Personal Data
Watch this 3-minute video that explains what personal data are:
According to Article 4(1) GDPR, personal data is defined as any information relating to an identified or identifiable natural person (also named data subject.) This definition is a cornerstone of privacy regulations, particularly the EU General Data Protection Regulation (GDPR), and extends far beyond obvious identifiers.
An individual is considered identifiable if they can be recognized, either directly or indirectly, through various identifiers or factors. These identifiers can include:
Direct identifiers. These are information that can directly point out and identify an individual, such as address, social security number, bank accounts, or an email address.
Indirect or quasi identifiers. These are data that can, when combined with other pieces of data, lead to the identification of an individual. Examples include date of birth, age, gender, geographic location (like a ZIP or postal code), marital status, or details about events (e.g., admission dates, procedure codes).
The assessment of whether a person is identifiable takes into account all means reasonably likely to be used by the data controller or another person to identify the natural person, including methods such as “singling out”. This “reasonably likely” criterion considers objective factors such as costs, time, effort, and the technological means available at the time of processing, as well as potential future technological developments. For instance, a dynamic IP address can qualify as personal data if it can be linked to a specific person, even if that linking capability resides with an Internet Service Provider and requires a court order Breyer v Bundesrepublik Deutschland (2016)].
In a research context, this means that even datasets without names or email addresses can contain personal data. A dataset with age, gender, postal code, and occupation might seem harmless - but if someone knows that a particular person participated in your study, they might be able to single them out using just these variables. This is especially relevant for smaller or more specialized samples (e.g., employees of a specific company, students in a specific program) where combinations of demographic variables become more unique. It is also relevant when your data contains grouped observations - for example, couples, families, or school classes - since group members share certain attributes and may use their knowledge about other members to identify them in the dataset.
Special Types of Personal Data
The GDPR (Art. 9) describes several categories of sensitive data that receive heightened protection:
- racial or ethnic origin,
- political opinions,
- religious or philosophical beliefs,
- trade union membership,
- genetic data, biometric data (for unique identification),
- health data,
- and data concerning a natural person’s sex life or sexual orientation.
Otherwise sensitive data include:
- information about criminal convictions and offenses
- financial data
In practice, this means you need to be extra careful with these variables when sharing data - they require stronger anonymization measures, and in some cases it may be advisable to remove them from a shared dataset entirely if they are not essential to the research question.
Exercise: Personal Data
Here is a simulated dataset of 200 Germans. The data’s purpose is to answer whether certain political opinions are linked to religion. This dataset will be used throughout the exercises.
Download the dataset here:
Copy this to a new Markdown file in RStudio to import the data for further analysis:
# Load data based on downloaded file
data <- read.csv("../SimulatedData.csv") # Change based on the location of your data file
# data <- data %>%
# select(-"X")
# data <- data %>%
# mutate(income = case_when(
# id == 198 ~ 480928.61,
# id == 55 ~ 902234.42,
# id == 3 ~ 724891.17,
# TRUE ~ income # keep everything else unchanged
# ))
#
# write.csv(data, "../SimulatedData.csv", row.names = FALSE)
data id name email plz
1 1 Fatma Bonbach fatma.bonbach@t-online.de 80331
2 2 Karolina Ehlert karolina68@web.de 96176
3 3 Willy Scheel willy.scheel@vodafone.de 80636
4 4 Lia Röhricht lia.röhricht@web.de 85777
5 5 Adem Aumann adem.aumann@web.de 44869
6 6 Hans-Detlef Junken hans-detlef.junken@gmail.com 80539
7 7 Hans-Bernd Hande hans-bernd.hande@hotmail.com 91731
8 8 Louise Ortmann louise16@icloud.com 72224
9 9 Hans Peter Tlustek hans peter98@bluewin.ch 72348
10 10 Nuri Gute nuri.gute@yahoo.com 80331
11 11 Aleksander Ebert aleksander.ebert@yahoo.com 25845
12 12 Carmela Fechner carmela.fechner@vodafone.de 32832
13 13 Bernt Käster bernt.käster@gmx.de 90441
14 14 Helena Kostolzin helena78@hotmail.com 80331
15 15 Theodora Jopich theodora99@t-online.de 97944
16 16 Britta Pruschke britta.pruschke@bluewin.ch 94113
17 17 Samuel Ritter samuel28@hotmail.com 65623
18 18 Selma Klotz selma35@vodafone.de 39120
19 19 Ben Fechner ben.fechner@t-online.de 80636
20 20 Marlies Täsche marlies22@icloud.com 66589
21 21 Monique Kostolzin monique.kostolzin@vodafone.de 86492
