors that can arise when CFA results are interpreted without considering structure coefficients are described, and some examples from current literature illustrating these errors are also presented.

James M. Graham

Papers

Congeneric and (Essentially) Tau-Equivalent Estimates of Score Reliability What They Are and How to Use Them

The Dyadic Adjustment Scale: A Reliability Generalization Meta-Analysis

Modified cam-clay modelling of temperature effects in clays

The reliability of relationship satisfaction: a reliability generalization meta-analysis.

Consequences of Not Interpreting Structure Coefficients in Published CFA Research: A Reminder