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Samenvatting Uitwerkingen ALLE casussen_Data analyse_

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Dit document betreft de uitwerkingen van ALLE casussen van data analyse van het schooljaar . Naast de wekelijkse casussen (1-5), bevat het document ook de uitwerkingen van de twee tentamenpreparatie casussen. De volgende casussen zijn opgenomen in het document: 1 - Simon 2 - Sleeping 3 - Effectiveness 4 - Spirituality 5 - Learning and Flexibility 6 - Data Preparation (PR) (tentamen) 7 - New Analysis (NA) (tentamen) Disclaimer: vragen over de programma's R, RStudio en G*Power worden in dit document niet behandeld omdat dit geen onderdeel uitmaakt van het tentamen. Voor het geval dat je een ander jaar deelneemt aan deze cursus: de wekelijkse assignments zullen qua inhoud overeenkomen met de casussen in dit document, ongeacht de titels van je casussen. Zodra de titel van jouw casussen overeenkomen met de titels van deze casussen, kun je de uitwerkingen identiek overnemen. Let wel op: de data kan per jaar iets verandert zijn, waardoor enkel de checks net een ander getalletje geven. Maar de uitwerkingen zijn in dit geval alsnog identiek!

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CASUS 1: SIMON
Description data
The files pp2_SGR.sav through pp11_SRR.sav contain the data of 10 of the 12 participants (the
amount of data in this case is less than what was collected in total). These data are already imported in
SPSS. The data of participants 1 and 12 are still in .txt format (pp1_SGR.txt and pp12_SRR.txt).
In the names of the files, GR indicates the green-right (red-left) mapping and RR indicates the red-
right (green-left) mapping.
Assignment 1
Convert the files of participants 1 and 12 into SPSS files. Ensure that the files are identical to the files
that are already imported in SPSS with respect to variables and labels and the like.
1. File  Import Data  Text Data  pp1_SGR.txt
2. Does your text file match a predefined format?  No
3. How are your variables arranged?  Delimited
4. Are variable names included?  No
5. What is the decimal symbol?  Period
6. How are your cases represented  Each line represents a case
7. Which delimiters appear between variables  Comma
8. Verander de naam van de variabelen: Digit, Color_field, Response, rt. Laat ze allemaal op
‘automatic’ staan.
9. Klik op finish
10. Voeg proefpersoonnummer toe met transform  compute variable  ppnr = 1
11. Save als pp1_SGR
12. Doe hetzelfde voor pp12_SRR
Assignment 2
Create two new data files, based on the individual files. Since the participants did not all receive the
same mapping of color and response side, it is recommended to create one data file for all of the
participants in the red-right mapping and one for all of the participants in the green-right mapping. You
can deduce this from the name of the file involved (see Description data). Before you address this on a
large scale (with all files), it might be clever to start small by first carrying out the whole assignment
with two files.
When merging, remember to give the new file a different filename than the existing files, so that you
do not overwrite the original data.
At the end of this step, ensure that you have two data files that include the data of all of the
participants with the same mapping containing the variables: ppnr (participant number), digit (0-9),
color_field (1-4), response (0 or 1) and rt (response time).
1. Open pp1_SGR.sav  data  merge files  add cases  klik aan: an external SPSS
Statistics data file: browse  kies pp2_SGR.sav
2. Voeg elk andere SGR pp toe.
3. Doe ditzelfde voor alle participanten van SGG.
4. Sla het SGR en SGG bestand op onder verschillende namen (all_pp_SGR & all_pp_SRR).
5. Om te checken of alle variabelen goed zijn: Analyze  Descriptive statistics 
Frequencies  kies voor minimum en maximum onder het kopje Statistics  Dispersion
 digit (0-9), color_field (1-4) en response (0-1) klopt allemaal.
Check: both data files contain the following variables: ppnr, digit, color_field, response and rt. Each of
the two files contains 3600 rows and 5 columns with data.
Assignment 3

