In addition to the previous two blog entries, here are a few more tips you should consider as you review my comments from the first BSF and prepare to submit BSF2:
1. Never analyze nominal data (and many ordinal level measures) using the mean and standard deviation. For example, a mean of 1.55 and standard deviation of .623 on the variable sex (1=male, 2=female) is an absolutely meaningless set of statistics. Hence, when you describe categories associated with nominal data, only a percentage analysis is warranted. Note: When you’re ready to make inferences about the population from nominal level sample data (e.g., using “Chi-Square” along with the “Contingency C Coefficient”), then you are conducting inferential analyses, not descriptive analyses. This effort involves testing hypotheses and estimating population parameters which we previously discussed in class. I raise this latter issue because, in BSF2, you are also responsible for constructing non-directional and directional hypotheses of difference and association from the list of variables found in the QBA_Database.sav file. See my previous blog for a discussion on constructing these hypotheses.
2. Be sure to examine the VALID PERCENT column of a table, not the PERCENT column because the valid percent excludes all missing cases in the tally. Related to this, you can report the number/percentage of missing cases tied to the PERCENT column. However, unless you’re asked to do so or you have a pressing, legitimate reason to do so, don’t bother (or put the missing information in a footnote). Only report those categories that appear in the VALID PERCENT column without noting information on missing cases.
3. When you gaze at a crosstabular table with a potentially large number of row and column percentages, be selective in what you ultimately choose to report. Do not report everything you see. Identify and report on the highlights (maybe 4-6 key points). Where do you begin? As we discussed in class, center on categories of the Y (dependent variable) and, within those categories, report on the X's (independent variable) differences. Do not analyze the X categories and look at how they vary across Y. For example, you may find that 60 percent of the sample has “A GREAT DEAL” of confidence in America’s banks and financial institutions (i.e., one category of Y). Of that percentage, are females more likely than males to comprise this group? What are the percent differences who report “A GREAT DEAL”? Note: We are not testing hypotheses when using crosstabs. We’re just gaining a two-variable (bivariate) understanding of the data and reporting what we found.
4. When you’re requested to provide graphic evidence of the statistical outcomes in an assignment, integrate each selected table (or chart) below its respective discussion and be sure to reference the table (or chart) either in the discussion or at the paragraph’s end, i.e., “(see Table 1A).” You should never present graphic information in a report without references to it in your discussion. To this end, always add the corresponding table number at the top of a table (or bar chart) to the left of the table’s description.
5. When you’re presenting graphic outcomes in a bar or pie chart, add value labels in each bar or wedge in the chart. How? Once the chart is generated in SPSS, click on it and another window will open. Look for the ICON that looks like a bar chart, click and the value labels (percentages, for example) will automatically appear in the bar chart.
6. In the univariate section of your report, please use paragraphs to logically separate parts of your discussion, especially where the discussion has become statistically tedious (e.g., lots of percentages reported) and long. This means you should have a separate, labeled paragraph for nearly every variable in your report. If there are two related variables, such as AGE and AGEORD (i.e., age recoded at the ordinal level), combine the discussion into one paragraph. In this example, the result will be a paragraph that not only covers the mean and standard deviation of AGE, but also a percentage breakdown of AGEORD by the recoded variable’s categories.
7. If you have trouble fitting a table or chart on one page in a WORD document, try the following (besides reading my prior blog on this subject): Generate the table or chart in SPSS, edit it to make the necessary changes (such as change the title, add “TABLE #,” add percent labels to the bars in a chart, etc.), then right click to COPY the table/chart into WORD. In your WORD document, click PASTE SPECIAL and choose PICTURE (Windows Metafile). That will paste the graphic image into WORD. From there, you can click on the graphic and adjust/resize it smaller to fit a page. However, don’t make it so small that the title or data can’t be read.
1. Never analyze nominal data (and many ordinal level measures) using the mean and standard deviation. For example, a mean of 1.55 and standard deviation of .623 on the variable sex (1=male, 2=female) is an absolutely meaningless set of statistics. Hence, when you describe categories associated with nominal data, only a percentage analysis is warranted. Note: When you’re ready to make inferences about the population from nominal level sample data (e.g., using “Chi-Square” along with the “Contingency C Coefficient”), then you are conducting inferential analyses, not descriptive analyses. This effort involves testing hypotheses and estimating population parameters which we previously discussed in class. I raise this latter issue because, in BSF2, you are also responsible for constructing non-directional and directional hypotheses of difference and association from the list of variables found in the QBA_Database.sav file. See my previous blog for a discussion on constructing these hypotheses.
2. Be sure to examine the VALID PERCENT column of a table, not the PERCENT column because the valid percent excludes all missing cases in the tally. Related to this, you can report the number/percentage of missing cases tied to the PERCENT column. However, unless you’re asked to do so or you have a pressing, legitimate reason to do so, don’t bother (or put the missing information in a footnote). Only report those categories that appear in the VALID PERCENT column without noting information on missing cases.
3. When you gaze at a crosstabular table with a potentially large number of row and column percentages, be selective in what you ultimately choose to report. Do not report everything you see. Identify and report on the highlights (maybe 4-6 key points). Where do you begin? As we discussed in class, center on categories of the Y (dependent variable) and, within those categories, report on the X's (independent variable) differences. Do not analyze the X categories and look at how they vary across Y. For example, you may find that 60 percent of the sample has “A GREAT DEAL” of confidence in America’s banks and financial institutions (i.e., one category of Y). Of that percentage, are females more likely than males to comprise this group? What are the percent differences who report “A GREAT DEAL”? Note: We are not testing hypotheses when using crosstabs. We’re just gaining a two-variable (bivariate) understanding of the data and reporting what we found.
4. When you’re requested to provide graphic evidence of the statistical outcomes in an assignment, integrate each selected table (or chart) below its respective discussion and be sure to reference the table (or chart) either in the discussion or at the paragraph’s end, i.e., “(see Table 1A).” You should never present graphic information in a report without references to it in your discussion. To this end, always add the corresponding table number at the top of a table (or bar chart) to the left of the table’s description.
5. When you’re presenting graphic outcomes in a bar or pie chart, add value labels in each bar or wedge in the chart. How? Once the chart is generated in SPSS, click on it and another window will open. Look for the ICON that looks like a bar chart, click and the value labels (percentages, for example) will automatically appear in the bar chart.
6. In the univariate section of your report, please use paragraphs to logically separate parts of your discussion, especially where the discussion has become statistically tedious (e.g., lots of percentages reported) and long. This means you should have a separate, labeled paragraph for nearly every variable in your report. If there are two related variables, such as AGE and AGEORD (i.e., age recoded at the ordinal level), combine the discussion into one paragraph. In this example, the result will be a paragraph that not only covers the mean and standard deviation of AGE, but also a percentage breakdown of AGEORD by the recoded variable’s categories.
7. If you have trouble fitting a table or chart on one page in a WORD document, try the following (besides reading my prior blog on this subject): Generate the table or chart in SPSS, edit it to make the necessary changes (such as change the title, add “TABLE #,” add percent labels to the bars in a chart, etc.), then right click to COPY the table/chart into WORD. In your WORD document, click PASTE SPECIAL and choose PICTURE (Windows Metafile). That will paste the graphic image into WORD. From there, you can click on the graphic and adjust/resize it smaller to fit a page. However, don’t make it so small that the title or data can’t be read.
I hope this helps!
Professor Ziner
Professor Ziner
No comments:
Post a Comment