This document contains assignment 1 and 2 from the labsessions, with supportive screenshots. You can print this out and keep it with you during the exam, for me this was all I needed! It is completely in English.
Assignment 2: SPSS Syntax, Data Handling & T-test 7
Assignment 2.1: Creating Syntax (basic) 7
Assignment 2.2: Data screening and data handling 11
Assignment 2.3 One sample t-test 17
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,Assignment 1: introduction
Assignment 1.1: introduction to SPSS
Please right-click on the link and choose “Save Link As”. Then, save the file on your computer.
Then, open the file in SPSS. Proceed as follows: via the op menu in SPSS follow the route:
File -> open -> data. SPSS now opens a new window. Search for the file ‘Stress.sav’ and open the file in
SPSS.
After opening the data file, you will see two tabs (Data View and Variable View) at the bottom-left of your
SPSS screen. You can click the information button below to help you find them.
Make sure you are looking at the Variable View tab.
The variable view gives an overview of the variables in the data set. Each row in the variable view
represents a variable. The columns describe the properties of the variable. This includes the following.
● Name: A technical name for the variable. It’s an unique variable name to be used by SPSS. It has
some restrictions (e.g., may contain no blanks, should start with a letter, no special signs (e.g.,
%), and can have a limited number of characters). Think of an SNR for a student. It’s a technical
code that we use in our administration system for your results (See more details about names
under the info button).
● Label: A label is a description of the variable. Think of the student’s name. The label doesn’t have
to be unique, but of course it is not very convenient to use the same label for different variables.
● The difference between the name and the label is as follows: the name is a (short) technical code
for the variable. It should be unique. The label is a description of the variable, which will also
appear in the output. So, it’s important to use meaningful labels, so it makes reading and
understanding the output easier for you and your fellow researchers.
● Values: values are relevant if you have nominal or ordinal variables. For each value you can
specify a label that describes the meaning of the value (so called value labels).
● Measure: it specifies the measurement level. We distinguish between three levels: nominal,
ordinal, and scale.
Now switch to the Data View tab.
You will now see the data on your screen. In other words, it shows the scores on all variables for all cases
(participants) in the dataset.
Explain in a couple of sentences what the rows and columns represent in the Data View.
rows: the answers of one participant for all the questions asked
columns: all given answers for the question
Now, switch back to the variable view tab.
As you have learned from the lectures, every variable has a so-called level of measurement (nominal,
ordinal, interval, or ratio). In SPSS, this information is shown in the column “measure” in the variable view.
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, By clicking on a cell in this column, you can select one out of three options. Note that interval and ratio
measurement level are no options in this list. Instead, SPSS calls them both Scale!
Write down the measurement level for each of the variables in this dataset. In case of an nominal or
ordinal measurement level, also write down the value labels of the variable. You can find these value
labels in the column “values”. You can see all information on the values by clicking the cell and then
clicking the three blue dots.
We make a basic distinction between three types of data, known as measurement levels.
· Nominal data: numbers express groups membership. Nominal variables classifies cases into
two or more categories. Categories must be exhaustive (all possibilities should be covered)
and mutually exclusive (every case fits into one category and one category only).
Examples: male/female, yes/no, college/no college, marital status.
· Ordinal data: numbers express an ordering (less/more). Numbers express more or less a
quantity, but the difference between 1 and 2 is not the same in quantity as between 2 and 3, 3
and 4, and so on. Example: Smoking intensity 1=never, 2=occasionally, 3=regular 4=heavy.
· Interval and ratio (scale-level): Data express differences in quantity using a common unit.
Examples: temperature, IQ,
Within scale data we can distinguish between interval-level and ratio-level.
· Ratio-level data have a natural zero-point. (grades, length, income). As a result, you can
compare the relative magnitude of things, you can say someone is twice as tall as another.
· Interval variables don’t have natural zero-point, but are arbitrarily chosen and can differ
across scales (0 Fahrenheit is not the same as 0 Celsius). Is not a natural zero, it’s chosen.
· Both interval and ratio are referred to as scale data. The idea is simple: all variables that are
not nominal or ordinal are treated as scale levels variables. Likert Scale: it doesn’t really
matter if it’s interval or ratio: that’s why everything is scale-level.
Assignment 1.2: Descriptive statistics
The first step in any statistical analysis involves inspection of the data at hand by means of descriptive
statistics and graphical summaries. This includes descriptive statistics such as the mean, standard
deviation, minimum and maximum value, amongst others. Graphical summaries include scatter plots, bar
charts, and histograms.
This assignment shows you how to compute descriptive statistics and create plots in SPSS.
We will now generate the descriptive statistics.
Proceed as follows: In the top menu, select: analyze -> descriptive statistics -> descriptives. SPSS opens
a new menu. Select the variables Optimism, life satisfaction and negative emotions. In this menu,
click on the tab options and make sure that the boxes for the mean, standard deviation, minimum, and
maximum are checked. If they are checked, click on continue and on OK. SPSS now opens a new
window - the output window - including a table with the descriptives.
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