,The variable color_field indicates in which color the digit was presented and in which visual field the
digit was presented. The variable color_field has four levels
 1 = color green and left visual field
 2 = color green and right visual field
 3 = color red and left visual field
 4 = color red and right visual field
For the remainder of this case, it is convenient to convert this variable into two new variables: color of
the stimulus and visual field of the stimulus.
In addition, the coding of the responses in the two different mappings is reversed.
 For the RR mapping, for the variable response, the coding is 0 = left and 1 = right
 For the GR mapping, for the variable response, the coding is 0 = right and 1 = left
It is convenient when the coding of left and right responses is the same in both files. Therefore, create
in both files a new variable, with the following coding: 0 = left and 1 = right Create the new variables
that are described above in both files.
Voor beide variabelen:
1. Transform  recode into different variables  2x een variable maken: color & field (vanuit
de variabele Color_field)
(Op het moment dat je de tweede variabele aan wil maken, zal Color_field niet aan te klikken
zijn zoals je gewend bent. Klik onder op reset voordat je de tweede variabele aanmaakt, dit
zorgt ervoor dat je Color_field wel gewoon een tweede keer kunt selecteren)
 Color:
o Range, lowest through 2: 0 (groen)
o Range, highest through 3: 1 (rood)
 Field:
o Value: 1 = 0, 3 = 0 (links), 2 = 1, 4 =1 (rechts).
In variable view  values (om betekenis eraan te geven, puur voor de handigheid).
Voor het bestand SGR:
2. Compute  maak 1=0 en 0=1 (om de twee bestanden gelijk aan elkaar te maken, wat betreft
links/rechts = 0/1).
Check: for both files, the digits are presented equally often in red and in green and are presented
equally often in the left visual field and in the right visual field. The number of left responses in the
GR file is 1815. The number of left responses in the RR file is 1806.
Om te checken of je dit goed gedaan hebt: analyse  descriptive statistics  frequencies  response:
1815 links bij GR en 1806 bij RR.
Assignment 4
Although the participants’ task in this kind of computerised tasks is often very simple, it is common
that participants nevertheless sometimes make an error. In addition, the responses on some trials are so
fast or so slow that they are considered unusable for further analyses. In this study, a decision was
made to exclude trials with response times shorter than 200 ms or longer than 1200 ms from the
analyses (but see, for example, Heathcote, Popiel, & Mewhort, 1991, for a discussion on trimming
data).

,a) Using SPSS, determine in which trials an error is made and which trials should be classified as
outliers. Do not delete these types of trials from the file but rather create new variables to indicate
which trials are correct and incorrect and which trials are outliers or are not outliers.
1. Transform  recode into different variables  rt (reaction time)
2. Voor beide bestanden (outliers): 0 = geen outlier | 1 = wel outlier




1 databestand is GREEN-RIGHT: juiste respons is groen-rechts (0,1) of rood-links (1,0)
(respectievelijk, kleur, veld).
1 databestand is RED-RIGHT: juiste respons is groen-links (0,0) of rood-rechts (1,1).
0 = groen | 1 = rood | 0 = links | 1 = rechts
Error: combinatie response & color.
3. Maak eerst een variabele die kleur en response optelt. Transform  Compute  Combi =
Color + Response
 GR: 1 (voor een goede respons)
 RR: 0 of 2 (voor een goede respons)
4. Transform  Recode into different variables  voeg de variabele Combi in (die we net
gemaakt hebben) 
 GR: 1 = 0 | 0 = 1 | 2 = 1 (want alleen 1 is geen error)
 RR: 0 = 0 | 2 = 0 | 1 = 1 (want 0 en 2 zijn geen error)
o 1 = error
o 0 = geen error
b) Subsequently, combine the two files into one file.
1. Data  Merge Files  Add cases  Select the other file.
Check: In total, in the GR mapping, 71 errors were made and there are 23 outliers. Participant 7 made
15 errors and has 3 outliers. (Suggestion: To get this check, try using the function Split file).
Analyze  Descriptive statistics  Frequencies  71 errors and 23 outliers in the GR mapping.
Data  Split file  Compare groups (‘Organize output by groups’ gives you a separate table for each
participant)  Groups based on: ppnr.  Analyze  Frequencies  Vul in: Errors & Outliers 
Participant 7 has 15 errors and 3 outliers.

, Assignment 5
If you did not successfully manage the previous assignments, use the file Simon_Assignment5.sav for
this assignment. Create a new file that contains, for each participant, a mean response time for every
combination of all conditions. There are 10 (digits) x 2 (visual field) x 2 (response hand) conditions in
the study, so eventually you should have 40 means for each participant.
Important! As you may have read, the mean response times are not calculated across all of the trials
that were presented to the participants. Instead, certain trials are excluded from further analyses (see
method section).
So, ensure that you first determine which trials should not be included in the calculations of the means.
Data  select cases  if condition is satisfied  if  outlier = 0 & correct = 0 (niet op basis van
‘Simon_Assignment5.sav’, maar op basis van mijn eerdere codering (0 = geen error en 0 = geen
outlier))  continue  OK
Data  Aggregate  Break Variable(s): ppnr, digit, visualfield, responsehand  summaries of
variables: MEAN(rt) en selecteer number of cases  Save: Create a new dataset  Fill in dataset
name.

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