Description
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The first thing the R script does is to give you a summary of the dataset. Scroll up to the top of the results and you will find a table that looks like this: CONSTRUCTION VERB REDUCED PARTICIPLE goal : 871 _zero:393 no :1353 no : 895 theme:1049 na :368 yes: 567 yes:1025 po :703 za :456 This table tells you how many items of each type are in each column of the dataset. Next comes the logistic regression analysis, which you find under the heading "Logistic Regression Model" in the R output. We used a procedure (following Baayen 2008 and Gries 2009) for discovering the minimal adequate model for our data. This means that we started with a hypothetical model in which all independent variables serve as both main effects and have interactions with each other, and then we progressively stripped away those that were not significant until we arrived at a model that represented only significant relationships. We will not walk you through this whole procedure, but just show you the optimal model. This model has all of the independent variables as main effects, plus an interaction between the VERB and PARTICIPLE variables. The formula for this model is represented this way in your R output (and in the R script): lrm(formula = CONSTRUCTION ~ VERB + REDUCED + PARTICIPLE + VERB:PARTICIPLE, data = loaddata, x = T, y = T, linear.predictors = T) This can be stated in prose thus: "CONSTRUCTION varies according to VERB, REDUCED, and PARTICIPLE as main effects, and an interaction between VERB and PARTICIPLE." Next comes a little table telling you the overall number of items for each value for the dependent variable CONSTRUCTION: goal has 871, and theme has 1049. Next come some figures that indicate how well the model performs. |