Statistical-Tools that are commonly used in college

Commonly used stat tools.

1. One group of students is exposed to the contextual framing approach; the other to conventional teachin g approach. Is the performance of students exposed to contextual framing approach significantly higher than those exposed to conventional approach? What can you say? Situation 1

2. A research study was conducted to examine the clinical efficacy of a new antidepressant. Depressed patients were randomly assigned to one of three groups: a placebo group, a group that received a low dose of the drug, and a group that received a moderate dose of the drug. After four weeks of treatment, the patients completed the Beck Depression Inventory. The higher the score, the more depressed the patient. The data are presented below. What can you say? Situation 2

3. The Frequency and Percentage

4. The Weighted Mean

5. Hypothesis testing is an essential procedure in statistics. It is used to evaluate two mutually exclusive statements about a population to determine which statement is best supported by the sample data. Hypothesis testing is important because it helps you decide 1. if something really happened, 2. if certain treatments have positive effects 3. if groups differ from each other 4. if one variable predicts another Hypothesis testing also provides a way for researchers to correctly analyze their data before making any decisions. We can’t leave anything to chance alone. 4. Parametric vs Non-Parametric Test

6. 3. What is hypothesis testing, and why do it.

7. One group of students is exposed to the contextual framing approach; the other to conventional teachin g approach. Is the performance of students exposed to contextual framing approach significantly higher than those exposed to conventional approach? What can you say? Situation 1

8. A research study was conducted to examine the clinical efficacy of a new antidepressant. Depressed patients were randomly assigned to one of three groups: a placebo group, a group that received a low dose of the drug, and a group that received a moderate dose of the drug. After four weeks of treatment, the patients completed the Beck Depression Inventory. The higher the score, the more depressed the patient. The data are presented below. What can you say? Situation 2

9. Parametric and nonparametric are two broad classifications of statistical procedures. Parametric tests are based on assumptions about the distribution of the underlying population from which the sample was taken. The most common parametric assumption is that data are approximately normally distributed. Nonparametric tests do not rely on assumptions about the shape or parameters of the underlying population distribution. If the data deviate strongly from the assumptions of a parametric procedure, using the parametric procedure could lead to incorrect conclusions. 4. Parametric vs Non-Parametric Test

10. You should be aware of the assumptions associated with a parametric procedure and should learn methods to evaluate the validity of those assumptions. If you determine that the assumptions of the parametric procedure are not valid , use an analogous nonparametric procedure instead. The parametric assumption of normality is particularly worrisome for small sample sizes (n < 30). Nonparametric tests are often a good option for these data. Nonparametric procedures generally have less power for the same sample size than the corresponding parametric procedure if the data truly are normal. 4. Parametric vs Non-Parametric Test

11. 4. Parametric vs Non-Parametric Test

12. 5. Common Statistical Tools

13. 5. Common Statistical Tools

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18. How to interpret strength of relationship?

19. What are standardized beta coefficients? The beta coefficient represents the estimated average change in standard deviation units. So, a beta coefficient of 0.5 means that every time the independent variable changes by one standard deviation, the estimated outcome variable changes by half a standard deviation, on average.

20. Visit: www.socscistatistics.com

21. Visit: www.socscistatistics.com

22. Kendall’s Coefficient of Concordance (W) A measure that uses ranks to assess agreement between observers  (Kendall and Babington Smith 1939) similar to Spearman's rank correlation coefficient (1904). The objective was to test the utility of Kendall's W for determining the level of agreement among N observers.