22 22 Ottilie Scheel ottilie.scheel@vodafone.de 84524
23 23 Meral Bonbach meral.bonbach@yahoo.com 80539
24 24 Hedda Drub hedda.drub@outlook.com 82269
25 25 Ingmar Eberhardt ingmar28@hotmail.com 99830
26 26 Wenke Hertrampf wenke.hertrampf@gmail.com 22523
27 27 Hans-Christian Birnbaum hans-christian.birnbaum@gmx.de 80333
28 28 Ildiko Hahn ildiko.hahn@vodafone.de 80636
29 29 Kunigunde Mangold kunigunde.mangold@bluewin.ch 86759
30 30 Ida Bolzmann ida47@vodafone.de 50859
31 31 Christin Säuberlich christin.säuberlich@gmail.com 79875
32 32 Solveig Eimer solveig49@bluewin.ch 80539
33 33 Rochus Mosemann rochus11@web.de 97222
34 34 Zehra Röhricht zehra92@bluewin.ch 6847
35 35 Elma Killer elma47@outlook.com 50735
36 36 Dörte Gutknecht dörte87@gmx.de 88260
37 37 Gilda Schönland gilda.schönland@gmail.com 39444
38 38 Nermin Flantz nermin.flantz@bluewin.ch 80799
39 39 Gilda Bauer gilda.bauer@bluewin.ch 47249
40 40 Hans-Otto Heinrich hans-otto.heinrich@vodafone.de 94535
41 41 Hans-Jürgen Neuschäfer hans-jürgen.neuschäfer@gmx.de 72535
42 42 Priska Gehringer priska38@vodafone.de 69437
43 43 Leonid Werner leonid.werner@bluewin.ch 44145
44 44 Wolf Keudel wolf79@outlook.com 53773
45 45 Friedo Sontag friedo99@t-online.de 79793
46 46 Annemie Gotthard annemie70@gmx.de 80539
47 47 Robby Köster robby97@outlook.com 74388
48 48 Athanasios Hövel athanasios.hövel@outlook.com 74906
49 49 Danilo Henk danilo96@gmx.de 39649
50 50 Friedemann Pärtzelt friedemann85@web.de 64673
51 51 Ayten Kitzmann ayten.kitzmann@icloud.com 45479
52 52 Marlis Atzler marlis.atzler@gmx.de 97505
53 53 Hans-Detlef Bähr hans-detlef.bähr@bluewin.ch 87730
54 54 Kreszentia Kranz kreszentia46@web.de 13055
55 55 Andrzej Salz andrzej.salz@t-online.de 86567
56 56 Pia Wilmsen pia57@gmail.com 54538
57 57 Grzegorz Weihmann grzegorz.weihmann@vodafone.de 3253
58 58 Corinna Pohl corinna28@gmail.com 52224
59 59 Stanislav Jockel stanislav.jockel@bluewin.ch 36391
60 60 Mercedes Bolnbach mercedes.bolnbach@vodafone.de 80636
61 61 Dusan Scheibe dusan15@t-online.de 90613
62 62 Mehmet Birnbaum mehmet52@gmx.de 76137
63 63 Hans-H. Nohlmans hans-h.26@gmail.com 31096
64 64 Vera Weitzel vera.weitzel@yahoo.com 66509
65 65 Kristian Hörle kristian15@outlook.com 72660
66 66 Heinz-Willi Meister heinz-willi17@gmx.de 80331
67 67 Agnes Rädel agnes.rädel@bluewin.ch 86688
68 68 Claus-Peter Heser claus-peter39@gmx.de 76448
69 69 Margaret Etzler margaret10@gmx.de 7985
70 70 Hans-Friedrich Gotthard hans-friedrich61@yahoo.com 29365
71 71 Katarzyna Zorbach katarzyna70@icloud.com 72149
72 72 Liesel Mende liesel.mende@web.de 64546
73 73 Jacek Binner jacek32@web.de 35764
74 74 Reinald Schottin reinald14@icloud.com 79771
75 75 Mariola Schweitzer mariola73@t-online.de 7806
76 76 Gerhild Trommler gerhild39@icloud.com 26131
77 77 H.-Dieter Stiffel h.-dieter72@yahoo.com 80799
78 78 Christof Grein Groth christof.grein groth@icloud.com 33758
79 79 Mareile Bolander mareile.bolander@hotmail.com 65931
80 80 Gudrun Schönland gudrun.schönland@hotmail.com 54306
81 81 Dajana Dowerg dajana17@outlook.com 80636
82 82 Marieluise Oderwald marieluise87@gmail.com 76530
83 83 Emanuel Kranz emanuel79@icloud.com 84140
84 84 Swantje Meister swantje13@icloud.com 80636
85 85 Leonhard Müller leonhard99@yahoo.com 24863
86 86 Thoralf Kroker thoralf.kroker@hotmail.com 80636
87 87 Rebecca Hesse rebecca16@bluewin.ch 80331
88 88 Lissy Wulff lissy.wulff@bluewin.ch 80539
89 89 Diether Patberg diether.patberg@outlook.com 28755
90 90 Freya Speer freya56@gmx.de 94366
91 91 Etta Wirth etta.wirth@vodafone.de 46395
92 92 Sylvie Meister sylvie86@hotmail.com 80539
93 93 Ilka Mies ilka98@vodafone.de 96277
94 94 Petros Döhn petros.döhn@icloud.com 80636
95 95 Alla Heidrich alla.heidrich@outlook.com 99706
96 96 Margarita Schönland margarita.schönland@hotmail.com 13059
97 97 Reginald Hänel reginald29@t-online.de 79585
98 98 Bianca Hövel bianca.hövel@outlook.com 80331
99 99 Holger Rohleder holger.rohleder@outlook.com 80539
100 100 Piotr Hofmann piotr68@vodafone.de 88319
101 101 Ewald Jähn ewald.jähn@gmail.com 6217
102 102 Knud Römer knud.römer@gmail.com 80636
103 103 Bastian Keudel bastian.keudel@icloud.com 21789
104 104 Sepp Weitzel sepp72@outlook.com 45657
105 105 Edgar Schleich edgar25@gmx.de 1445
106 106 Cläre Nohlmans cläre.nohlmans@yahoo.com 92339
107 107 Alla Geisel alla56@hotmail.com 92289
108 108 Miroslav Steinberg miroslav10@bluewin.ch 1587
109 109 Hanne-Lore Scheibe hanne-lore.scheibe@web.de 80799
110 110 Hubertine Drubin hubertine.drubin@gmx.de 91097
111 111 Hansjoachim Tschentscher hansjoachim75@gmail.com 67482
112 112 Lene Hertrampf lene10@hotmail.com 23863
113 113 Hans-Christian Bohnbach hans-christian.bohnbach@gmail.com 80333
114 114 Hans-Gerhard Freudenberger hans-gerhard.freudenberger@web.de 93093
115 115 Irmhild Meyer irmhild.meyer@vodafone.de 24784
116 116 Susi Putz susi.putz@yahoo.com 80539
117 117 Korbinian Hövel korbinian.hövel@t-online.de 26121
118 118 Mercedes Ebert mercedes63@t-online.de 86690
119 119 Nikolaj Schottin nikolaj.schottin@gmx.de 80799
