Define user group for the app

[StefZimm]

Which users should use the app?

Non-scientists with basic or little experience with statistical indicators.

• Modern historians (Common User)
• Journalists/data journalists (Common User)
• Scientists (Common User)
• Respondents who want to know how their information translates into results (motivational enhancement for respondents?)
• Possible new data users of the SOEP

Topics the App should cover:

• Demography and Population (Census data are more likely to answer such questions)
• Work and Employment
• Income, Taxes, and Social Security (strongest SOEP topic)
• Family and Social Networks
• Health and Care (strongest SOEP topic)
• Home, Amenities, and Contributions of Private HH
• Education and Qualification (Education is already grouping variable, concrete degrees are difficult to represent)
• Attitudes, Values, and Personality (strongest SOEP topic)
• Time Use and Environmental Behavior
• Integration, Migration, Transnationalization (Migration usually affects only a smaller sample group, Moreover, migration background is grouping variable for user)
• Survey Methodology (Survey methodology questions are not suitable for app)

Definition of grouping variables

Grouping variables are variables that can be used by the user to display grouped results.Up to three groupings should be possible (e.g., year; year and gender; year, gender, migration background) Grouping variables should have the following characteristics:

• Must be available from 1984 to 2019
• Shall contain only valid values
• Shall remain invariably constant over time
• Should contain as few categories as possible
• Must be categorical
• Demographic variables are preferentially used from trackig datasets (migback in ppathl, gebjahr in ppathl, bula_h in pbrutto).

variable	type	label_de	level
age_gr	categorical	Age Group	3
education	categorical	Education Level	5
bula_h	categorical	Federal States	15
sex	categorical	Gender	2
pgisced97	categorical	ISCED-1997-Classification	7
pgcasmin	categorical	CASMIN-Klassifikation	10
migback	categorical	Migration background	3
sampreg	categorical	Sample Region	2
e11102	categorical	Employment Status of Individual	2
e11103	categorical	Employment Level of Individual	3
hgtyp1hh	categorical	Household typology (1-digit)	6

variable	value	label
age_gr	1	16-34 y.
age_gr	2	35-65 y.
age_gr	3	66 and older
education	1	lower secondary degree
education	2	secondary school degree
education	4	college entrance qualification
education	5	Other degree
education	6	no degree/no degree yet
bula_h	1	Schleswig-Holstein
bula_h	2	Hamburg
bula_h	3	Lower Saxony
bula_h	4	Bremen
bula_h	5	North Rhine-Westphalia
bula_h	6	Hesse
bula_h	7	Rhineland-Palatinate,Saarland
bula_h	8	Baden-Württemberg
bula_h	9	Bavaria
bula_h	11	Berlin
bula_h	12	Brandenburg
bula_h	13	Mecklenburg-Western Pomerania
bula_h	14	Saxony
bula_h	15	Saxony-Anhalt
bula_h	16	Thuringia
sex	1	Male
sex	2	Female
migback	1	no migration background
migback	2	direct migration background
migback	3	indirect migration background
sampreg	1	West Germany
sampreg	2	East Germany
e11102	0	Not Employed
e11102	1	Employed
e11103	1	Full Time
e11103	2	Part Time
e11103	3	Not Working
hgtyp1hh	1	1-pers.-HH
hgtyp1hh	2	(Married) couple without C.
hgtyp1hh	3	Single parent
hgtyp1hh	4	Couple + C. LE 16
hgtyp1hh	5	Couple + C. GT 16
hgtyp1hh	6	Couple + C. LE and GT 16
pgisced97	0	in school
pgisced97	1	inadequately
pgisced97	2	general elemantary
pgisced97	3	middle vocational
pgisced97	4	vocational + Abi
pgisced97	5	higher vocational
pgisced97	6	higher education
pgcasmin	0	(0) in school
pgcasmin	1	(1a) inadequately completed
pgcasmin	2	(1b) general elementary school
pgcasmin	3	(1c) basic vocational qualification
pgcasmin	4	(2b) intermediate general qualification
pgcasmin	5	(2a) intermediate vocational
pgcasmin	6	(2c_gen) general maturity certificate
pgcasmin	7	(2c_voc) vocational maturity certificate
pgcasmin	8	(3a) lower tertiary education
pgcasmin	9	(3b) higher tertiary education

Redundant information education pgisced97 pgcasmin? Redundant information e11102 e11103? Redundant information sampreg bula_h?

Definition of variables for the analysis

These variables should be available in tables and graphs in the app. The analysis variables must have the following characteristics:

• Variables should be assignable to the defined topics
• Are ideally available for all years 1984-2019
• Are available over a constant time period (e.g., 2001-2019)
• Follow a clear questionnaire rhythm (e.g., 2010, 2012, 2014, 2016, 2018)
• Generated version of a variable in a generated dataset is preferred over the raw variable (e.g., pequiv>hl, pgen>pl)
• Variables should be categorical, numeric, or ordinal (no character variables)

[StefZimm]

According to the user group classification, the app should be compiled user-specifically

[StefZimm]

Deliverance will make a pdf out of the concept and deliver it on time in canvas

[StefZimm]

submitted by Deliverance