Research Design Decision Tree

josman9
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Mind Map on Research Design Decision Tree, created by josman9 on 07/09/2013.

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josman9
Created by josman9 over 6 years ago
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Research Design Decision Tree
1 How Many Variables?
1.1 1 VARIABLE
1.1.1 Scale of Measurement?
1.1.1.1 Nominal
1.1.1.1.1 Info about Distribution?
1.1.1.1.1.1 Central Tendency: tables for modal value
1.1.1.1.1.2 Distribution: tables for frequency of modal value or class
1.1.1.1.1.3 Frequencies: tables for relative and absolute
1.1.1.2 Ordinal
1.1.1.2.1 Info about Dispersion?
1.1.1.2.1.1 Central Tendency: tables for median
1.1.1.2.1.2 Dispersion: need the inter-quartile deviation
1.1.1.2.1.3 Frequencies: tables for relative and absolute
1.1.1.3 Interval
1.1.1.3.1 Info about Distribution?
1.1.1.3.1.1 Symmetry: calculate skewness
1.1.1.3.1.2 Dispersion:
1.1.1.3.1.3 Central Tendency:
1.1.1.3.1.3.1 Skewed: compute the mean and median
1.1.1.3.1.3.2 Symmetric: compute the mean
1.1.1.3.1.4 Normality:
1.1.1.3.1.4.1 Normality: Kolmogorov-Smirnov one-sample test, Lilliefors extension of Kolmogorov-Smirnov test, Chi-square goodness-of-fit test, the Jarque-Bera test, D'Agnostino-Pearson K-squared test, Shapiro-Wilk test. Skewness & kurtosis: D'Agnostino-Pearson K-squared Jarque-Bera
1.1.1.3.1.5 Frequencies:tables for relative and absolute. Consider requesting n-tiles
1.1.1.3.1.6 Peakedness: compute the kurtosis of a variable
1.1.1.3.1.6.1 To test departures from normality: for N greater than 1000, refer the critical ratio of the kurtosis measure to a table of the unit normal curve; for N between 200 and 1000, refer the kurtosis measure to a table for testing kurtosis; for N less than 200, use Geary's criterion.
1.2 2 VARIABLES
1.2.1 Scale of Measurement?
1.2.1.1 1 Interval, 1 Nominal
1.2.1.1.1 Is interval variable dependent?
1.2.1.1.1.1 YES: measure of strength or test of significance?
1.2.1.1.1.1.1 Test of significance:
1.2.1.1.1.1.1.1 assuming homoscedasticity across levels of ind. variable, perform an analysis of variance and F-test for significance
1.2.1.1.1.1.1.2 With no homoscedasticity across levels of ind. variable, use ANOVA. For hypothesis testing use the Welch statistic, the Brown-Forsythe statistic, or the t-test
1.2.1.1.1.1.2 Measure of strength:
1.2.1.1.1.1.2.1 Use the ANOVA, and Omega Squared Intraclass Correlation Coefficient Kelley's Epsilon Squared
1.2.1.1.1.2 NO: ANOVA to perform an analysis of variance
1.2.1.2 1 Nominal, 1 Ordinal
1.2.1.2.1 compute the Friedman test and probability of chance occurrence.
1.2.1.2.2 Use Freeman's coefficient of differentiation, theta
1.2.1.3 1 Interval, 1 Ordinal
1.2.1.3.1 If ordinal is based on an underlying normally distributed interval variable, use Jaspen's Coefficient of Multiserial Correlation
1.2.1.4 Both Nominal
1.2.1.4.1 Both variables 2-point scale?
1.2.1.4.1.1 YES: What will be measured?
1.2.1.4.1.1.1 Symmetry: Use McNemar's test of symmetry; it is equivalent to Cochran's Q
1.2.1.4.1.1.2 Covariation: use Yule's Q Phi
1.2.1.4.1.2 NO: At least one is not a 2-point scale and one is considered an independent variable
1.2.1.4.1.2.1 Statistic based on number of cases in each category
1.2.1.4.1.2.1.1 use Goodman and Kruskal's tau b
1.2.1.4.1.2.2 Statistic based on number of cases in modal categories
1.2.1.4.1.2.2.1 calculate the asymmetric lambdas A and B
1.2.1.5 Both Ordinal
1.2.1.5.1 Distinction between dependent & independent variables?
1.2.1.5.1.1 YES: use Somer's d for 2 ordinal variables
1.2.1.5.1.2 NO: What do you want to measure?
1.2.1.5.1.2.1 Agreement: no applicable statistic, but data may be transformed to ranks and r or Krippendorff's r used
1.2.1.5.1.2.2 Covariance: depending on if the ranks are treated as interval scales, use Kendall's tau-a, tau-b, tau-c Goodman and Kruskal's gamma, Kim's d, or Spearman's rho (rs)
1.2.1.6 Both Interval
1.2.1.6.1 Distinction between dependent & independent variables?
1.2.1.6.1.1 YES: looking for linear relationship?
1.2.1.6.1.1.1 YES: use the F-test, also computed by Regression
1.2.1.6.1.1.2 NO: Curvilinear relationships- use the F-test, computed by Regression, equal to t-squared, for each coefficient
1.2.1.6.1.2 NO: looking for equal means on both variables?
1.2.1.6.1.2.1 YES: calculate the t-test for paired observations
1.2.1.6.1.2.2 NO: treat the relationship as linear What do want to measure?
1.2.1.6.1.2.2.1 Agreement: penalty without same distribution?
1.2.1.6.1.2.2.1.1 YES: Robinson's A or the intraclass correlation coefficient. The test is the F-test.
1.2.1.6.1.2.2.1.2 NO: Use Krippendorff's coefficient of agreement
1.2.1.6.1.2.2.2 Covariance:
1.2.1.6.1.2.2.2.1 Use Pearson Product-Moment r (correlation coefficient), Biserial R, or Tetrachoric r depending on how many of the variables are dichotomous
1.3 More than 2 Variables
1.3.1 [Didn't Learn This]

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