120 120 Trudel Holt trudel.holt@icloud.com 56340
121 121 Luis Trapp luis.trapp@bluewin.ch 80539
122 122 Celal Löffler celal27@outlook.com 80636
123 123 Luisa Jacobi Jäckel luisa.jacobi jäckel@hotmail.com 77756
124 124 Cosimo Wende cosimo.wende@icloud.com 53225
125 125 Flora Klingelhöfer flora.klingelhöfer@t-online.de 80539
126 126 Bernward Barth bernward16@t-online.de 80333
127 127 Leonhard Höfig leonhard47@vodafone.de 80799
128 128 Sandra Adolph sandra.adolph@web.de 80799
129 129 Ernestine Mielcarek ernestine.mielcarek@gmail.com 39599
130 130 Till Speer till.speer@hotmail.com 76229
131 131 Erdmute Conradi erdmute.conradi@web.de 4416
132 132 Torsten Meister torsten43@hotmail.com 8141
133 133 Franz-Peter Klemt franz-peter.klemt@icloud.com 4821
134 134 Alois Gierschner alois16@gmx.de 86153
135 135 Cathleen Kraushaar cathleen.kraushaar@web.de 76698
136 136 Friedo Karge friedo.karge@hotmail.com 86701
137 137 Goran Mangold goran54@outlook.com 58708
138 138 Ellen Ullrich ellen29@t-online.de 54455
139 139 Ronny Rust ronny99@yahoo.com 35619
140 140 Solveig Wulf solveig.wulf@yahoo.com 64385
141 141 Stjepan Gröttner stjepan.gröttner@t-online.de 6132
142 142 Gertraut Bohnbach gertraut24@outlook.com 82110
143 143 Janett Weller janett22@web.de 73734
144 144 Kathleen Eckbauer kathleen38@yahoo.com 31249
145 145 Magrit Jessel magrit42@icloud.com 94538
146 146 Nikolaus Schulz nikolaus64@yahoo.com 84130
147 147 Hans-Friedrich Lachmann hans-friedrich59@bluewin.ch 57648
148 148 Friedrich-Karl Trüb friedrich-karl88@hotmail.com 37449
149 149 Jeannette Jähn jeannette15@gmail.com 80799
150 150 Halil Rohleder halil94@web.de 80333
151 151 Wera Mühle wera92@hotmail.com 31637
152 152 Jaroslaw Gumprich jaroslaw84@vodafone.de 3253
153 153 Gesine Karge gesine85@vodafone.de 80636
154 154 Dörthe Sölzer dörthe.sölzer@gmail.com 80799
155 155 Clara Kuhl clara.kuhl@hotmail.com 27333
156 156 Robin Martin robin.martin@gmx.de 91284
157 157 Sonja Pruschke sonja.pruschke@web.de 80331
158 158 Roy Kuhl roy50@yahoo.com 80799
159 159 Liesbeth Holsten liesbeth61@icloud.com 9380
160 160 Olaf Zobel olaf.zobel@t-online.de 80636
161 161 Xenia Rogge xenia.rogge@web.de 79256
162 162 Wibke Pohl wibke.pohl@gmail.com 86685
163 163 Raissa Tröst raissa.tröst@gmail.com 67468
164 164 Ellen Wagner ellen.wagner@t-online.de 47226
165 165 Eckart Loos eckart.loos@yahoo.com 80539
166 166 Edith Blümel edith.blümel@gmail.com 53545
167 167 Miroslawa Täsche miroslawa10@gmail.com 80799
168 168 Franz-Josef Hellwig franz-josef.hellwig@vodafone.de 54413
169 169 Isabel Kade isabel29@yahoo.com 80333
170 170 Carin Klingelhöfer carin54@outlook.com 94522
171 171 Liesel Kraushaar liesel15@hotmail.com 19209
172 172 Eugen Kramer eugen13@hotmail.com 37079
173 173 Sigmund Peukert sigmund53@outlook.com 94239
174 174 Leni Mitschke leni56@vodafone.de 80333
175 175 Leopold Albers leopold.albers@outlook.com 14822
176 176 Nevenka Biggen nevenka48@gmail.com 49429
177 177 Ekrem Tschentscher ekrem64@gmail.com 9575
178 178 Franka Budig franka65@bluewin.ch 25879
179 179 Reza Schüler reza.schüler@hotmail.com 2763
180 180 Arif Gutknecht arif.gutknecht@vodafone.de 33719
181 181 Mina Zorbach mina94@yahoo.com 80636
182 182 Marcel Patberg marcel.patberg@icloud.com 36043
183 183 Eva-Marie Dörr eva-marie33@gmx.de 24161
184 184 Olaf Lachmann olaf.lachmann@bluewin.ch 80331
185 185 Raik Fechner raik45@outlook.com 32457
186 186 Roswita Naser roswita.naser@hotmail.com 56424
187 187 Franz-Peter Köhler franz-peter72@vodafone.de 18225
188 188 Anke Trüb anke.trüb@bluewin.ch 76770
189 189 Burkhardt Hethur burkhardt.hethur@gmail.com 29525
190 190 Sibille Finke sibille.finke@hotmail.com 39122
191 191 Francoise Etzold francoise63@web.de 80539
192 192 Aloys Beer aloys30@gmail.com 8324
193 193 Alice Bonbach alice66@hotmail.com 80333
194 194 René Weimer rené63@outlook.com 80539
195 195 Hans-Hinrich Junken hans-hinrich90@yahoo.com 77815
196 196 Falk Staude falk.staude@hotmail.com 72505
197 197 Valentine Margraf valentine67@vodafone.de 80331
198 198 Martine Gieß martine.gieß@hotmail.com 54290
199 199 Pamela Etzler pamela47@gmx.de 63920
200 200 Ewa Aumann ewa31@hotmail.com 97922
gender age income years_in_job religion
1 female 20 8471.35 3 Catholicism
2 female 32 34721.35 4 None
3 male 69 724891.17 0 None
4 female 63 59490.47 5 None
5 male 18 52337.39 1 Islam
6 male 61 60960.23 6 Protestantism
7 male 39 61037.20 11 None
8 female 37 58720.93 2 Buddhism
9 male 36 55625.01 1 Catholicism
10 male 21 75996.23 4 Catholicism
11 non-binary 41 29760.83 6 Catholicism
12 female 38 43369.95 9 None
13 male 33 70224.28 3 None
14 female 43 43707.07 2 Islam
15 female 70 50073.94 5 Islam
16 female 33 53897.71 9 Catholicism
17 male 51 47372.98 8 Catholicism
18 female 69 53557.83 8 Judaism
19 male 33 88933.73 4 Islam
20 female 56 33516.81 2 None
21 female 50 60266.05 3 None
22 female 28 3234.65 11 None
23 female 45 18647.76 0 Protestantism
24 female 48 53198.80 0 Catholicism
25 male 58 66750.89 8 Protestantism
26 female 43 33275.84 2 Catholicism
27 male 41 43974.11 0 Catholicism
28 female 30 56904.06 2 None
29 female 24 49785.63 2 Islam
