inferential

• Bivariate Analysis = includes measures of association.
• describing relationship

  • very weak or no = 0 - 0.20
• describing relationship

  • weak = 0.21 - 0.40
• describing relationship

  • moderate = 0.41 - 0.60
• describing relationship

  • strong = 0.61 - 0.80
• describing relationship

  • very strong = 0.81 - 0.99
• describing relationship

  • perfect = 1.00
• research question: descriptive

  • descriptive = mean, frequency
• research question: descriptive

  • complex = cross-tabulation, factor analysis
• research question: factor analysis

  • single = T-test, Z-test , one-way ANOVA
• research question: factor analysis

  • complex = factorial ANOVA
• research question: associational

  • basic = correlation
• research question: associational

  • complex = Pearson R; multiple regression
• Phi coefficient = mean square contingency coefficient
• Phi coefficient = a measure of association for two binary variables
• Phi coefficient = for nominal variables.
• a Phi coefficient of 0 would indicate that there is no systematic pattern across the 2x2 matrix.
• Phi correlation = introduced by Karl Pearson, it is similar to the Pearson correlation coefficient in its interpretation.
• Spearman Rho = a nonparametric measurement of rank correlation
• Spearman Rho = for ordinal, interval, or ratio variables.
• Spearman Rho = shows the statistical dependence between the ranking of two variables.
• Spearman Rho = named after Charles Spearman.
• spearman rho

  • +1 = perfect positive correlation between ranks
• spearman rho

  • -1 = perfect negative correlation between ranks
• spearman rho

  • 0 = no correlation between ranks
• Kruskal-Wallis H Test = one-way ANOVA
• Kruskal-Wallis H Test = rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or original dependent variable
• Kruskal-Wallis H Test = for ordinal variables.
• Kruskal-Wallis H Test = determines whether the medians of two or more groups are different
• Pearson R = a statistical formula that measures the strength between variables and relationships.
• Pearson R = a coefficient value that ranges between -1.00 (negatively correlated) to 1.00 (positively correlated).
• level of significance is the probability of rejecting a true Ho (null) hypothesis.
• a is conventionally set at 0.05, 0.01, or 0.1.
• interpretation of data

  • correlational value is equal or less than test value = accept the Ho
• interpretation of data

  • correlational value is more than test value = reject the Ho
• errors

  → type 1 = false positive error
• errors

  → type 1 = when the researcher concludes that the samples tested are from different populations (i.e., a significant difference exist between groups) when, in fact, the samples are from the same population (i.e., no significant difference exist between groups).
• errors

  → type 1 = null hypothesis is rejected when it is true.
• errors

  → type 1 = asserting something as true when it is actually false; “false alarm”
• errors

  → type 1 = e.g., a person is regarded as guilty when in reality, they are innocent.