CMNS 260 Lecture Notes - Lecture 11: Falsifiability, Null Hypothesis, Scatter Plot
10 May 2016
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Cmns 260 week 11: introduction to quantitative data analysis. Continue our discussions of how to design and read results of quantitative analysis. Introduce bivariate descriptive statistics: present some basic approaches to statistical analysis of relationships. Reading and interpreting statistical tables of bivariate relationships. Are the results meaningful: methods for studying relationships, use contingency tables to produce cross-tabulations & proportions/ percentages, create graphs, charts (like scattergrams or plots, use measures of association. Past keyword: a hypothesis is a statement from a causal expalanation or a proporsiiton that has at least one independent and one dependent variable, but it has yet to be empirically tested. The 5 characteristics of causal hypothesis: at least 2 variables, causal relationship between these variables, expressed as a prediction or an expected future outcome. 4. logically linked to the research question (and therefore to a related theoretical.