30 female 27 65838.27 4 None
31 female 46 43510.82 2 Catholicism
32 female 28 35050.43 11 Protestantism
33 male 23 45612.60 6 None
34 female 24 74064.45 2 Islam
35 female 18 19700.02 1 None
36 female 18 35491.71 1 Protestantism
37 female 29 31146.67 3 Protestantism
38 female 18 58538.33 1 None
39 non-binary 32 25565.51 10 None
40 male 55 53781.80 12 None
41 male 33 48621.36 6 None
42 female 57 52099.26 1 None
43 male 22 80544.39 5 None
44 male 49 44352.87 0 None
45 male 22 36923.45 5 Catholicism
46 female 66 108934.56 8 Catholicism
47 male 59 82695.22 6 None
48 male 67 101342.65 16 Protestantism
49 non-binary 27 43699.08 3 Protestantism
50 male 25 55256.89 8 Catholicism
51 female 19 57280.41 2 Protestantism
52 female 48 65257.71 17 Catholicism
53 male 30 73130.44 5 Protestantism
54 female 58 77231.91 5 Islam
55 non-binary 41 902234.42 4 None
56 female 50 49240.52 6 None
57 male 18 49181.85 1 Protestantism
58 female 46 45667.06 11 Catholicism
59 male 27 71020.92 3 Catholicism
60 female 53 17867.79 0 None
61 male 39 59154.65 7 None
62 male 18 34867.09 1 Protestantism
63 male 61 103880.23 9 Protestantism
64 female 46 6537.27 4 Islam
65 non-binary 28 53963.61 5 Protestantism
66 male 38 53233.39 0 None
67 female 66 35192.39 0 Catholicism
68 male 46 88998.52 7 Protestantism
69 female 32 76185.54 15 None
70 male 18 15277.69 1 Catholicism
71 female 53 32358.41 4 Protestantism
72 female 59 59564.29 0 Catholicism
73 male 18 44946.10 1 None
74 male 59 66701.73 10 Judaism
75 female 37 52378.45 13 Protestantism
76 female 35 28532.02 1 Protestantism
77 male 42 56000.53 5 Protestantism
78 male 28 69690.09 11 None
79 female 51 46431.85 2 Protestantism
80 female 65 85894.23 4 None
81 female 45 59395.77 3 Protestantism
82 female 24 50677.57 0 Protestantism
83 male 61 19523.64 3 Islam
84 female 34 60402.28 5 Protestantism
85 male 41 73762.07 24 None
86 male 55 57488.83 2 Catholicism
87 female 34 48387.34 4 Catholicism
88 female 49 51020.65 32 Catholicism
89 non-binary 35 106715.14 8 None
90 female 40 52973.81 5 Catholicism
91 female 25 65059.84 5 Protestantism
92 female 46 92745.09 5 Catholicism
93 female 52 60871.39 10 Catholicism
94 male 18 38100.28 1 None
95 female 70 1508.25 1 None
96 female 45 35839.71 2 Catholicism
97 male 61 33524.46 3 None
98 female 48 105629.59 10 Protestantism
99 male 43 37535.39 18 None
100 male 41 70163.08 3 Catholicism
101 male 24 44974.20 2 Buddhism
102 male 35 65006.97 5 Catholicism
103 male 48 28030.40 13 Protestantism
104 male 40 56143.31 1 Protestantism
105 male 29 21741.10 12 Catholicism
106 female 18 61855.98 1 None
107 female 18 15326.24 1 None
108 male 61 9709.52 5 None
109 female 44 25659.34 5 Catholicism
110 female 55 69044.93 3 Catholicism
111 male 35 66301.02 3 Protestantism
112 female 33 27090.40 2 None
113 male 48 45324.62 4 None
114 male 60 50849.11 5 Catholicism
115 female 70 30421.29 13 None
116 female 47 50230.45 3 Protestantism
117 male 25 37902.42 6 None
118 female 18 55949.20 1 Catholicism
119 male 41 53509.42 0 None
120 female 70 57558.17 2 Catholicism
121 male 37 67904.73 3 Protestantism
122 male 18 37003.44 1 None
123 female 41 74012.32 0 None
124 male 47 54693.74 0 None
125 female 62 36977.03 12 None
126 male 37 36546.40 2 None
127 male 33 84045.58 10 Catholicism
128 female 25 82138.06 1 None
129 female 50 30733.31 1 None
130 male 43 61673.59 3 Protestantism
131 female 50 69536.55 1 Protestantism
132 male 38 48347.66 6 Protestantism
133 male 36 66190.75 0 Protestantism
134 male 63 66078.75 11 None
135 female 56 18269.69 1 None
136 non-binary 41 59707.29 1 Protestantism
137 male 40 52099.71 17 None
138 female 26 34656.89 4 Protestantism
139 male 45 34257.44 10 Catholicism
140 female 31 24074.20 14 None
141 male 48 64152.22 1 Catholicism
142 female 47 83251.70 2 Protestantism
143 female 42 64440.78 13 Protestantism
144 female 26 47105.72 9 None
145 female 44 36879.16 3 Catholicism
146 male 38 42220.17 15 Protestantism
147 male 39 63960.04 0 None
148 male 55 3530.08 9 None
149 female 36 13668.60 4 Islam
150 male 47 50377.45 7 Protestantism
151 female 45 61128.01 3 Catholicism
152 male 23 43719.31 4 None
153 female 41 54589.13 10 None
154 female 57 19115.90 10 None
155 female 42 10024.02 2 Catholicism
156 male 28 85761.37 7 Catholicism
157 non-binary 68 82607.20 3 Catholicism
158 male 56 44383.80 1 None
159 female 34 66185.55 15 Catholicism
160 male 56 46443.35 11 Protestantism
161 female 52 41571.20 9 Protestantism
162 female 45 48002.36 1 Catholicism
163 female 47 64303.37 0 Protestantism
164 female 57 63828.73 10 Catholicism
165 male 26 62158.38 1 Islam
166 female 35 36173.97 6 None
167 female 42 22233.87 0 Islam
168 male 24 65795.96 2 Catholicism
169 female 18 50500.79 1 None
170 female 44 34635.22 6 None
171 female 61 51093.90 11 None
172 male 30 38140.07 13 None
173 non-binary 48 83316.86 2 Catholicism
174 female 45 96422.37 10 None
175 male 46 47266.68 13 Protestantism
176 female 22 38736.53 5 Catholicism
177 male 36 51254.30 9 Catholicism
178 female 49 3070.65 2 Islam
179 male 57 67298.52 7 None
180 male 41 52914.99 6 Protestantism
