Week 10: Understanding Statistics in Research - PDF

College of Health Sciences Bachelor of Science in Nursing (BSN) Program NUR-3253 Research Methodology for Nursing By: Prof. Farida Habib Dr. Amina Elzainy.2 Week 10 Understanding Stati...

College of Health Sciences Bachelor of Science in Nursing (BSN) Program NUR-3253 Research Methodology for Nursing By: Prof. Farida Habib Dr. Amina Elzainy.2 Week 10 Understanding Statistics in Research Part II ©McGraw-Hill Education 3 Outlines Inferential statistics Parametric and non-parametric tests ©McGraw-Hill Education.4 Learning Objectives By the end of this presentation the students will be able to: 1. Interpret the results obtained from inferential statistical analyses. 2. Compare and contrast statistical significance and clinical importance of results. 3. Describe the process of inferring from a sample to a population. 4. Discuss the distribution of the normal. 5. Critically appraise statistical results, findings, limitations, conclusions, and generalization of findings. ©McGraw-Hill Education 5 What are Inferences? A conclusion or judgment based on evidence Inferential statistics, based on the laws of probability, provide a means for drawing conclusions about a population, given data from a sample. Judgments are made based on statistical results. Statistical inferences must be made cautiously and with great care. Estimating population parameters from sample data Testing hypotheses SLO 1: Interpret the results obtained from inferential statistical analyses ©McGraw-Hill Education 6 Parametric and Nonparametric Tests There are two broad classes of statistical tests, parametric and nonparametric. Parametric tests :involve estimation of a parameter, require measurements on at least an interval scale, and involve several assumptions, such as the assumption that the variables are normally distributed in the population. ©McGraw-Hill Education 7 Parametric and Nonparametric Tests Nonparametric test: They involve less restrictive assumptions about the shape of the variables’ distribution than do parametric tests. Parametric tests are more powerful than nonparametric tests and are usually preferred. ©McGraw-Hill Education 8 Chi-Square Test of Independence Used with nominal or ordinal data Tests for differences between expected frequencies if groups are alike and frequencies actually observed in the data ©McGraw-Hill Education 9 Example of Chi-Square Table Exercise No exercise Total Male 35 15 50 Female 10 40 50 Total 45 55 100 ©McGraw-Hill Education 10 Chi-Square Results Indicate that there is a significant difference between some of the cells in the table The difference may be between only two of the cells, or there may be differences among all of the cells. Nonparametric inferential technique appropriate for comparing nominal sets of data ©McGraw-Hill Education 11 Pearson Product-Moment Correlation Tests for the presence of a relationship between two variables – Called bivariate correlation Types of correlation are available for all levels of data. Best results are obtained using interval data. ©McGraw-Hill Education 12 Correlation Performed on data collected from a single sample Measures of the two variables to be examined must be available for each subject in the data set. Results – Nature of the relationship (positive or negative) – Magnitude of the relationship (–1 to +1) – Testing the significance of a correlation coefficient ©McGraw-Hill Education 13 Correlation Results r = 0.56 (p = 0.03) r = –0.13 (p = 0.2) r = 0.65 (p < 0.002) Which ones are significant? ©McGraw-Hill Education 14 Regression Analysis Used when one wishes to predict the value of one variable based on the value of one or more other variables For example, one might wish to predict the possibility of passing the credentialing exam based on grade point average (GPA) from a graduate program. ©McGraw-Hill Education.15 Regression Analysis (Cont.) Regression analysis could also be used to predict the length of stay in a neonatal unit based on the combined effect of multiple variables such as gestational age, birth weight, number of complications, and sucking strength. ©McGraw-Hill Education 16 Regression Analysis (Cont.) The outcome of analysis is the regression coefficient R. The R2 is also called the coefficient of multiple determination. ©McGraw-Hill Education 17 t-Test Requires interval level measures Tests for significant differences between one or two samples means. Most commonly used test of differences Parametric test assumptions Compares means of interval or ratio data ©McGraw-Hill Education 18 t-Test Parametric test that examines differences between the means of two groups of values Independent t test or unrelated samples t test – Test for independent samples Eg. 2 different groups Dependent t test or paired t test – Used when scores or values are associated or have some connection Eg. pre post test for the same sample ©McGraw-Hill Education 19 Analysis of Variance (ANOVA) Tests for differences between means More flexible than other analyses in that it can examine data from more than two groups Parametric statistical Based on assumptions that data are: – Interval or ratio level – Have been selected from populations that are normally distributed – Have equal variances on the variable that is being measured ©McGraw-Hill