Tag: SPSS

Leveraging My Technology Learning Tools

Twitterfeed
I am sandwiching in (actually eating donuts!) some brief writing time between my two labs for Statistics and Experimental Design (PSY205). The students seem to have indeed come in prepared, having looked at the screen cast I had made using Screenflow for them about how to do a two-way, between subjects ANOVA with SPSS. One of my students offered to use Quizlet to make a review for their fellow students of the “language of statistics.”
Student research assistant Alison has been at work in my office since 8:00 updating our Macs with the new WordPress app. With no formal direction from me, she has reviewed our latest blogs, given thought as to how we can (and if we should) move up in the “elearning feeds ratings” and investigated how to use CreateSpace for the book we shall be writing together. I alert her to several more tools i want her and my other three student assistants to explore: Scoop.it, Paper.li, and either Feedly or Inoreader. She bounces the idea off me of my mentoring a Carroll Pioneer Scholar research program about technology learning tools.
I check my Twitter feed and discover  Much Ado about Nothing brouhaha among Wikipedia editors. That might make my colleague and Shakespearean scholar  (and Object Lesson author) John Garrison laugh. I share it with John. Time to head back to teaching my 2nd lab. Technology CAN be a tool as long as I don’t allow it to control me.


Learning by Teaching: Alison's and Lizzy's Guide to Using SPSS Data Analysis for Simple Linear Regression

DSCN8480One of the many lessons I’ve learned from many years of teaching is how much I learn through the act of teaching. It recently occurred to me that one way to facilitate my students’ learning of statistics is to position them to teach it. Below is a video created by two of my students illustrating how to use and interpret SPSS’s procedures for creating a scatter plot, calculating Pearson’s r, and, if warranted, performing a simple linear regression. Here is what they wrote and did:
 
This video was designed to help demonstrate an SPSS analysis for a simple linear regression. This video helps to show the steps to obtain an analysis of data, but the steps are also printed below for further assistance.

Step 1) Enter the names of the data into the variable view. For our data, the first name is Global Awareness which is the “independent variable” while the second name is “Satisfaction” which is the dependent variable. The data will come up automatically as numeric, but change the decimals to 0. Once complete hit the data view.
Step 2) Enter data under the appropriate name.
Step 3) To see if several of Pearson r’s assumptions are met  first create a scatter plot. To create the scatter plot, go underneath graphs, legacy dialogues, and then click on scatter/dot. Then a pop up menu will appear and select simple scatterplot, which is the first option. Then SPSS will ask you for the x and y axis. The X is the independent variable while the Y is the dependent variable.
Step 4) When the scatter plot appears, notice the  direction (positive or negative), the strength of the scatter plot, and if the scatter plot is linear. If the scatter plot is linear, calculate Pearson’s r.
Step 5) To calculate Pearson’s r, go under Analyze, Correlate, than select bi-variate, and a pop up menu will ask you for the independent and dependent variable. Make sure the Pearson box is selected as well as the two tailed box.
Step 6) To calculate the linear regression, go under Analyze, Regression, and select linear. A pop up menu will ask for the independent and dependent variable.
To understand the data:
Pearson’s r indicates how strong the two variables are correlated.
r squared is the coefficient of determination which communicates how much of the Y variable is explainable by knowing the X variable.
The standard error of estimate is the range around a predicted score within which you are sure with a specified degree of certainty that the predicted score will indeed fall.
Underneath the coefficients table in the B column, one is able to see the y predicted equation (Ypredicted = Bx + A). B is going to be the next to the independent variable while the A is going to be next to the constant.