181 female 60 53761.55 3 None
182 male 45 62667.11 10 Catholicism
183 female 18 69837.87 1 Protestantism
184 male 32 48027.70 9 None
185 male 54 68760.98 6 Catholicism
186 female 23 53996.54 6 None
187 non-binary 70 29089.31 2 Protestantism
188 female 19 58074.39 0 Protestantism
189 male 38 43647.51 5 Buddhism
190 female 44 59911.66 17 Catholicism
191 female 44 15015.81 4 None
192 male 30 48884.82 3 Catholicism
193 female 35 11665.95 5 Protestantism
194 male 44 51434.15 6 Eastern Orthodoxy
195 male 38 73230.95 10 Catholicism
196 male 50 52969.14 5 None
197 female 31 38514.53 11 None
198 female 40 480928.61 0 Protestantism
199 female 34 58564.02 4 Catholicism
200 female 43 30312.73 3 Catholicism
job_title education
1 Local government officer trade school
2 Structural engineer high school
3 Psychotherapist, dance movement high school
4 Fitness centre manager high school
5 Programme researcher, broadcasting/film/video high school
6 Chief Strategy Officer high school
7 Engineer, communications high school
8 Secretary/administrator university
9 Video editor trade school
10 Hotel manager high school
11 Herbalist university
12 Teacher, primary school high school
13 Production assistant, television high school
14 Surveyor, mining high school
15 Data scientist high school
16 Programmer, applications high school
17 Horticulturist, commercial trade school
18 Training and development officer high school
19 Catering manager high school
20 Textile designer no degree
21 Designer, fashion/clothing high school
22 Medical physicist high school
23 Seismic interpreter trade school
24 Biomedical engineer high school
25 Biomedical engineer no degree
26 Technical brewer university
27 Advertising art director high school
28 Clothing/textile technologist high school
29 Stage manager university
30 Advertising art director high school
31 IT consultant university
32 Secondary school teacher trade school
33 Restaurant manager university
34 Politician's assistant trade school
35 Illustrator high school
36 Community pharmacist high school
37 Solicitor doctoral title
38 Local government officer high school
39 Osteopath high school
40 Medical illustrator high school
41 Surgeon trade school
42 Financial controller high school
43 Systems analyst high school
44 Sports therapist high school
45 Sound technician, broadcasting/film/video doctoral title
46 Amenity horticulturist high school
47 Firefighter university
48 Copy high school
49 Sales promotion account executive trade school
50 Public relations officer trade school
51 Financial planner high school
52 Museum/gallery curator high school
53 Dealer high school
54 Higher education careers adviser high school
55 General practice doctor university
56 Recruitment consultant university
57 Training and development officer high school
58 Mudlogger high school
59 Pharmacist, hospital high school
60 Horticultural consultant high school
61 Ergonomist high school
62 Chartered accountant no degree
63 Psychiatrist university
64 Data processing manager high school
65 Broadcast journalist trade school
66 Administrator, charities/voluntary organisations high school
67 Speech and language therapist high school
68 Energy manager high school
69 Editor, commissioning high school
70 Advertising account executive high school
71 Surveyor, land/geomatics high school
72 Exercise physiologist high school
73 Risk manager trade school
74 Risk manager high school
75 Games developer high school
76 Illustrator university
77 Speech and language therapist university
78 Gaffer high school
79 Heritage manager high school
80 Buyer, industrial high school
81 Psychiatrist university
82 Public house manager university
83 Production assistant, television trade school
84 Wellsite geologist high school
85 Dealer high school
86 Manufacturing engineer high school
87 Engineer, electrical university
88 Brewing technologist university
89 Social researcher trade school
90 Advertising account planner high school
91 Community pharmacist high school
92 Conservator, furniture no degree
93 Designer, blown glass/stained glass university
94 Scientist, product/process development high school
95 Dealer trade school
96 Insurance underwriter high school
97 Curator high school
98 Armed forces logistics/support/administrative officer university
99 Chief Executive Officer university
100 Special effects artist trade school
101 Quarry manager doctoral title
102 Therapist, sports high school
103 Chartered management accountant no degree
104 Graphic designer trade school
105 Professor Emeritus high school
106 Runner, broadcasting/film/video high school
107 Forensic psychologist trade school
108 Engineer, electrical trade school
109 Designer, furniture university
110 Engineer, manufacturing systems high school
111 Patent attorney high school
112 Research officer, trade union university
113 Museum/gallery conservator high school
114 Furniture conservator/restorer high school
115 Television floor manager university
116 Print production planner high school
117 Financial trader high school
118 Estate agent high school
