Trivariate Hypothesis Testing with Tables & Examples
Explore the concept of trivariate hypothesis testing using tables and practical examples. Understand how to analyze correlations, non-spuriousness, and potential confounders in research. Discover relationship insights like storks and babies, and learn to construct and interpret trivariate tables efficiently.
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Presentation Transcript
TESTING TRIVARIATE HYPOTHESES USING TRIVARIATE TABLES HENK VAN DER KOLK
AIM Testing a bivariate hypothesis means studying Time order Correlation/association Non-spuriousness Theorizing potential confounders Formulating a trivariate hypothesis Testing the trivariate hypothesis Constructing a trivariate table 2
EXAMPLE Using confounding to show the idea of elaboration and trivariate tables , but it also applies to: interpretation, modification, addition etc 3
EXAMPLE Relationship between # of storks and # of babies (per capita) in municipalities 4
Title Table: Y by X (absolute numbers; column percentages)(N=...) Independent variable X Increasing order of values Low High Total Column percentages Negative Dependent variable Y Positive Total 5
Table: # of babies by # of storks (absolute numbers; column percentages)(N= 400 (municipalities)) # storks Low High Total 130 (65%) 70 Low 200 (35%) 70 130 (65%) 200 High # babies (35%) 200 (100%) 200 (100%) Total 400 6
CONFOUNDING IN A MODEL Degree of urbanization Number of storks Number of babies 7
CONFOUNDING IN A GRAPH Babies Not urbanized (1) low Very urbanized (10, highest) Storks 8
Table: # of babies by # of storks (absolute numbers; column percentages)(N= 400 (municipalities)) # storks Low High Total 130 (65%) 70 Low 200 (35%) 70 130 (65%) 200 High # babies (35%) 200 (100%) 200 (100%) Total 400 9
Table: # of babies by # of storks (absolute numbers; column percentages)(N= 400 (municipalities)) # storks Urbanized Not urbanized Low High Total (10 + 120 = ) 130 (65%) (30 + 40 = ) 70 (35%) (40 + 30 = ) 70 (35%) (120 + 10 = ) 130 (65%) Low 200 High # babies 200 Total 200 (100%) 200 (100%) 400 10
Table: # of babies by # of storks (absolute numbers; column percentages)(N= 400 (municipalities)) Not urbanized # storks Urbanized # storks Low High Total Low High Total 120 (75%) 30 10 40 Low 150 50 (75%) (25%) (25%) # babies 40 10 30 120 (75%) High 50 150 (25%) (25%) (75%) 160 (100%) 40 40 160 (100%) 200 Total 200 (100%) (100%) 11
CONFOUNDING IN A GRAPH Babies Not urbanized (1) low Very urbanized (10, highest) Storks 12
TESTING TRIVARIATE HYPOTHESES USING TRIVARIATE TABLES HENK VAN DER KOLK
