Class 16

• The biggest mistake in quantitative research is to think that data analysis decisions can wait until after the data have been collected.
• Data analysis decisions must be fully aware of what analysis techniques will be used before data collection begins.
• Reported as a statistically significant probability (p) Comparing means and statistical significance Compare the mean of the explained variation (variation between the subgroups in the independent variable) in relation to means of the error variance (variation within each of the subgroups that make up the independent variable).
• The questionnaire, observation schedule, and coding frame should be designed with the data analysis in mind.
• The statistical techniques that can be used depend on how a variable is measured.
• Inappropriate measurement may make it impossible to conduct certain types of data analysis.
• The size and nature of the sample also imposes limitations on the kinds of techniques that are suitable for the data set.
• Nominal variables are the only difference that exists between participants is being in one category or another.
• Categories in a nominal variable cannot be ordered by rank and cannot do arithmetic or mathematical operations with the categories.
• Ordinal variables allow the categories of the variable to be rank ordered.
• The distance or amount of difference between categories in an ordinal variable may not be equal and cannot do arithmetic or mathematical operations with the categories.
• Interval/Ratio variables have a '0' start position and distance or amount of difference between categories is uniform.
• Univariate analysis is the analysis of one variable at a time.
• The first step in univariate analysis is often to create frequency tables for the variables of interest.
• Frequency tables show the number of times a particular variable shows up in the population, expressed as an actual number and as a percentage of the whole population.
• Combining categories in frequency tables makes the data more manageable and easier to comprehend.
• When interval/ratio variables are shown in frequency tables, some of the categories may be combined as long as they don't overlap.
• Diagrams can be used to illustrate frequency distributions.
• Use bar charts and pie charts, for displaying a nominal or ordinal variable.
• A null hypotheses tests the significance of the bivariate association, stating that there is no relationship between two variables, or that two populations do not differ on some characteristic.
• Values range from 0 to 1.
• To test for statistical significance, set up a null hypothesis, establish an acceptable level of significance, and if the null is correct there is no relationship.
• Correlation and statistical significance must be weighed together, as statistical significance speaks to the results not occurring by chance alone and does not speak to the importance of the results.
• Chi-square (Χ2) is used with contingency tables to measure the likelihood that a relationship between the two variables exists in the population, calculated by comparing the observed frequency in each cell with what would be expected by chance (if there were no relationship between the variables).
• Allows prediction of the second variable based on the score from the first.
• Comparing means and statistical significance is done through analysis of variance (F statistic), indicating there is a reduced likelihood of no relationship between the set of independent variables and the dependent variable.
• The chi-square value is affected by the sample size.
• Statistical significance is stated as a probability level and the probability that the results are not due to chance.
• If the null is rejected and the statistical significance (p) of the findings are ≤ .05 there is indirect support for the research hypothesis, meaning it is unlikely that the results occurred by chance.
• The amount of explained variance is represented by eta, Kendall's tau-b, Spearman's rho, and Pearson's r.
• Determines level of association between the two variables.
• Squaring shows how much the variation in one variable will explain variation in the other variable.
• Nominal categories cannot be rank ordered.
• The significance of a Pearson's r and a Kendall's tau-b correlation coefficient is determined by the size of the coefficient and the sample size.
• Comparing means and eta is used with an interval/ratio variable and a nominal variable, the nominal variable is the independent variable, and the means of the interval variable are compared for each subgroup of the nominal variable.
• The mean is the sum of all scores, divided by the number of scores, and can be used with interval/ratio data, but is vulnerable to outliers (extreme scores).
• Measures of central tendency include the mode, median, and mean.
• Standard deviation measures the amount of variation around the mean and is influenced by outliers.
• The mode is the score that shows up the most in a particular category and can be used with all variable types, but is most applicable to nominal data.
• Spearman's rho shows correlation between pairs of ordinal variables, values range from 0 to ± 1, and can predict a rank position from one variable to another.