119 Trading standards officer trade school
120 Housing manager/officer university
121 Community pharmacist high school
122 Sound technician, broadcasting/film/video high school
123 Catering manager high school
124 Nutritional therapist doctoral title
125 Surveyor, mining high school
126 Designer, industrial/product trade school
127 Geographical information systems officer high school
128 Furniture designer university
129 Multimedia programmer high school
130 Pharmacist, community doctoral title
131 Curator doctoral title
132 Event organiser trade school
133 Energy engineer no degree
134 Armed forces operational officer no degree
135 Geophysicist/field seismologist high school
136 Sales promotion account executive high school
137 Conservation officer, historic buildings high school
138 Engineer, electrical trade school
139 Statistician doctoral title
140 Sport and exercise psychologist doctoral title
141 Pharmacist, community high school
142 Water engineer high school
143 Data scientist university
144 Commercial horticulturist high school
145 Horticulturist, commercial no degree
146 Homeopath high school
147 Minerals surveyor trade school
148 Cartographer trade school
149 Programmer, multimedia trade school
150 IT trainer trade school
151 Commercial/residential surveyor high school
152 Water engineer high school
153 Insurance broker university
154 Museum/gallery exhibitions officer trade school
155 Ceramics designer high school
156 Camera operator high school
157 Paramedic university
158 Fitness centre manager trade school
159 Immunologist high school
160 Chief Executive Officer high school
161 Purchasing manager trade school
162 Pharmacist, hospital university
163 Physiotherapist high school
164 Market researcher high school
165 Marketing executive high school
166 Horticulturist, amenity university
167 Jewellery designer trade school
168 Make high school
169 Child psychotherapist university
170 Interior and spatial designer high school
171 Environmental health practitioner high school
172 Health and safety inspector high school
173 Broadcast journalist trade school
174 Health visitor high school
175 Dancer high school
176 Lexicographer high school
177 Psychiatric nurse high school
178 Newspaper journalist university
179 Research scientist (maths) high school
180 Restaurant manager, fast food trade school
181 Software engineer trade school
182 Engineer, electrical high school
183 Advertising account planner high school
184 Ranger/warden no degree
185 Scientist, clinical (histocompatibility and immunogenetics) no degree
186 Health physicist university
187 Special effects artist university
188 Hospital pharmacist university
189 Medical sales representative doctoral title
190 Technical sales engineer university
191 Engineer, manufacturing high school
192 Surveyor, commercial/residential trade school
193 Scientist, water quality trade school
194 Museum/gallery conservator high school
195 Psychologist, forensic high school
196 Optician, dispensing high school
197 Translator university
198 Secretary, company trade school
199 Economist university
200 Marketing executive high school
pol_immigration pol_environment pol_redistribution pol_eu_integration
1 5 4 5 2
2 3 5 3 4
3 2 1 2 5
4 2 3 5 1
5 2 4 3 3
6 2 5 5 4
7 2 3 3 2
8 5 5 4 5
9 1 3 1 3
10 1 3 3 5
11 4 4 2 2
12 5 4 2 3
13 3 2 3 2
14 3 2 5 3
15 5 1 2 3
16 4 5 5 5
17 2 3 4 3
18 1 2 1 1
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20 3 2 3 1
21 5 3 5 1
22 4 2 4 4
23 1 2 1 3
24 5 5 4 1
25 5 5 3 4
26 2 2 2 3
27 4 2 2 4
28 5 1 1 1
29 2 2 3 2
30 3 3 5 3
31 3 2 4 1
32 1 1 5 2
33 5 3 5 1
34 3 2 3 4
35 4 1 1 5
36 3 3 3 4
37 1 4 4 5
38 2 1 5 5
39 1 2 1 3
40 4 1 5 5
41 4 2 1 3
42 3 3 3 4
43 5 2 4 2
44 2 3 4 5
45 2 2 3 5
46 4 5 4 3
47 2 1 3 1
48 3 4 4 2
49 5 4 2 3
50 4 5 5 2
51 5 3 1 1
52 4 1 4 1
53 3 3 2 3
54 3 5 2 5
55 5 2 5 2
56 5 3 5 2
57 3 1 1 4
58 1 5 4 4
59 1 4 1 4
60 5 2 5 3
61 4 2 3 1
62 1 5 4 4
63 4 2 3 2
64 5 5 5 2
65 5 1 3 5
66 2 2 1 5
67 1 3 3 5
68 2 3 3 3
69 3 5 5 5
70 4 4 2 4
71 1 3 5 4
72 2 5 1 3
73 1 3 1 5
74 2 5 5 3
75 5 4 5 2
76 5 1 5 2
77 5 3 5 2
78 2 1 1 4
79 5 3 2 3
80 3 3 4 5
81 3 4 1 2
82 5 3 3 5
83 2 3 5 4
84 3 3 5 1
85 4 5 1 3
86 1 3 5 4
87 2 3 3 1
88 2 1 2 3
89 4 1 2 1
90 1 3 3 5
91 4 4 4 3
92 3 3 2 5
93 4 1 4 3
94 3 3 3 2
95 1 5 2 2
96 2 3 1 5
97 4 5 5 2
98 4 5 5 4
99 3 2 2 2
100 2 5 2 2
101 5 4 2 5
102 2 5 4 2
103 2 3 2 3
104 5 4 3 1
105 2 3 4 1
106 1 4 3 1
107 3 5 4 3
108 5 2 2 1
109 3 4 2 1
110 2 1 2 5
111 2 5 2 1
112 1 3 2 4
113 4 5 4 4
114 3 3 1 5
115 3 5 5 4
116 2 3 3 4
117 4 4 4 1
118 1 4 4 4
119 1 4 3 5
120 1 5 1 1
121 2 1 5 2
122 2 5 2 2
123 1 1 5 5
124 1 1 4 2
125 3 5 4 3
126 5 3 1 5
127 4 3 3 3
128 2 4 5 4
129 1 2 2 3
130 2 5 4 3
131 4 3 1 1
132 3 5 5 1
133 2 3 3 2
134 2 5 3 4
135 3 4 2 1
136 5 1 2 5
137 3 2 4 1
138 1 4 5 4
139 5 5 1 1
140 4 5 2 3
141 3 1 1 5
142 5 2 1 3
143 4 1 5 1
144 5 4 4 5
145 4 1 2 1
146 4 4 4 1
147 2 4 3 2
148 1 1 2 2
149 3 2 4 4
150 4 4 3 2
151 1 3 4 3
152 1 2 4 2
153 2 4 2 5
154 3 5 5 2
155 4 4 1 4
156 1 3 1 3
157 4 2 2 3
158 5 2 3 4
159 1 2 4 2
160 1 2 3 3
161 1 5 3 1
