Method Report

Austrian Corona Panel Data: Method Report

Last updated: 31 July 2023

1 Introduction

The corona crisis has fundamentally changed everyday life in Austria as well as in many other countries. However, people are affected in very different ways. Against this background, the Austrian Corona Panel Project aims to provide an overview of various health, economic and social aspects of the Corona crisis. This report provides details on the methods and process of data collection of the Austrian Corona Panel Data gathered by the Austrian Corona Panel Project at the University of Vienna.

1.1 How to cite the data

The data from the Austrian Corona Panel Project should be cited as follows:

Scientific Use File (SUF edition)
Kittel, Bernhard; Kritzinger, Sylvia; Boomgaarden, Hajo; Prainsack, Barbara; Eberl, Jakob-Moritz; Kalleitner, Fabian; Lebernegg, Noëlle S.; Partheymüller, Julia; Plescia, Carolina; Schiestl, David W.; Schlogl, Lukas, 2020, "Austrian Corona Panel Project (SUF edition)", https://doi.org/10.11587/28KQNS, AUSSDA.

Open Access (OA edition)
Kittel, Bernhard; Kritzinger, Sylvia; Boomgaarden, Hajo; Prainsack, Barbara; Eberl, Jakob-Moritz; Kalleitner, Fabian; Lebernegg, Noëlle S.; Partheymüller, Julia; Plescia, Carolina; Schiestl, David W.; Schlogl, Lukas, 2020, "Austrian Corona Panel Project (OA edition)", https://doi.org/10.11587/P5YJ0O, AUSSDA.

Please also cite the data paper along with the data:

Kittel, Bernhard; Kritzinger, Sylvia; Boomgaarden, Hajo; Prainsack, Barbara; Eberl, Jakob-Moritz; Kalleitner, Fabian; Lebernegg, Noëlle S.; Partheymüller, Julia; Plescia, Carolina; Schiestl, David W.; Schlogl, Lukas, 2021, “The Austrian Corona Panel Project: Monitoring Individual and Societal Dynamics amidst the COVID-19 Crisis”, European Political Science 20: 318–344, https://doi.org/10.1057/s41304-020-00294-7.

2 Study Description

2.1 Title and version

Austrian Corona Panel Project (V20230728)

2.2 Principal Investigators

Bernhard Kittel, Department of Economic Sociology, University of Vienna
Sylvia Kritzinger, Department of Government, University of Vienna
Hajo Boomgaarden, Department of Communication, University of Vienna
Barbara Prainsack, Department of Political Science, University of Vienna

2.3 Funding / Acknowledgments

Data collection has been made possible by COVID-19 Rapid Response Grant EI-COV20-006 of the Wiener Wissenschafts- und Technologiefonds (WWTF), financial support by the rectorate of the University of Vienna, and funding by the FWF Austrian Science Fund (P33907). Further funding by the Austrian Social Survey (SSÖ), the Vienna Chamber of Labour (Arbeiterkammer Wien), the Federation of Austrian Industries (Industriellenvereinigung) and the Austrian Academy of Sciences (ÖAW) is gratefully acknowledged. In addition, three survey waves have been funded by the work and corona project which is financed by the digifonds of the Vienna Chamber for Labor.

2.4 Fieldwork agency

The fieldwork for the Austrian Corona Panel Project was conducted by “Marketagent.com online research GmbH” (Baden, Austria).

2.5 Topics / Keywords

Crisis perception: health, economy, crisis management;
Effects of the crisis: health, factual behaviour, day structure, psychological effects, emotional status, care, partnership and family;
Expectations of the crisis: social and individual norms, corporate feeling, societal mood;
Politics: political trust, democracy, relationship government - parliament, government performance, surveillance measures, privacy, party choice, left/right;
Work: job situation, change in job situation, income, state support;
Economy: perceptions of inflation, unemployment, purchase behaviour, personal and national economic situation (prospective and retrospective);
Communication: media use (traditional and social media), interpersonal relationships, fake news, perception of reporting of crisis;
Psychological predispositions: risk behaviour, life satisfaction;
Sociodemographic information: age, gender, education, region, occupation, religion, household structure, migration background.

