Longer tests generally produce higher reliabilities because the percentage of measurement error decreases as test length increases. You can read more about item discrimination below.Īnother way to improve test reliability is lengthening the test. Reliability and discrimination are positively correlated: Questions with high discrimination increase test reliability, while questions with low discrimination decrease test reliability. Questions with high discrimination are good because they discriminate between high performing students and low performing students. To determine the quality of test questions, we use item discrimination. One way to improve test reliability is creating good quality questions. Grading factors: inconsistent scoring guidelines, carelessness, counting or computation errors Test factors: questions that are too hard, confusing or unclear questionsģ. Student factors: motivation, concentration, fatigue, boredom, lapses of memory, careless in marking answers, luck in guessingĢ. These errors arise due to the following factors:ġ. Measurement errors can cause students to answer questions correctly or incorrectly. Reliability is important in testing because it checks how much students’ test scores reflect random measurement errors. A rule of thumb states that instructor-created tests with a reliability greater than or equal to 0.70 are acceptable. The value of reliability can range from 0.0 to 1.0. The Kuder and Richardson Formula 20 is used to calculate reliability. If a test has high reliability, it is consistent to the point that students would achieve close to the same results if they were administered that same test multiple times. In testing, reliability is a measure of consistency. In the summary report, we provide the reliability of the test. To begin, we collect these data from a group of people.How to Interpret GradeHub’s Statistical Reports Reliability In this example, we are interested in the relationship between height and gender. And if you have at least one ordinal variable, you should use Spearman’s Rho or Kendall’s Tau instead. If you have two continuous variables, you should use Pearson Correlation. Some good examples of binary variables include smoker(yes/no), sex(male/female) or any True/False or 0/1 variable. Some good examples of continuous variables include age, weight, height, test scores, survey scores, yearly salary, etc.īinary means that your variable is a category with only two possible values. Continuous means that the variable can take on any reasonable value. Other types of analyses include testing for a difference between two variables or predicting one variable using another variable (prediction).įor this test, you should have one continuous and one binary variable. You are looking for a statistical test to look at how two variables are related. You should use Point-Biserial Correlation in the following scenario:ġ)You want to know the relationship between two variablesĢ)Your variables of interest include one continuous and one binary variable The assumptions for Point-Biserial correlation include:
Assumptions mean that your data must satisfy certain properties in order for statistical method results to be accurate. Alternately, you could use a point-biserial correlation to determine whether there is an association between cholesterol concentration, measured in mmol/L, and smoking status (i.e., your continuous variable would be “cholesterol concentration”, a marker of heart disease, and your dichotomous variable would be “smoking status”, which has two categories: “smoker” and “non-smoker”).ĪSSUMPTIONS OF POINT BISERIAL CORRELATIONĮvery statistical method has assumptions. It is a special case of the Pearson’s product-moment correlation, which is applied when you have two continuous variables, whereas in this case one of the variables is measured on a dichotomous scale.įor example, you could use a point-biserial correlation to determine whether there is an association between salaries, measured in US dollars, and gender (i.e., your continuous variable would be “salary” and your dichotomous variable would be “gender”, which has two categories: “males” and “females”). A point-biserial correlation is used to measure the strength and direction of the association that exists between one continuous variable and one dichotomous variable.
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