Paper Example on Armed Services Vocational Aptitude Battery (ASVAB)

Introduction

The ASVAb is an assessment tool used to stimulate interest in training areas within the armed forces in the United States. The tool is primarily used to help military recruiters to screen recruits so that they can be placed in areas where they will be most productive and successful. ASVAB has three composite scores, namely verbal ability, math ability and academic ability. There are ten subtests within the three broad categories. ASVAB is unique in that it is a highly standardized test and extra measures have been taken to include the racial aspect. ASVAB scores are interpreted as ability and general interest.

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Hypothesis Testing for the Coefficient Alpha

Numerous studies have been conducted on various facets of internet relationships, focusing on the levels of intimacy, closeness, different communication modalities, and the frequency of use of computer-mediated communication (CMC). Hypothesis testing is often used to evaluate if an aim can be met, the assessment is targeted to reach the desired population, and if a test is valid and reliable. In Maydeu-Olivares, Coffman, Garcia-Forero, and Gallardo-Pujol's article, the researchers explored three different situations for testing the coefficient of alpha. The first situation involves whether a coefficient alpha equals a prescribed value. The second situation entails hypothesis tests in which two statistically independent alphas may arise when the equality of coefficient alphas is tested across groups. The last situation involves two statistically independent sample alphas, which may arise when the equality of alphas is tested across time.

Normal and Asymptotic Distributions

Asymptotic distribution, known as a limiting distribution, is used hypothetically to assume that there will be a convergence of data into a normal distribution within a large-scale sample, while a normal distribution, also known as the bell curve, assumes that data will fall into symmetrical data representation with a mean of 1 and a standard deviation of 1. In this case, the data is said to be normally distributed.

Maydeu-Olivares et al. (2010) attributed the assumptions of these distributions because, in each of the three situations, the process of drawing statistical inferences for alphas is straightforward. This means that the process is model-free and does not require assuming that the items contained in the test score be normally distributed. Due to this computational ease, researchers interested in making statistical inferences about population reliability may find it necessary to draw inferences for population alphas instead (Hicks, Iacono & McGue, 2012).

Internal Consistency and Coefficient Alphas Using Cronbach's Alpha

Cronbach's alpha is one of the most widely used measures to test reliability. This measure is preferred because of multiple reasons. The main reason is that there is no requirement to have multiple raters or administration of the scale to prove reliability (Streiner, 2003). However, with the ease of this, there are cautions and myths associated with its use.

Cautions of Use

Users cannot trust that published approximations of alpha apply in each situation. If the group for which the case is used is homogenous than the one used in the published report, then the alpha value will most likely be different (Conway, Levy, Vanyukov, Chandler, Rutter, Swan & Neale, 2010).

High values of alpha do not necessarily guarantee internal consistency because alpha is sensitive to the length of the scale. Scales containing more than 20 items can show acceptable alpha values even if they may consist of a few orthogonal dimensions. As such, it is imperative to examine the individual matrix of correlations for each item and then compare this information to the total correlations. In this regard, it is recommended to use a mean inter-item of 0.15 and 0.20 for scales with more broad characteristics (Miller & Lovler, 2016).

The last caution is that alpha values can be too high than the expected values. Such high values may be a pointer to the existence of some aspects of redundancy among the individual items. In this case, it is recommended to use a maximum value of 0.90 to avoid distorted results (Conway et al., 2010).

Myths Associated with Alphas Proving Reliability

There are four major myths associated with alpha. These are:

1. That alpha is a fixed property of the scale on which it is used.
2. That alpha can only measure the internal consistency of a scale and that it is not a reliable measure of external consistency.
3. Higher values of alpha are always preferred to smaller ones.
4. That alpha is always restricted to the range of zero and one.

Propensity and Addiction

A test measuring propensity in addition to the Ability Test is sorely limited to assessments such as the Transmissible Liability Index (TLI). This assessment makes efforts to measure the propensity or natural inclination towards addiction by using genetics, characteristics of discriminant groups, such as the differences between those with or without addiction present in parents, and measures addiction psychopathology using psychological and psychiatric instruments (Conway et al., 2010).

Conclusions

Based on the context covered, contrary to the belief that the value of alpha must lie between 0 and 1, evidence from research shows that this value can lie between negative infinity and 1. Nonetheless, only positive values of alpha are of significance in statistical procedures. Although Cronbach's alpha is the most widely used measure of internal consistency, there are some fundamental problems with it. The main problem is that the alpha value is dependent not only on the number of scale items but also the extent of correlations among the items. As such, a scale can be manipulated to appear homogenous by simply doubling the total number of items.

References

Conway, K. P., Levy, J., Vanyukov, M., Chandler, R., Rutter, J., Swan, G. E., & Neale, M. (2010). Measuring Addiction Propensity and Severity: The Need for a New Instrument. Drug and Alcohol Dependence, 111(1-2), 4-12. http://doi.org/10.1016/j.drugalcdep.2010.03.011

Hicks, B. M., Iacono, W. G., &McGue, M. (2012). Index of the Transmissible Common Liability to Addiction: Heritability and Prospective Associations with Substance Abuse and Related Outcomes. Drug and Alcohol Dependence, 123S1, S18-S23. http://doi.org/10.1016/j.drugalcdep.2011.12.017

Maydeu-Olivares, A., Coffman, D. L., Garcia-Forero, C., & Gallardo-Pujol, D. (2010). Hypothesis testing for coefficient alpha: An SEM approach. Behavior Research Methods, 42(2), 618-625.

Miller, L. A., & Lovler, R. L. (2016). Foundations of Psychological Testing (5th ed.). Thousand Oaks, CA: Sage Publications.

Streiner, D. L. (2003). Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency. Journal of Personality Assessment, 80(1), 99-103.