3 Study Design

3.1 Sampling

The Austrian Corona Panel Project surveyed respondents with access to the internet (via computer or mobile devices such as smartphones or tablets). Respondents were sampled from a pre-existing online access panel provided by Marketagent, Austria. Respondents were selected (quota sample) based on the following key demographics: age, gender, region (Bundesland), municipality size, and educational level based on official statistics, with the quota sample being structured to closely mirror the Austrian resident population. In order to participate in the study, the respondents had to be residents in Austria and at least 14 years old. Please note that the usual caution must be applied in interpreting these data as specific segments of the population continue to be hard to reach by online surveys.

3.2 Fieldwork

The data collection started on 27 March 2020 and is currently ongoing:

Wave 1: 27 March 2020 – 30 March 2020
Wave 2: 3 April 2020 – 8 April 2020
Wave 3: 10 April 2020 – 16 April 2020
Wave 4: 17 April 2020 – 21 April 2020
Wave 5: 24 April 2020 – 29 April 2020
Wave 6: 1 May 2020 – 6 May 2020
Wave 7: 8 May 2020 – 13 May 2020
Wave 8: 15 May 2020 – 20 May 2020
Wave 9: 23 May 2020 – 27 May 2020
Wave 10: 29 May 2020 – 3 June 2020
Wave 11: 12 June 2020 – 17 June 2020
Wave 12: 26 June 2020 – 1 July 2020
Wave 13: 10 July 2020 – 15 July 2020
Wave 14: 14 August 2020 – 19 August 2020
Wave 15: 11 September 2020 – 18 September 2020
Wave 16: 16 October 2020 – 23 October 2020
Wave 17: 13 November 2020 – 20 November 2020
Wave 18: 11 December 2020 – 18 December 2020
Wave 19: 15 January 2021 – 22 January 2021
Wave 20: 12 February 2021 – 19 February 2021
Wave 21: 12 March 2021 – 19 March 2021
Wave 22: 16 April 2021 – 23 April 2021
Wave 23: 21 May 2021 – 28 May 2021
Wave 24: 25 June 2021 – 2 July 2021
Wave 25: 24 September 2021 – 1 October 2021
Wave 26: 22 October 2021 – 29 October 2021
Wave 27: 26 November 2021 – 03 December 2021
Wave 28: 14 January 2022 – 21 January 2022
Wave 29: 18 February 2022 – 25 February 2022
Wave 30: 18 March 2022 – 25 March 2022
Wave 31: 22 April 2022 – 29 April 2022
Wave 32: 20 May 2022 – 29 May 2022
Wave 33: 21 October 2022 – 31 October 2022
Wave 34: 17 February 2023 – 27 February 2023
Wave 35: 23 June 2023 – 06 July 2023

The median interview duration was:

Wave 1: 16.4 min
Wave 2: 20.9 min
Wave 3: 16.7 min
Wave 4: 19.7 min
Wave 5: 22.6 min
Wave 6: 18.7 min
Wave 7: 16.9 min
Wave 8: 18.7 min
Wave 9: 21.4 min
Wave 10: 21.3 min
Wave 11: 17.3 min
Wave 12: 19.7 min
Wave 13: 17.9 min
Wave 14: 26.1 min
Wave 15: 16.8 min
Wave 16: 20.8 min
Wave 17: 20.5 min
Wave 18: 24.9 min
Wave 19: 17.5 min
Wave 20: 22.6 min
Wave 21: 20.2 min
Wave 22: 24.0 min
Wave 23: 24.8 min
Wave 24: 23.7 min
Wave 25: 20.5 min
Wave 26: 18.5 min
Wave 27: 21.4 min
Wave 28: 19.1 min
Wave 29: 23.3 min
Wave 30: 22.5 min
Wave 31: 16.5 min
Wave 32: 18.7 min
Wave 33: 20.8 min
Wave 34: 17.9 min
Wave 35: 28.5 min

3.3 Panel participation

Respondents were asked and incentivized with 180 credit points to participate in each wave of the panel. The invitation for necessary fresh panelists (if the retention was not sufficiently large, n < 1.500) was sent out a few days after field-work started. All respondents who participated in the previous wave were also invited to participate in subsequent waves.