Education 20 ANOVA (Cont.) Can look at between-group variance, within-group variance, and total variance Compares difference among more than two means Allows the researcher to simultaneously analyze difference between several means ©McGraw-Hill Education 21 Results of ANOVA If there are more than two groups under study, it is not possible to determine where the significant differences are. Post hoc tests are used to determine the location of differences. ©McGraw-Hill Education 22 What is a normal curve? A theoretical frequency distribution of all possible values in a population No real distribution exactly fits the normal curve. However, in most sets of data, the distribution is similar to the normal curve. Levels of significance and probability are based on the logic of the normal curve. ©McGraw-Hill Education 23 Types of Results Significant and predicted results Non significant results SLO 2:Compare and contrast statistical significance and clinical importance of results. ©McGraw-Hill Education 24 Significant and Predicted Results Are in keeping with those predicted by researcher and support logical links developed by researcher among the framework, questions, variables, and measurement tools ©McGraw-Hill Education 25 Nonsignificant Results Also called negative or inconclusive results Analysis showed no significant differences or relationships. Could be a true reflection of reality. If so, the researcher or theory used by researcher to develop hypothesis is in error. In this case, negative findings are an important addition to the body of knowledge. ©McGraw-Hill Education 26 Levels of Acceptable Significance < 0.05 statistically significant * < 0.01 statistically highly significant** < 0.001 very highly significant *** ©McGraw-Hill Education  Exercise Several researchers conducted different studies. Indicates the significance the of their results P = 0.03 P = 0.0712 P = 0.0265 P = 0.1002 P = 0.00231 P = 0.000234 P = 0.543 P = 0.00458 ©McGraw-Hill Education  Answer Several researchers conducted different studies. Indicates the significant the of their results P = o.03 Statistically significant P = 0.0712 Statistically not significant P = 0.0265 Statistically significant P = 0.1002 Statistically not significant P = 0.00231 Statistically highly significant P = 0.000234 Statistically very highly significant P = 0.543 Statistically not significant P = 0.00458 Statistically highly significant ©McGraw-Hill Education 29 Implications The meanings of conclusions for the body of nursing knowledge, theory, and practice Based on, but more specific than, conclusions Provide specific suggestions for implementing the findings ©McGraw-Hill Education 30 Generalization A generalization is the application of information that has been acquired from a specific instance to a general situation. Generalizing requires making an inference. Both inference and generalization require the use of inductive reasoning. ©McGraw-Hill Education 31 Generalization (Cont.) An inference is made from a specific case and extended to a general truth, from a part to a whole, from the known to the unknown. In research, an inference is made from the study findings to a more general population. ©McGraw-Hill Education 32 Empirical Generalizations Are based on accumulated evidence from many studies Are important for verification of theoretical statements or for development of new theory Are the basis of a science Contribute to scientific conceptualization ©McGraw-Hill Education 33 Generalizing the Findings Extends the implications of the findings: – From the sample studied to a larger population – From the situation studied to a more general situation How far can generalizations be made? ©McGraw-Hill Education 34 Suggesting Further Studies Researcher gains knowledge and experience from conducting the study that can be used to design a better study next time. Researcher often makes suggestions for future studies that logically emerge from the present study. ©McGraw-Hill Education 35 Critical Appraising Statistics in a Study What statistics were used to describe the characteristics of the sample? Are the data analysis procedures clearly described? Did statistics address the purpose of the study? Did the statistics address the objectives, questions, or hypotheses of the study? Were the statistics appropriate for the level of measurement of each variable? SLO 3: Critically appraise statistical results, findings, limitations, conclusions, and generalization of findings. ©McGraw-Hill Education 36 Summary of Statistical Tests Chi square: Nonparametric inferential technique appropriate for comparing nominal sets of data Correlation: Tests for the presence of a relationship between two variables Regression analysis: Used when one wishes to predict the value of one variable based on the value of one or more other variables ©McGraw-Hill Education 37 Summary of Statistical Tests T test: Tests for significant differences between one or two samples means. ANOVA: Tests for differences between means. More flexible than other analyses in that it can examine data from more than two groups ©McGraw-Hill Education 38 Critical Thinking Questions List examples of parametric and nonparametric statistics. ©McGraw-Hill Education 39 Critical Thinking Questions Explain types of results. ©McGraw-Hill Education