1. PY550 Research and Statistics Dr. Mary Alberici Central Methodist University

2. Descriptive Statistics (describing data with numerical indices or graphs): Summary Statistics  Mean Median Standard Deviation Percentile Frequency Distribution Summary Tools  Pie charts Histograms Boxplots Scatterplots

3. Mean: Sum of the scores in a distribution / # of scores = the most commonly used measure of central tendency Median: The middle point in a distribution, with exactly 50% of scores above it and 50% below it. Standard Deviation: The most stable measure of variability because it takes into account each and every score in a distribution. Percentile: The score below which a given percent of a known group scores… i.e., the 90 th percentile is a score of x (depending on the size of the group). Frequency Distribution: A tabular method of showing all the scores obtained within a group or sample population.

4. Pie chart: a circular graphic that displays data in divisions (categories) within a circle. Histogram: A graphic of comparative rectangles that represent the scores in a frequency distribution. Boxplot: A diagram that shows a 5-number summary. Scatterplot: A graphic drawn from points around coordinate axes, illustrating relationships between two quantitative variables.

5. Inferential Statistics: Used to make comparisons between groups or to study relationships… i.e., how likely results based on a sample are generalizable to entire population. Estimation Confidence Interval Hypothesis Testing (p.228-229)

6. SPSS is often used as a data collection tool by researchers. The data entry screen in SPSS looks much like any other spreadsheet software. You can enter variables and quantitative data and save the file as a data file. You can organize your data in SPSS by assigning properties to different variables. For example, you can designate a variable as a nominal variable, and that information is stored in SPSS. The next time you access the data file, which could be weeks, months or even years, you'll be able to see exactly how your data is organized.

7. A data file must be structured in a particular way so that SPSS can use it. SPSS uses data organized in rows and columns. The rows are cases and the columns are variables. A case contains information for one unit of analysis, such as a person, an animal, a business, or a machine. Variables are the information collected for each case, such as age, body weight, profits, salaries, or fuel consumption. SPSS can import data from existing database files (i.e., Excel).

8. Qualitative variables are categories that are non-numeric, such as gender, race, or blood-type. Quantitative variables are categories that can be counted or measured, such as GPA or salary or height or weight.

9. Data can also be categorized by one of four commonly used measurement scales: 1. Nominal (for non-numeric group labels like gender, male could be assigned 0 and female 1) 2. Ordinal (discrete data that is categorical and rankable, as in agree to strongly disagree may be ranked 1 to 5) 3. Continuous interval (discrete data where the difference between two values is meaningful, but the ration between them is not… for example, IQ, or temperature… it’s not very meaningful to say today is twice as hot as yesterday) 4. Continuous ratio (discrete data where both the difference and the ration between values have meaning… for example, weight or salaries)

10. Once data is collected and entered into the data sheet in SPSS, you can create an output file from the data. For example, you can create frequency distributions of your data to determine whether your data set is normally distributed. The frequency distribution is displayed in an output file. You can export items from the output file and place them into a research article you're writing. Instead of recreating a table or graph, you can take the table or graph directly from the data output file from SPSS.

11. The most obvious use for SPSS is to use the software to run statistical tests. SPSS has all of the most widely used statistical tests built-in to the software, so you won't have to do any mathematical equations by hand. Once you run a statistical test, all associated outputs are displayed in the data output file. You can also transform your data by performing advanced statistical transformations. This is especially useful for data that is not normally distributed.

12. Normal distributions are symmetrical statistical distributions characterized by bell-shaped density curves with a single peak, as in the diagram above. http://stattrek.com/probability-distributions/normal.aspx http://www-stat.stanford.edu/~naras/jsm/NormalDensity/NormalDensity.html These websites will help you to better understand this important concept.

13. Launch SPSS. The Data Editor window will open. The variable names appear in the list dialogs (if you have already loaded data). It's usually best when these variable titles are in alphabetical order.

14. From the "Edit" menu, select "Options." Then select the "General" tab. Select "Display labels" in the Variables list group. Select "Alphabetical" and then click "OK" twice.

15. Open a Data File. To do so, go to the "File" menu and select "Open" and "Data." The Open File dialog will display. Double-click the "Tutorial" folder, double- click "sample file_folders," click the file "demo.sav" and click "Open." From the "View" menu, select "Value Labels."

16. Run an Analysis. To do so, go to the "Analyze" menu and select "Descriptive Statistics" and "Frequencies." The Frequencies dialog will be displayed and the icons will provide the information needed about the data type and level of measurement

17. http://hmdc.harvard.edu/projects/SPSS_Tutorial /spsstut.shtml http://pages.infinit.net/rlevesqu/spss.htm http://www.datastep.com/SPSSTraining.html http://calcnet.mth.cmich.edu/org/spss/toc.htm