Assessment, Statistics, and Research

  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.

  The bell curve is also known as the normal curve or normal distribution . The bell curve has mathematical  properties that allow researchers to draw conclusions about where scores (or data) are located relative to other scores (or data). Click hyperlinks for more details. The three measures of central tendency (mode, median, mean ) are at the same middle point in a normal curve. The numbers representing the middle of the bell curve divide the distribution in half. On the x -axis in the normal distribution, the mean is at zero and there are standard deviation units above and below the mean.  The height of the curve indicates the percentage of scores in that are a. You can see that a large percentage of the scores are between 1 and -1 standard deviations. About 68% of scores fall between +1 and -1 standard deviations from the mean.  Look at the illustration below to see that there are about 34% of the scores in falling one standard deviation above the mean and another 34% in one st

  The term correlation can refer to a statistic and a type of research.  Understanding correlations is an important building block of many complex ideas in statistics and research methods. My focus in this post is on the common correlation statistic, also called the Pearson r . The Pearson r is a statistical value that tells the strength and direction of the relationship between two normally distributed variables measured on an interval or ratio scale . Researchers examine the two sets of values and calculate a summary statistic called a correlation coefficient . The longer name for a common correlation statistic is the Pearson Product Moment Correlation Coefficient but sometimes it is referred to as the Pearson r . The symbol for correlation is a lower case and italicized r .  In behavioural research, we normally round values to two decimal points. A moderately strong positive correlation example is r = .78.       Sometimes, the relationship between the two variables is negativ