Maybe others understand what you are wanting to do but I’m confused. So, first, “hierarchical logistic regression”, to do this analysis are you using the logistic
command or the genlin or genlinmixed commands. “Hierarchical” has multiple meanings and I don’t know which you mean. Let’s say you are using logistic. What would the plots look like? Are you wanting one plot for each IV or something that displays multiple
IVs? So for a dichotomous IV, wouldn’t a bar chart work: percent Yes for IV1=0 and percent Yes for IV1=1. For a continuous IV, you are going to need to categorize the IV values so that you can get the percent Yes for each IV value segment.
So far as I know you have two choices. One choice is GGraph; the other is Graph. From the Graphs dropdown, Chart builder is, I think, ggraph and Legacy dialogs
is Graph. If these are “look-at and discard” graphs, the legacy dialogs are much, much easier to use, but if you want much nicer looking graphs, then chart builder.
Also, if you will do a archives search for the Andy W, you can look at example ggraph examples. Andy is a graphing artist.
Gene Maguin
From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of Kseniya Katsman
Sent: Tuesday, April 04, 2017 5:48 PM
To: [hidden email]
Subject: Plotting – hierarchical logistic regression, chi square
I have two plotting questions, please forgive me if the questions seem trivial:
1. I'm trying to understand how I can plot hierarchical logistic regression in SPSS or alternatively in Excel after exporting the data. I have 3 models and would like to make a plot for the final one. All the variables (dependent and independent) are dichotomous
(hierarchical binomial logistic regression).
2. This question is about plotting chi square results. The dependent variable is dichotoous, but not all independent variables are. Naturally, I will be making several plots, but I would be thankful for a simple explanation on what is the process. For now the
only examples I've seen included ordinal or continuous dependent variables, which makes it easier.
Thank you!
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