162 5 1 1 1
163 3 5 1 5
164 2 3 2 1
165 4 4 3 1
166 2 5 3 5
167 1 2 1 2
168 2 1 2 3
169 1 3 5 5
170 1 1 4 3
171 2 4 4 5
172 1 2 5 1
173 2 1 1 5
174 2 5 4 5
175 5 2 1 3
176 2 1 4 2
177 5 4 3 2
178 1 4 2 5
179 3 4 2 5
180 5 2 1 2
181 3 4 2 1
182 1 3 5 5
183 1 3 4 4
184 3 3 1 2
185 1 3 3 2
186 5 4 3 5
187 1 3 4 1
188 1 5 1 4
189 2 1 4 5
190 5 3 4 4
191 4 1 1 3
192 3 5 4 4
193 1 1 5 1
194 4 2 3 3
195 4 1 1 2
196 5 5 4 5
197 5 3 3 4
198 2 5 1 5
199 4 3 3 5
200 5 5 5 5
ip_address
1 217.0.37.127
2 79.192.222.137
3 87.128.247.149
4 2.160.215.205
5 2.160.201.162
6 84.128.70.52
7 217.0.222.244
8 78.48.146.54
9 84.128.209.105
10 84.128.55.38
11 78.48.61.11
12 31.16.199.152
13 46.5.145.20
14 217.0.121.156
15 84.128.154.89
16 91.0.180.9
17 93.192.226.242
18 46.5.82.51
19 2.160.94.146
20 78.48.193.103
21 79.192.149.44
22 31.16.221.216
23 78.48.114.173
24 217.0.211.226
25 217.0.57.214
26 79.192.204.137
27 79.192.70.35
28 79.192.127.61
29 78.48.67.198
30 93.192.252.31
31 46.5.48.186
32 79.192.228.226
33 93.192.212.59
34 46.5.67.193
35 46.5.107.202
36 84.128.71.87
37 79.192.100.126
38 78.48.170.46
39 31.16.14.6
40 46.5.13.70
41 79.192.39.206
42 79.192.47.192
43 78.48.45.255
44 2.160.144.60
45 78.48.50.176
46 79.192.17.226
47 84.128.197.59
48 2.160.44.59
49 46.5.133.10
50 2.160.154.149
51 93.192.232.43
52 78.48.172.48
53 93.192.76.82
54 87.128.173.156
55 2.160.10.124
56 84.128.157.65
57 217.0.247.144
58 31.16.106.76
59 84.128.180.251
60 87.128.218.196
61 2.160.55.147
62 91.0.95.35
63 78.48.254.112
64 2.160.224.133
65 91.0.32.140
66 31.16.39.245
67 93.192.161.252
68 2.160.247.226
69 84.128.91.68
70 78.48.249.20
71 31.16.222.104
72 2.160.43.58
73 93.192.224.157
74 46.5.52.142
75 31.16.146.53
76 79.192.106.226
77 31.16.33.250
78 79.192.103.1
79 87.128.41.117
80 46.5.15.114
81 2.160.33.2
82 46.5.95.145
83 31.16.54.166
84 46.5.18.167
85 79.192.213.100
86 93.192.130.18
87 93.192.226.128
88 84.128.5.215
89 78.48.219.44
90 217.0.234.125
91 78.48.5.23
92 87.128.22.203
93 46.5.66.254
94 84.128.104.217
95 2.160.56.0
96 79.192.247.241
97 2.160.230.151
98 93.192.251.79
99 84.128.124.89
100 2.160.22.17
101 78.48.184.215
102 87.128.28.1
103 31.16.109.29
104 46.5.86.107
105 84.128.251.146
106 93.192.93.110
107 217.0.156.198
108 2.160.24.160
109 79.192.237.198
110 87.128.93.78
111 2.160.244.179
112 78.48.80.16
113 91.0.85.56
114 84.128.151.57
115 2.160.19.208
116 31.16.27.166
117 87.128.18.148
118 79.192.159.13
119 87.128.190.95
120 2.160.234.221
121 217.0.183.52
122 79.192.92.180
123 91.0.153.248
124 79.192.127.121
125 2.160.217.44
126 46.5.54.185
127 79.192.214.152
128 31.16.17.245
129 79.192.11.202
130 46.5.89.175
131 91.0.27.152
132 78.48.228.26
133 31.16.253.69
134 87.128.197.193
135 91.0.112.72
136 84.128.174.32
137 46.5.234.33
138 2.160.182.200
139 2.160.155.208
140 2.160.231.148
141 79.192.234.51
142 217.0.225.21
143 31.16.99.136
144 217.0.170.222
145 78.48.151.217
146 78.48.143.102
147 2.160.121.190
148 46.5.212.158
149 79.192.216.94
150 79.192.227.161
151 217.0.191.39
152 84.128.22.39
153 84.128.240.1
154 78.48.122.2
155 78.48.136.248
156 217.0.235.68
157 31.16.197.34
158 217.0.82.206
159 2.160.76.142
160 31.16.10.165
161 2.160.34.33
162 78.48.11.82
163 79.192.254.226
164 217.0.48.16
165 217.0.74.239
166 46.5.46.219
167 84.128.15.122
168 2.160.15.158
169 79.192.249.204
170 87.128.57.157
171 93.192.227.70
172 217.0.144.1
173 91.0.61.57
174 84.128.126.41
175 46.5.70.103
176 31.16.60.152
177 87.128.241.245
178 84.128.184.35
179 31.16.134.20
180 79.192.231.151
181 31.16.252.3
182 217.0.19.42
183 78.48.67.103
184 31.16.21.226
185 91.0.159.165
186 31.16.238.171
187 93.192.156.237
188 46.5.250.137
189 93.192.78.207
190 31.16.249.202
191 93.192.218.172
192 78.48.61.190
193 78.48.37.227
194 87.128.163.40
195 217.0.86.81
196 87.128.190.28
197 87.128.223.129
198 31.16.213.20
199 84.128.234.228
200 2.160.3.162
NoteData Dictionary
| Variable Name | Description | Item | Values |
|---|---|---|---|
| id | Number assigned to each participant in order of participation | (assigned in background) | Integer; 1-200 |
| name | First and last name of participant | Please indicate your full name (first and last name) | String of characters |
| Email address of participant | What is your e-mail address? | String of characters | |
| plz | German postal code | What is your postal code? | String of characters |
| gender | Gender of participant | What is your gender? | Factor; “male”/“female”/“non-binary” |
| age | Age of participant in years | What is your age in years? | Integer; 18-100 |
| income | Personal annual income in Euros | What was your income over the last twelve months | Integer |
| religion | Religion of participant | What is your religion? | Factor; “Catholicism”,“Protestantism”,“Islam”,“Eastern Orthodoxy”,“Judaism”, “Buddhism”, “Hinduism”, “Other”, “None” |
| job_title | Title of job of partcipant | What is your job title? | String of characters |
| education | Highest degree of education | What is your highest degree of education? | Factor; “no degree”,“trade school”,“high school”, “university”,“doctoral title” |
