Data Analysis and Presentation Describing and Presenting Data Three important criteriaaccuracy, conciseness, and understandability Researchers should always present their data in ways that most accurately represent the data Numerical data can be classified as
numerical (example: percentages, (means) or graphical (graphs) method) Statistics Three areas: 1) Descriptive central tendency, dispersion measures 2) Relational univariate, bivariate, or multivariate statistics 3) Inferential difference of means, statistical significance tests Measures of Central Tendency
The meanaverage; most common and useful measure of central tendency; impacted by extreme scores The medianmiddle score of a distribution; less affected by extreme scores (outliers) The modemost frequent score Measures of Variability
Measures of Variabilitydescriptive statistics that convey information about the spread or variability of a set of data Variancea numerical index of the variability in a set of data Standard deviationa measure of variability that is equal to the square root of the variance Rangethe difference between the highest and lowest scores in a distribution
Relational Statistics 1) univariate study of one variable for a subpopulation (ex: age of murderers) 2) bivariate study of relationship between two variables (ex: correlation) 3) multivariate study of relationship between three or more variables (ex: multiple correlation) Correlation
Measure of the strength of some relationship between two variables, but not causality. Correlations can be positive, negative, or zero. Strength of relationship depends on coefficient. Correlations Correlation Strength
0.8 to 1.0 Very strong 0.6 to 0.8 Strong 0.4 to 0.6 Moderate 0.2 to 0.4 Weak
0.0 to 0.2 Very weak Inferential Tests Refer to a variety of tests for inferential purposes. 1. difference of means to test hypotheses, most common is Z-test. 2) statistical significance most common are t-test and chi-square (used for less
than interval data) Frequency Distributions A table that summarizes raw data by showing the number of scores that fall within each of the categories Pounds Lost by Dieter Frequency 0 5 1-5 10
6-10 15 Over 10 5 Frequency Histograms and Polygons
Sometimes information in frequency distributions is more easily grasped when it is presented graphically Histogram is used when horizontal (xaxis) variable is measured on an interval or ratio scale (bars on graph touch each other) If data is nominal or ordinal, the bars do not touch each other and it is a bar graph Graphical Methods of Reporting Data 30 25 20 0 pounds 1-5 pounds 6-10 pounds
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