# Design Equation and Statistics

Chapter
Part of the Health Informatics book series (HI)

## Abstract

The previous chapter discussed the different types of variables one has to ­understand to design a study. This chapter uses the experimental design equation to show how these variables contribute to variability in results. The independent variable is assumed to affect the scores of the dependent variable. Other variables need to be controlled so that the effect of the independent variable can be clearly seen. Then, an overview of how to test the changes that the different levels of independent variables bring about is provided. Descriptive statistics are introduced first. These statistics describe the results, such as mean and standard deviation. Then inferential statistics or statistical testing follows. Statistical testing allows the researchers to draw conclusions about a population of users based on a study that involves a ­sample. Underlying, essential principles, such as the standard distribution and the central limit theorem, are reviewed, followed by the three tests most commonly performed in informatics: t-test, ANOVA and chi-square.

## Keywords

Null Hypothesis External Validity Decision Support System Central Limit Theorem Internal Validity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

## References

1. 1.
Kirk RE (1995) Experimental design: procedures for the behavioral sciences, 3rd edn. Brooks/Cole Publishing Company, Pacific GroveGoogle Scholar
2. 2.
Fang J-Q (2005) Medical statistics and computer experiments. World Scientific Publishing Co. Pte. Ltd., SingaporeGoogle Scholar
3. 3.
Gravetter FJ, Wallnau LB (2007) Statistics for the behavioral sciences, 7th edn. Thomson Wadsworth, BelmontGoogle Scholar
4. 4.
Raymondo JC (1999) Statistical analysis in the behavioral sciences. McGraw-Hill College, BostonGoogle Scholar
5. 5.
Kurtz NR (1999) Statistical analysis for the social sciences. Social sciences – statistical ­methods. Allyn & Bacon, Needham HeightsGoogle Scholar
6. 6.
Vaughan L (2001) Statistical methods for the information professional: a practical, painless approach to understanding, using, and interpreting statistics. Commercial statistics. Information Today, Inc, New JerseyGoogle Scholar
7. 7.
Ropella KM (2007) Introduction to statistics for biomedical rngineers. synthesis lectures on biomedical engineering. Morgan & Claypool. doi:10.2200/S00095ED1V01Y200708BME014Google Scholar
8. 8.
Ross SM (2004) Introduction to probability and statistics for engineers and scientists, 3rd edn. Elsevier, BurlingtonGoogle Scholar
9. 9.
10. 10.
Lewin IP (1999) Relating statistics and experimental design. Quantitative applications in social sciences. Sage, Thousands OaksGoogle Scholar
11. 11.
Rosenthal R, Rosnow RL (1991) Essentials of behavioral research: methods and data analysis. McGraw-Hill, BostonGoogle Scholar
12. 12.
Smith CE, Dauz ER, Clements F, Puno FN, Cook D, Doolittle G, Leeds W (2006) Telehealth services to improve nonadherence: a placebo-controlled study. Telemed J E Health 12(3):289–296
13. 13.
Heywood C, Beale I (2003) EEG biofeedback vs. placebo treatment for attention-deficit/hyperactivity disorder: a pilot study. J Atten Disord 7(1):43–55
14. 14.
Leykin Y, DeRubeis RJ, Gallop R, Amsterdam JD, Shelton RC, Hollon SD (2007) The relation of patients’ treatment preferences to outcome in a randomized clinical trial. Behav Ther 38:209–217
15. 15.
Sidani S, Miranda J, Epstein D, Fox M (2009) Influence of treatment preferences on validity: a review. Can J Nurs Res 41(4):52–67
16. 16.
Baker L, Wagner TH, Signer S, Bundorf MK (2003) Use of the internet and e-mail for health care information: results from a national survey. J Am Med Assoc 289(18):2400–2406
17. 17.
Maisiak RS, Berner ES (2000) Comparison of measures to assess change in diagnostic ­performance due to a decision support system. In: AMIA Fall Symposium. AMIA, pp 532–536Google Scholar
18. 18.
Van der Feltz-Cornelis CM, Nuyen J, Stoop C, Chan J, Jacobson AM, Katon W, Snoek F, Sartorius N (2010) Effect of interventions for major depressive disorder and significant depressive symptoms in patients with diabetes mellitus: a systematic review and meta-analysis. Gen Hosp Psychiatry 32:380–395