Transcript Basic SPSS for Students

PY550 Research and Statistics
Dr. Mary Alberici
Central Methodist University
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
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 90th 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.
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.
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)
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.
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).
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.
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)
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.
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.
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.
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.
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.
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, doubleclick "sample file_folders," click the file
"demo.sav" and click "Open."
From the "View" menu, select "Value Labels."
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
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