A Jeanne Site
A Jeanne Site
California State University, Dominguez Hills
University of Wisconsin, Parkside
Latest update: August 29, 1999
jeanne.
Where is Burbules and Calister's "Risky Promises"?
Where are the Statistics Exercises?
Where is the Statistics Syllabus?
You could look up either Burbules or Risky Promises in the Site Index.
Or you could look up either Burbules or Calister in the Author Index.
Burbules and Callister article, "Risky Promises" External Site
Once you have the exercise you want up, then go to the task bar and click on the Netscape icon that starts with "Risky..." Now you are running two versions of Netscape. The windows should be cascaded. You can get from the article to the exercise just by clicking on the edge of the other window. Or you can go down to the task bar and click on the one you want. Both will be represented there by icons.
Note: This technique takes full advantage of the potential of Windows 95, 98, and NT. If you have an earlier version of Windows, you won't have all these options. Play with it though, or maybe print out the exercise. In the lab, you have Windows 95 - use it's taskbar advantages!
It's on the Net Guide page. You could get there from the Table of Contents from the Statistics Site home page. Or you could click on the link to the Activity of the Web-Related Exercise. Or you can click on the link here:
LINK to Activity for Web-Related Exercise 2.
It's on the Net Guide page. You could get there from the Table of Contents from the Statistics Site home page. Or you could click on the the link to the Activity of the Web-Related Exercise. Or you can click on the link here:
LINK to Activity for Web-Related Exercise 3.
It's on the Net Guide page. You could get there from the Table of Contents from the Statistics Site home page. Or you could click on the the link to the Activity of the exercise. Or you can click on the link here:
LINK to Activity for Extra Credit Web-Related Exercise 1.
Those assumptions are in the Introduction to Curran and Takata's Statistics. They say basically that when we judge facts by observing behavior, we are assuming what people believe on the basis of their behavior. When we judge facts by what people say, we are assuming what people would do on the basis of what they say. Both of these are risky assumptions.
Simon says you may go back and read the introduction and say "thank you" for the reminder.
LINK to the Comments. Click on any one of them to read the comments.
LINK back to the Hatcher article itself.
Now you have both available to check the dates.
Babbie and Halley, p. 39, next to last para..
A variable whose values can be arranged in order.
Example: age:
Go to to Dr. Nelson's Web assignment: He suggests that you go to the Location box in which the URL is typed, erase the end directories, until you come to the last one after the DNS. Try accessing that, and you may very well get the homepage of the person who wrote the site.
Now try it. Go to Burbules' and Callister's "Risky Promises. . ." and find Nicholas Burbules home page.
The category that has the highest frequency in it. For example, at a senior citizen's home, the age category "over 65" would probably be the mode.
See Babbie and Halley, p. 39.
A source book that should be included with every data set (including your own, so prepare one) that tells you what the variables were, how they were measured or defined, and what were the values that they could take on.
Babbie and Halley give you part of a codebook on pp. 19-21. You would need to supplement this within the SPSS program by clicking on Utilities ->Variables -> choice of a variable from the list of variables in the left frame. Then you will see the various values the researchers allowed for that variable.
Most important thing to remember: Choice are made by the researcher at every level of this process. There is no objective world in which research decisions are made neutrally by people uninfluenced by theory and situatedness. Nota bene: This statement reflects Jeanne's beliefs as a postmodernist. Jeanne is not without situation and subjectivity either.
A percentage that is calculated using only those respondents who actually answered the question. Missing values are not included.
So, if you asked 50 people in this class if they had learned anything, and if 42 said yes, and 5 said no, and 3 didn't answer ore weren't present, you would calculate a valid percentage of those saying yes as 42/47 x100%. If you used 42/50 x100% you would be including the 3 missing values in your calculation as a no. That would be inaccurate. SPSS, when it gives a valid percentage is not counting the missing values in the sample size or denominator.
See Babbie and Halley, p. 38.
A cumulative percentage adds all the percentages for each category up to that point. See, Babbie and Halley, p.38.
Example, the frequency distribution of religious identification on B and H, p.37. 63.1% of the respondents were Protestant. 23.9% were Catholic. If we collapse those categories to Christian, then we can use the cumulative percentage, saying that 87%of the respondents were Christian. This using valid percentages, so that means that we're only including people who actually answered the quetion on religious preference.
From either the data editor or the output window, select from the menu along the top of the window:
Statistics -> Summaries -> Frequency
If nothing happens, remember that you have to select a variable. Do this by selecting from either the data editor or the output window, select from the menu along the top of the window:
Utilities -> Variables
And then you must select the variable you want by highlighting it in the left frame and by clicking the arrow to move it to the frame on the right. The arrow should be active (not grayed out) from left to right.
Nota bene: If you have a pre-selected variable in the frame on the right, you need to highlight it by clicking on it, then move it back to the list of variables in the left frame (while the variable is highlighted) by clicking on the arrow which will now be active in a right to left direction..
Look through the Net Guide section in the Table of Contents to the Dear Habermas Site. Of Link directly to the Net Guide.
Basically, it means that you have more information than you really want to use. For example, you may have asked your respondents to tell you their age. So you have ratio data on age. But you don't really want to analyze age with that much detail. You just want to know whether your respondents are teen-agers, adults, or older adults. Collapse all the possible categories for age into teen-agers (from 12 to 20), adults (from 21 to 55), and older adults (56 and over). You ahve more data on age, but you don't want to use it. You have essentially thrown out information.
There is no way to analyze data you did not get. So you can't turn nominal data or ordinal data into higher level of data. If you didn't collect the information, you didn't collect it. That's it. But when you collected more detail than you need, such as number of years of education, or actual age, you can throw some of that out by simply recoding it into categories that make sense for your analysis.
Another way to collapse categories is what we did with church attendance. You might have asked people to choose from ten categories, then collapse those ten into three by just adding some of them together.
Yet another way to collapse categories is to throw out the information from one whole category to make sure the categories you retain are really different. If you measured anger on a scale of 1-10, then you might want to analyze one category as all those who scored 1-2, ignore 3-4 altogether, make the middle category 5-6, then ignore 7-8 altogether, and make the highest category 9-10. What this does is give you more donfidence that the three categories you analyzed will really be different from one another because they will be several points away on the scale. Imagine that someone who scored 3 may not be very different from someone who scored 2. But hopefully, someone who scored 5 or 6 will be considerably different from someone who scored 2. You throw out information in the interest of making your analysis more accurate in terms of the validity of measurement.
Go back to the Babbie and Halley text, pp.45-53. Run through recoding "attend" to "chatt" again.
Try this to get you oriented:
Put the book disk in Drive a.
Be sure that you are in SPSS for Windows, Data Window. That's the one with all the little cell boxes!
Click on File in the main SPSS menu at the top of the window.
Click Open.
Click Data.
You should have an Open File Dialog Box.
On lab machines: Go to the file box, where sometimes *.sav is highlighted.
Backspace to erase the *.sav.
Type in the file name box: a:\gss.sav
Click OK and wait patiently for several seconds.
The data should appear in the cells. Now you may begin to run the statistics.
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