Net sample sizes were as follows:

Wave 1: 1541
Wave 2: 1559
Wave 3: 1500
Wave 4: 1528
Wave 5: 1515
Wave 6: 1551
Wave 7: 1517
Wave 8: 1501
Wave 9: 1502
Wave 10: 1504
Wave 11: 1510
Wave 12: 1522
Wave 13: 1532
Wave 14: 1540
Wave 15: 1581
Wave 16: 1670
Wave 17: 1592
Wave 18: 1567
Wave 19: 1612
Wave 20: 1574
Wave 21: 1573
Wave 22: 1533
Wave 23: 1503
Wave 24: 1513
Wave 25: 1511
Wave 26: 1514
Wave 27: 1551
Wave 28: 1524
Wave 29: 1522
Wave 30: 1507
Wave 31: 1528
Wave 32: 1557
Wave 33: 1535
Wave 34: 1536
Wave 34: 1555

A total of 208 respondents completed all waves (Wave 1 to 35).

Initial participation rate:

Wave 1: 35.2% (1541 interviews, 4381 invitations)

Retention rates for panelists were as follows:

Wave 2: 86.2% (1328 interviews, 1541 W1 invitations)
Wave 3: 81.3% (1437 interviews, 1768 W1-2 invitations)
Wave 4: 78.1% (1430 interviews, 1831 W1-3 invitations)
Wave 5: 73.8% (1425 interviews, 1931 W1-4 invitations)
Wave 6: 74.3% (1499 interviews, 2018 W1-5 invitations)
Wave 7: 70.8% (1464 interviews, 2067 W1-6 invitations)
Wave 8: 69.1% (1460 interviews, 2114 W1-7 invitations)
Wave 9: 67.6% (1456 interviews, 2153 W1-8 invitations)
Wave 10: 65.5% (1447 interviews, 2208 W1-9 invitations)
Wave 11: 65.7% (1484 interviews, 2258 W1-10 invitations)
Wave 12: 61.3% (1399 interviews, 2283 W1-11 invitations)
Wave 13: 62.5% (1505 interviews, 2409 W1-12 invitations)
Wave 14: 55.8% (1355 interviews, 2429 W1-13 invitations)
Wave 15: 59.0% (1573 interviews, 2664 W1-14 invitations)
Wave 16: 63.6% (1662 interviews, 2612 W1-15 invitations)
Wave 17: 60.4% (1572 interviews, 2603 W1-16 invitations)
Wave 18: 56.4% (1474 interviews, 2614 W1-17 invitations)
Wave 19: 58.9% (1596 interviews, 2709 W1-18 invitations)
Wave 20: 58.2% (1574 interviews, 2706 W1-19 invitations)
Wave 21: 58.1% (1573 interviews, 2706 W1-20 invitations)
Wave 22: 55.8% (1533 interviews, 2695 W1-21 invitations)
Wave 23: 54.1% (1472 interviews, 2721 W1-22 invitations)
Wave 24: 50.3% (1385 interviews, 2751 W1-23 invitations)
Wave 25: 48.4% (1385 interviews, 2861 W1-24 invitations)
Wave 26: 50.9% (1514 interviews, 2974 W1-25 invitations)
Wave 27: 51.8% (1538 interviews, 2968 W1-26 invitations)
Wave 28: 49.5% (1469 interviews, 2967 W1-27 invitations)
Wave 29: 48.9% (1474 interviews, 3012 W1-28 invitations)
Wave 30: 48.4% (1478 interviews, 3052 W1-29 invitations)
Wave 31: 47.0% (1446 interviews, 3076 W1-30 invitations)
Wave 32: 46.0% (1450 interviews, 3150 W1-31 invitations)
Wave 33: 45.0% (1450 interviews, 3221 W1-32 invitations)
Wave 34: 45.0% (1466 interviews, 3282 W1-33 invitations)
Wave 35: 40.4% (1466 interviews, 3327 W1-34 invitations)

Participation rates of replacements for drop-outs:

