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Lecture

# Research in Nursing - Ch.15-18 Vocabulary.doc

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School
Department
Nursing
Course
NUR1 422
Professor
John Hayes
Semester
Summer

Description
Chapter 15: Analyzing Quantitative Data nominal measurement – classification of attributes into mutually exclusive categories ordinal measurement – ranking of objects based on relative standing on an attribute interval measurement – indicating not only the ranking of objects but also the distance between them ratio measurement – distinguished from interval measurement by having a rational zero point descriptive statistics – enables researchers to synthesize and summarize quantitative data – data for a variable can be completely described in terms of shape of its distribution, central tendency, and variability frequency distribution – numeric values are ordered from lowest to highest, together with count of number (or percentage) of times each value was obtained skewed distribution – one tail longer than the other on curve (positively skewed or negatively skewed) unimodal distribution – one peak (one value of high frequency) multimodal distribution – more than one peak normal distribution – bell-shaped curve – symmetric, unimodal, and not too peaked central tendency – indicates average or typical value of a variable – mode, median, mean mode – value that occurs most frequently in a distribution median – point above which and below which 50% of the cases fall (middle number) mean – average of all scores – usually preferred measure of central tendency because of stability variability – how spread out the data are – range and standard deviation range – distance between highest and lowest scores standard deviation – indicates how much, on average, scores deviate from mean contingency table – two-dimensional frequency distribution in which frequencies of two nominal or ordinal level variables are cross-tabulated (against each other and see the relationship) correlation coefficients – describe the direction and magnitude of a relationship between two variables - -1 for perfect negative correlation, 0 for no relationship, +1 for perfect positive correlation product-moment correlation coefficient (Pearson’s r) – most commonly used coefficient – used with interval or ratio level variables – can be used to test whether a correlation is significantly different from zero inferential statistics – based on laws of probability – allow researchers to make inferences about a population based on data from a sample – offer framework for deciding whether the sampling error that results from sampling fluctuation is too high to provide reliable population estimates sample distribution of the mean – theoretical distribution of the means of an infinite number of same-sized samples drawn from a population – basis for inferential statistics standard error of the mean – SD of this theoretical distribution – indicates the degree of error of a sample mean – smaller SD error, the more accurate the estimates of the population value based on sample mean hypothesis testing – through statistical tests enables researchers to make objective decisions about relationships between variables type I error – occurs in null hypothesis is incorrectly rejected (false positive) type II error – occurs when null hypothesis is incorrectly accepted (false negative) level of significance (alpha level) – used to control risk of making Type I error – specifies the probability that such an error will occur – 0.5 level means that only 5/100 samples would the null hypothesis be rejected when should have been accepted beta – probability of committing Type II error – can be estimated through power analysis power – ability of a statistical test to detect true relationships – complement of beta – standard criterion for an acceptable level of power is 0.80 statistically significant – obtained results are not likely to be due to chance fluctuations at a given probability level (p level) parametric statistical tests – involve estimation of at least one parameter, use of interval or ratio level data, and an assumption of normally distributed variables non-parametric tests – used when data are nominal or ordinal and the normality of the distribution cannot be assumed t-test and analysis of variance (ANOVA) – two common statistical tests – used to test significance of difference between group means – ANOVA used when more than two groups chi-squared test – most frequently used nonparametric test used to test hypothesis about differences in proportions multivariable statistics – increasingly being used in nursing research to untangle complex relationships among three or more variables multiple regression – method for understanding effect of two or more predictor (independent) variables on a dependent variable multiple correlation coefficient (R) – can be squared to estimate the proportion of variability in the dependent variable accounted for by predictors analysis of covariance
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