| pol_immigration | Likert item measuring opinion on immigration | The government should limit immigration more strictly than it currently does. | Integer; 1-5 |
| pol_environment | Likert item measuring opinion on environment | Protecting the environment should be a top priority, even if it slows economic growth. | Integer; 1-5 |
| pol_redistribution | Likert item measuring opinion on redistribution of wealth | The government should reduce income differences between rich and poor. | Integer; 1-5 |
| pol_eu_integration | Likert item measuring opinion on membership in EU | Our country benefits from being a member of the European Union. | Integer; 1-5 |
| ip_address | IP address (version 4) of participant’s device when answering survey | (collected in background) | String of characters |
| years_in_job | Number of years the participant has been in their current job | How many years have you been in your current job? | Integer; 0–n |
Exercise: Direct Identifiers, Indirect Identifiers, and Special Categories
Inspect the dataset data. Answer the following questions:
1. Which columns contain direct identifiers?
NoteSolution
Direct identifiers: name, email, ip_address
Name is probably the most obvious identifier there is.
Email addresses are considered direct identifiers since they often contain names and therefore, clearly specify certain individuals. However, even when not containing names, they can act as indirect identifiers by linking to an account or revealing a person’s organization.
IP addresses can be linked back to specific devices and, through Internet service providers, to individuals. The Court of Justice of the EU has ruled that even dynamic IP addresses can constitute personal data when the entity holding them has the legal means to obtain identifying information from the ISP (Breyer v Bundesrepublik Deutschland 2016). In a research context, they should be treated as direct identifiers.
2. Which contains indirect identifiers?
NoteSolution
Indirect identifiers: gender, age, education, job_title, income, plz, years_in_job
Gender, age, education, job title, postal code, income, and years in job are indirect identifiers, since they cannot lead to idenitification of an individual on their own but may be linked with each other or external knowledge. When an attacker knows of a person who participated in the study and knows their job title, they could identify the person in the data.
3. Which contain special categories of personal data?
NoteSolution
Special category data: all pol\_ variables (political opinion), religion
The pol_immigration, pol_environment, pol_redistribution, and pol_eu_integration variables capture political opinions, and religion captures religious beliefs. Both categories are explicitly listed as sensitive data under Art. 9 GDPR. These variables require heightened protection because their disclosure could lead to discrimination or other harm for the individuals involved.
4. Is there any column that does not contain personal data?
NoteSolution
Personal data: All columns contain personal data.
Any data that can be linked to an individual is considered personal data. As long as identification (e.g., via direct identifiers such as name) is possible, it is considered personal data and GDPR applies.
Exercise: Remove Direct Identifiers
Now, delete all direct identifiers from the dataset.
NoteSolution
Any solution that deletes the name, email address, and IP address is correct. I use tidyverse syntax:
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.6
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.1 ✔ tibble 3.3.0
✔ lubridate 1.9.4 ✔ tidyr 1.3.2
✔ purrr 1.2.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata_withoutdirectidentifiers <- data %>%
select(-name, -email, -ip_address)Save the new file
write.csv(data_withoutdirectidentifiers, "../SimulatedData_noidentifiers.csv", row.names = FALSE)Learning Objective
- After completing this part of the tutorial, you will be able to distinguish between personal data and non-personal data, as well as sensitive and non-sensitive data, and be able to identify direct and indirect identifiers.
Exercises
- Identify variables that contain direct identifiers, indirect identifiers, and sensitive data
Resources, Links, Examples
- examples for how to categorize data: Van Ravenzwaaij et al. (2025)
References
Reference for a Preliminary Ruling — Processing of Personal Data — Directive 95/46/EC — Article 2(a) — Article 7(f) — Definition of “Personal Data” — Internet Protocol Addresses — Storage of Data by an Online Media Services Provider — National Legislation Not Permitting the Legitimate Interest Pursued by the Controller to Be Taken into Account). 2016. C-582/14.
Van Ravenzwaaij, Don, Marlon De Jong, Rink Hoekstra, et al. 2025. “De-Identification When Making Data Sets Findable, Accessible, Interoperable, and Eusable (FAIR): Two Worked Examples from the Behavioral and Social Sciences.” Advances in Methods and Practices in Psychological Science 8 (2): 1–23. https://doi.org/10.1177/25152459251336130.