Questions and Topics We Can Help to Answer
Paper Instructions;
Evidence-based nursing practice relies on research procedures and a DNP prepared nurse must be familiar with statistical approaches because the inappropriate use of them will result in faulty conclusions that would undermine the significance of the study. Parametric tests include estimation of a parameter, require measurements on at least an interval scale, and involve multiple assumptions, such as the one that the variables are normally distributed in the sample population (Polit & Beck, 2016). They are usually preferred compared with the nonparametric ones.
Inferential statistics derive conclusions about a population sample by analyzing the data. Using inferential statistics helps the researcher make objective judgments about the reliability of estimates. The basis for inferential statistics are sampling distributions with their standard deviation called standard error of the mean ( Polit & Beck, 2016). The decision on when to use inferential analysis is based on the purpose of the study and the type and number of each type of variable. According to Rice (2002), determination of the number of dependent and independent variables is important in deciding appropriate statistical procedures and studies with multiple independent and dependent variables require inferential statistical procedures compared with those with single independent or dependent variables.
Multivariate statistics involve the concurrent observation and analysis of more than one outcome variable. One commonly used technique is the multiple regression analysis which involves the investigation of the effects of two or more independent variables on a continuous dependent variable (Polit & Beck, 2016). This statistical method aims at predicting outcomes. According to Polit and Beck (2016), regression analysis solves for a and b, to form a prediction about Y for any value of X and the researchers focus on trying to improve predictions of Y by including multiple independent variables (predictors).
Nonparametric tests, also called distribution-free statistics, do not estimate parameters, make fewer assumptions about the shape of the variables’ distribution, and are less powerful than the parametric ones (Polit & Beck, 2016). However, they are useful with small sample size studies, when data cannot be interpreted as interval-level, and with distributions that are not normal. According to Nahm (2016), the advantages of nonparametric tests consist in a less of a possibility to reach incorrect conclusions because assumptions about the population are unnecessary, they are more intuitive and do not require much statistical knowledge, and statistics are computed based on signs or ranks; therefore not greatly affected by outliers.
References
Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: the basic concept and
the practical use. Korean Journal of Anesthesiology, 69(1), 8-14.
Polit, D. F. & Beck, C. T. (2016). Nursing research: Generating and assessing evidence for
nursing practice (10th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
Rice, M. H. (2002). Statistical analyses: Making sense of them in the research report. Journal of
Neuroscience Nursing, 34(2), 105.