Wave 2: 5.2% (231 interviews, 4401 invitations)
Wave 3: 4.3% (63 interviews, 1469 invitations)
Wave 4: 5.6% (98 interviews, 1760 invitations)
Wave 5: 5.4% (90 interviews, 1670 invitations)
Wave 6: 8.1% (52 interviews, 640 invitations)
Wave 7: 4.3% (53 interviews, 1220 invitations)
Wave 8: 5.9% (41 interviews, 690 invitations)
Wave 9: 3.1% (46 interviews, 1502 invitations)
Wave 10: 3.2% (57 interviews, 1800 invitations)
Wave 11: 3.0% (26 interviews, 870 invitations)
Wave 12: 4.5% (123 interviews, 2720 invitations)
Wave 13: 2.4% (27 interviews, 1130 invitations)
Wave 14: 6.5% (185 interviews, 2830 invitations)
Wave 15: 4.0% (8 interviews, 200 invitations)
Wave 16: 5.3% (8 interviews, 150 invitations)
Wave 17: 4.0% (20 interviews, 500 invitations)
Wave 18: 6.4% (93 interviews, 1450 invitations)
Wave 19: 4.0% (16 interviews, 400 invitations)
Wave 20: -/- (0 interviews, 0 invitations)
Wave 21: -/- (0 interviews, 0 invitations)
Wave 22: 4.0% (28 interviews, 700 invitations)
Wave 23: 4.5% (31 interviews, 694 invitations)
Wave 24: 5.9% (128 interviews, 2152 invitations)
Wave 25: 3.4% (126 interviews, 3670 invitations)
Wave 26: -/- (0 interviews, 0 invitations)
Wave 27: 4.3% (13 interviews, 300 invitations)
Wave 28: 3.1% (55 interviews, 1800 invitations)
Wave 29: 4.0% (48 interviews, 1200 invitations)
Wave 30: 3.6% (29 interviews, 800 invitations)
Wave 31: 3.9% (82 interviews, 2100 invitations)
Wave 32: 3.4% (107 interviews, 3149 invitations)
Wave 33: 3.3% (85 interviews, 2600 invitations)
Wave 34: 3.3% (70 interviews, 2100 invitations)
Wave 35: 2.6% (211 interviews, 8076 invitations)

4 Data Cleaning

4.1 Data protection (all editions)

Timestamps were truncated to the date of interview and respondent identifiers were replaced by an anonymized random number for reasons of data protection.

4.2 Data protection: Scientific Use File (SUF)

Some variables were recoded or dropped from the data file for reasons of data protection (see variable list for documentation).

4.3 Data protection: Open Access (OA)

Some variables were recoded or dropped from the data file for reasons of data protection (see variable list for documentation).

4.4 Variable format and missing values

For most variables missing values were coded as follows:

88 = don’t know
99 = no answer

4.5 List of known issues

The file of the variable list also includes a documentation of known issues.

5 Weighting of data

The dataset includes two types of weights which can be used for post-stratification adjustment to known population distributions (for each wave separately):

Demographic weight (W*_WEIGHTD)
Demographic + Political weight (W*_WEIGHTP)

Two additional weights are included to weight respondents who participated in the first ten waves according to the known target distributions (W1W10_WEIGHTD, W1W10_WEIGHTP).

Two additional weights are included to weight respondents who participated in the first fifteen waves according to the known target distributions (W1W15_WEIGHTD, W1W15_WEIGHTP).

Two additional weights are included to weight respondents who participated in the first twenty waves according to the known target distributions (W1W20_WEIGHTD, W1W20_WEIGHTP).

Two additional weights are included to weight respondents who participated in the first twenty-four waves according to the known target distributions (W1W24_WEIGHTD, W1W24_WEIGHTP).

Two additional weights are included to weight respondents who participated in the first thirty waves according to the known target distributions (W1W30_WEIGHTD, W1W30_WEIGHTP).

Two additional weights are included to weight respondents who participated in all waves according to the known target distributions (ALLWAVES_WEIGHTD, ALLWAVES_WEIGHTP).

Target distributions are based on Micro Census data (Statistics Austria StatCube 2019) and official election results are provided by the Austrian Ministry of the Interior. The demographic weight adjusts the following demographics to the target distributions: gender, age, gender X age, education, region (Bundesland), employment status, household size, and migration background. The demographic + political weight, in addition, uses the vote recall to match the election result of the 2019 national elections. For the computation of weights, we used the STATA module “ipfweight”. Weighting variables were trimmed to a minimum value of 0.20 and a maximum value of 5.00. Missing values on the variables used were weighted neutrally.