Parametric Vs Nonparametric Analysis

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statistics (Goldstein, 2011; Gay, 2010; Snijders & Bosker, 2012).
There are two types of statistical operations to which research data can be subjected for making of appropriate inferences about the population from the sample. They are parametric statistics and nonparametric statistics. It must be reiterated emphatically that of these two, the parametric statistics is incomparably more powerful, sensitive, appropriate, accurate and suitably desirable classically. A more powerful statistical test is that which can detect a small but real difference or relationship in the sample while simultaneously still being able to reject non-real difference or relationship that might be apparent. Parametric statistics are the most widely or commonly used
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Homoscedasticity demands that the
STATISTICAL ANALYSIS WITH SPSS FOR RESEARCH
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different samples should be having about equal variance.
(d) The data must be collected at interval and/or ratio scales of measurement to accommodate arithmetic operations of addition, division, subtraction, and multiplication that they will be subjected to.
Examples of parametric statistical tests include t-tests, z-tests, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA), product moment correlation, regression and multiple regression.
Nonparametric statistics on the other hand, use data that are collected at lower levels of measurement (nominal and ordinal scales). They have lower efficiency and do not require homogeneity of variance as well as normally distributed population. In short, nonparametric statistical tests are distribution-free statistical procedures and are less powerful. Also, they do not require the stringent conditions for parametric tests. Examples of nonparametric tests include Mann-Whitney, Wilcoxon test, Kruskal-Wallis test, sign test, median test, rank order correlation, Friedman test, Hodges-Lehmann test for two samples and chi-square (χ2).
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McQueen and Knussen (2006) have therefore averred that if the population under investigation contains both males and females of varying ages, the sample must also be made up of males and females of varying ages proportionally. The sample should necessarily be composed of the same cross section of persons or elements that make up the population in proportional distribution in terms of relevant characteristics such as age, gender, educational level, occupational status, cultural background and marital status. The sample has to be a true representative of the population from which it was drawn to guarantee equivalence of the characteristics of the persons that constitute them. It is on this basis that findings of the sample can correctly be generalized validly to cover the population. It is however worthy of note that in many areas of cognitive psychology such as testing of visual acuity, or response to audio signals, or memory; and in some medical research, it might be safe to assume that some measures will not vary as a result of gender and personality, or regional location and culture (Waltz, Strickland & Lenz 2016). This accounts for why the Paracetamol, for instance, for males and females do not differ in

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