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Showing posts with the label Reliability


  The Spearman-Brown formula estimates test score reliability of a full-length test when using a split-half method of reliability. The split-half method divides a test in two and calculate a correlation between the scores of the two halves. Longer tests are yield higher reliability values so the Spearman-Brown formula estimates the reliability value for the full test. Other names for the Spearman-Brown formula are Spearman-Brown Prophecy Formula, Spearman-Brown Correction. Learn more about test statistics in Buy Applied Statistics for Counselors   GOOGLE BOOKS   AMAZON Please check out my website     and see my books on    AMAZON          or    GOOGLE STORE Also, consider connecting with me on      FACEBOOK     Geoff W. Sutton           TWITTER    @Geoff.W.Sutton       You can read many published articles at no charge:    Academia    Geoff W Sutton        ResearchGate    Geoffrey W Sutton  

Coefficient Alpha or Cronbach's Alpha

  Coefficient Alpha (also called "alpha") is a statistical value indicating the degree of internal consistency of items in a multiple-item scale like survey items or Likert-type scales. Internal consistency is one measure of reliability for scores from scales, measures, and survey items. The alpha statistic was developed by Lee Cronbach in 1951 thus it is also called Cronbach's alpha . In research reports, you may just see the Greek lower case letter alpha,  α. The procedure to calculate alpha can be found in SPSS under Analyze > Scale > Reliabilty. For research purposes, scales with alpha levels equal to or above alpha = .70 are acceptable. The best scales have values of alpha = .9 or higher. The alpha method works best to evaluate unidimensional measures. If there are two or more dimensions in a set of items, the alpha value will be lower so, when alpha values are low, consider which item or items do not support the primary dimension. Cite this Post Sutton, G.W.

Metaphors Can Interfere with Understanding Survey Items and Results

Photo for illustration purposes only “If Jesus is God, how could he create the world if he wasn’t born yet.”                      —Girl, age 7 It will be a while until this 7-year-old passes through the stage of concrete operations and begins to pull apart various mental constructs in a serious fashion. Along the way she’ll pick up many metaphors, including those that unravel men’s thinking about God hundreds of years ago. And all sorts of other metaphors. Americans are known for being religious and in particular, for being Christian; however, as is commonly said, the devil is in the details . In this post, I look at religious survey items to make a point about being careful when writing and interpreting survey items containing concepts with a range of meaning. ********* God- Who is God? Gallup keeps tabs on Americans’ views on God. In an interesting article, Hrynowski ( 2019 ) reveals a different response rate for beliefs in God depending on how the question is asked. Spec

Measurement Error Standard Error of Measurement

In testing, measurement error usually refers to the fact that the same people can obtain different scores on the same test at different times. In a broad sense, measurement error can also refer to the degree of accuracy of a test to correctly identify a condition, which is discussed as test validity. Recall that test score reliability is a necessary but insufficient condition for test score validity. Many tests in psychology, medicine, and education are useful. The reliability of the scores will vary depending on such factors as the properties of the test itself as well as how well the user follows standard procedures in administering the test, environmental factors that can affect the scores, and factors within the person taking the test. The scores on many tests conform to the pattern called the normal curve or bell curve. In classical test theory, the scores people obtain on tests are simply called obtained scores (symbol X). Statisticians consider the variation in scores t