# 6052 Discussion 9

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Chapter 17 Study Sheet

Inferential Statistics

Kori Martinez, Sally Snyder, Blossom Jose, Susan Hensley, & Chinyere Etufugh

Inferential statistics are based on the laws of probability.  It is important for nurses to have a working knowledge of statistics to be able to understand and interpret data found in literature.  Enhanced patient care depends on nursing having the knowledge to know how to interpret evidenced based data.  Florence Nightingale first used statistics to improve sanitary conditions during the Crimean War (Giuliano & Polanowicz, 2008).

Description of Statistical Methods covered in chapter:

· Most hypothesis testing involves a two-tailed test, in which both ends of the sampling distribution are used to define the region of improbable values; one-tailed analysis may be appropriate if there is a strong rationale for an a priori directional hypothesis.

· Parametric tests involve the estimation of at least one parameter, the use of interval or ratio-level data, and assumptions of normally distributed variables; nonparametric tests are used when the data are nominal or ordinal or when a normal distribution cannot be assumed.

· Test for independent groups compare separate groups of people, and dependent groups compare the same group of people over time or conditions.

· McNemar's test is used on nominal data in a contingency table with a dichotomous trait, with matched pairs of subjects, to determine whether the row and marginal column frequencies are equal (McNemar's Test, 2017).

Key statistical tests:

· ANOVA: Analysis of variance is the parametric procedure for testing the difference between means when there are 3 or more groups. This is used to find out if they need to reject the null hypothesis or accept the alternate hypothesis. An example is when a researcher wants to test different groups: for instance; a group of psychiatric patients is trying three different therapies: counseling, medication, and biofeedback, the researcher wants to see if one therapy is better than the others (Polit & Beck, 2017).

· Paired t-test: Obtaining two measurements from the same people from a paired set of participants. This measures the difference between two related groups. An example of this includes testing the success rates, adverse symptom rate, and mortality of new drugs.

· Chi-square: is used to test hypotheses about differences in proportions. Fisher’s exact test should be used for small samples.

 Level of Measurement of Dependent Variable Group Comparisons: Correlation analyses (to examine relationship strength 2 Groups 3+ Groups Independent Group Tests Dependent Group Tests Independent Group Tests Dependent Group Tests Nominal (categorical) X2 (or Fisher’s test) McNemar’s test X2Chi-square Cochran’s Q Phi coefficient or Cramer’s V Ordinal (rank) Mann-Whitney test Wilcoxon signed ranks test Kruskal-Wallis H test Friedman’s test Spearmans’s rho (or Kendall’s tau) Interval or ration (continuous) Independent group t-test Paired t-test ANOVA RM-ANOVA Pearson’s r Multifactor ANOVA for 2+ independent variables RM-ANOVA for 2+groups x 2+ measurements over time

References

Giuliano, K., & Polanowicz, M. (2008). Interpretation and use of statistics in nursing research.

AACN Advanced Critical Care, 19(2), 211-222. doi:

10.1097/01.AACN.0000318124.33889.6e.

http://www.statisticssolutions.com/non-parametric-analysis-mcnemars-test/

Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for

nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer.

Response 1 Week 9

Kori, Sally, Blossom, Susan, and Chinyere,

I appreciate your post; I especially enjoyed your use of your table. It definitely brought everything together. According to Polit and Beck (2017), the chi-square test enables us to test hypotheses about group differences in proportions by summarizing between observed and expected frequencies for each cell. For me your table laid the differences in each test a little more clear.

Reference

Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for

nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer.