In psychology, the theory of reasoned action links attitudes to behaviour. According to the theory of reasoned action, action is guided by behavioural intention, which is influenced by perceived norms and subjective attitudes (Ajzen and Fishbein, 1980). In turn, attitudes are considered as the evaluative beliefs about an attitude object. Consistent with this background, and in contrast to existing longer instruments that concentrate on measuring a general survey attitude (e.g., Hox, de Leeuw, and Vorst, 2015; Stocké and Langfeldt, 2004), we aimed at a multidimensional measurement instrument.

An international literature search on empirical studies that investigated general attitudes and opinions on surveys resulted in three clear theoretical dimensions: two positive and one negative dimension could be distinguished that have recognizable roots in the survey methodology literature (Dillman, Smyth and Christina, 2014; Groves, 1989; Groves and Couper, 1998; Stoop et al., 2010). The first and second dimension describe attitudes that guide the behavioural intentions of potential respondents positively (Cialdini,1984). The first dimension reflects the individual perception of surveys as a positive experience: survey enjoyment, as discussed by Cialdini (1984) and reflected in the work of Puleston (2012) on gamification to increase the enjoyment of the survey experience. The second dimension points to a positive survey climate and emphasizes the subjective importance and value of surveys, as discussed by Rogelberg, Fisher, Maynard, Hakel, and Horvath (2001). The third dimension indicates a negative survey climate: surveys are perceived by respondents as a burden, which has a negative influence on motivation and participation (Goyder, 1986; Schleifer, 1986). Survey designers and methodologist have to try and counteract this negative attitude by decreasing the perceived burden (Dillman, 1978; Puleston, 2012).

These three dimensions are fundamental building blocks in theories on survey participation and nonresponse and are seen as important indicators of a deteriorating survey climate (Barbier, Loosveldt, and Carton, 2016; Loosveldt and Joye, 2016; Singer, van Hoewyk, and Maher, 1998). For instance, both the social exchange theory (Dillman, 1978) and the leverage saliency theory (Groves, Singer, and Corning, 2000) on survey participation emphasize that people are more willing to participate in a survey when the positive aspects of the survey are maximized and the negative aspects are minimized (Dillman, et al., 2014). These theories emphasize that for a positive decision to cooperate in a survey the perceived benefits should outweigh the perceived costs. This is achieved if a survey is seen as pleasant and fun (survey enjoyment), useful (survey value), and associated with minimal costs (survey burden).

Previous research that investigated attitudes toward surveys used one-dimensional to five-dimensional scales when measuring survey attitudes (Hox et al., 1995; Loosveldt and Storms, 2008; Rogelberg et al., 2001; Stocké and Langfeldt, 2004; Stocké, 2006, 2014). Hox et al. (1995) proposed a one-dimensional general attitude towards surveys, based on eight items. Stocké and Langfeldt (2004) and Stocké (2006) used a one-dimensional measure of general survey attitude, based on 16 items. Later, Stocké (2014) proposed a three-dimensional survey attitude measure with scales measuring survey value, survey reliability, and survey burden. Rogelberg et al. (2001) discerned two dimensions: survey enjoyment and survey value, based on 6 items. Finally, Loosveldt and Storms (2008) suggested five dimensions (survey value, survey cost, survey enjoyment, survey reliability, and survey privacy) based on a survey attitude questionnaire with nineteen items.

All studies on survey attitudes involved the positive dimension “survey value”, while the importance of “survey enjoyment” was noted by Rogelberg et al. (1997) and Loosveldt and Storms (2008). The concept “survey burden” that was mentioned by Stocké (2014) was referred to as “survey costs” in the work of Loosveldt and Storms (2008). These three common dimensions, survey enjoyment, survey value, and survey burden are also important concepts in theories on survey participation and nonresponse. Therefore, survey enjoyment, survey value, and survey burden were chosen as the three main constructs in the survey attitude scale.

Item generation and selection

For each construct in the survey attitude scale (i.e., enjoyment, value, and burden), we selected three questions that performed well in previous empirical research publications. Three questions per construct were selected as this is the minimum to identify a construct in a confirmatory factor model (Bollen, 1989, p. 244) needed to establish measurement equivalence over countries and modes. As the survey attitude scale was developed for regular use in both single-mode and mixed-mode surveys, we followed the recommendations for mixed-mode questionnaire construction (Dillman et al., 2014; Dillman and Edwards, 2016) and used a seven-point disagree/agree response scale that was endpoint labelled.

Survey enjoyment

In studies on nonresponse and survey attitudes, statements referring to enjoyment, such as, “I really enjoy responding.” are frequently posed (Cialdini, Braver, and Wolf, 1991; Hox et al., 1995; Loosveldt and Storms, 2008; Rogelberg et al., 2001). As our goal was to develop a general survey attitude scale that could also be used in mixed-mode studies, we included two questions on enjoyment (one referring to mail and online questionnaires, and one referring to interviews). Besides the direct emotional enjoyment, need for cognition can act as intrinsic motivation (Stocké, 2006). Thus, we added Stocké’s question on interest in surveys to the subscale on survey enjoyment. A similar question on survey interest was used by Hox et al. (1995) and Loosveldt and Storms (2008).

Survey value

Salience, relevance, and usefulness are all important for survey participation, and emphasizing these aspects plays an important role in theories of persuasion (Cialdini, 1984; Cialdini et al., 1991; Dillman, 1978; Groves, Cialdini, and Couper, 1992; Groves et al., 2000). From the literature on survey attitudes, we therefore selected a question on the importance of surveys for society that was used by multiple researchers in this field (i.e., Cialdini et al., 1991; Hox et al., 1995; Stocké, 2006) and a second question on the usefulness of the information gathered by surveys from Singer et al. (1998), which was also used by Rogelberg et al. (2001) and Loosveldt and Storms (2008). We also added a negatively formulated question on surveys as “a waste of time,” as an indicator of survey relevance. This question was based on the work of Rogelberg et al. (2001), Schleifer (1986), and Singer et al. (1998); a similar question was also used by Hox et al. (1995) and Loosveldt and Storms (2008).

Survey burden

According to Roper (1986) and Cialdini et al. (1991), an important aspect of the perceived survey burden is the amount of received requests to participate. Thus, we included a question on receiving too many requests in the subscale survey burden. This question was used in previous research on survey attitudes by Cialdini et al. (1991) and Hox et al. (1995). In addition, Stocké (2006) emphasized survey length as an indicator of burden and we added a question on this. Finally, Schleifer (1986) and Goyder (1996) pointed out the importance of privacy concerns, thus, we included a question on the invasion of privacy. Loosveldt and Storms (2008) used three slightly different questions to tap privacy as a sub-dimension. As our goal was to construct a brief survey attitude scale, we followed Shleifer (1986) and Goyder (1996) and only used one question on the invasion of privacy as part of the subscale survey burden.

Translation

The master questionnaire was developed in English; for the full text of the nine questions and references see Table 2. This master questionnaire was translated into Dutch and German. The translations were done by bilingual survey experts and checked with the original developer of the English master questionnaire and with senior staff of online panels in the Netherlands and Germany.

Table 2

English version of Survey Attitude questions followed by references to publications on which the respective question was based

Samples

 For the Netherlands, the data were collected online in the then newly established LISS panel from May to August 2008. The LISS panel is a probability-based online panel of approximately 4,500 Dutch households (7,000 individuals). It was established in autumn 2007 at the University of Tilburg and funded by a grant of the Dutch Science Foundation (NWO). The original recruitment was based on a random nationwide sample of addresses drawn from the community registers by Statistics Netherlands. The response to the recruitment interview was 75%, of which 84% (63% of gross sample) were willing to take part in the panel; finally, 48% of the gross sample did become an active panel member (Scherpenzeel and Das, 2011). Households with and without the Internet were recruited. Those without the Internet were provided with a ‘simple PC’ and internet connection. Panel members complete questionnaires each month with a duration of 15–30 min. Every year, they also complete a ‘core’ questionnaire. This longitudinal CORE study provides a wide range of data on the panel members that can be combined with the data of the individual ad-hoc studies. The survey attitude scale was part of the core questionnaire from 2008 to 2011. Panel members are paid for completing a questionnaire; this is based on the estimated average completion time (7.50 euro for 30 min). The present study used the survey attitude scale from the first wave of the core questionnaire and data were collected from 6808 individuals (wave response 78.1%). For more information on the LISS panel, see https://www.lissdata.nl/about-panel.

For Germany, data were collected in spring 2009 during recruitment interviews for the probability-based mixed-mode PPSM panel. Panel members could be contacted through different modes: telephone only (4,900), telephone and online (1,600), online only (100). The panel was established in spring/summer 2009 and consisted of approximately 6,600 persons in Germany. It was part of the Priority Program on Survey Methodology (PPSM) at the University of Bremen and was funded by the German Research Foundation (See also, http://www.survey-methodology.de). The original recruitment was based on a random nationwide sample of telephone numbers (RandomLastDigit both landline and mobile). Selection criteria were 18+ and entitled to vote in Germany. The response (completed interviews) was 13.6% (a typical response rate for telephone surveys in Germany at the time); partial interviews added an additional 0.9% (Engel, 2015). Panel members completed questionnaires regularly every 4 months and do not receive any payment. The study was longitudinal in character and served a scientific purpose. The survey attitude scale was part of the recruitment interview, which was administered by telephone (CATI) and took on average 20 min. In total, data were collected from 6,200 individuals during that interview.

The second Germany-based data collection took place in 2014 in the GESIS panel. The GESIS panel is a probability-based mixed-mode (online and postal mail) panel of the general population in Germany consisting of German-speaking individuals who live in private households aged 18–70 at the time of recruitment. The GESIS Panel was recruited from in 2013 via face-to-face interviews, based on a sample drawn from the German federal states’ central population registers. The response to the face-to-face recruitment interview was 35.5%, and 81.7% of those who responded to the recruitment interview were willing to participate in the first self-administered welcome survey, of which 79.5% became active panel members (Bosnjak et al., 2018). Thus, the active panel at the time of recruitment consisted of 4,938 respondents. Since then, the panel underwent two refreshments (in 2016 and in 2018) and in 2018 consist of about 4,400 respondents. About 67% of respondents complete the surveys online, while about 33% respond via mail. The field period for each wave is set at two months with six-panel waves per year. The questionnaires take about 20 min to complete. Every bimonthly questionnaire contains questions from the longitudinal core study that are repeated every year. The survey attitude scale is implemented in the last wave of each year in the core study module “Panel survey participation evaluation, survey mode preferences” and has been running since 2014. The survey attitude scale was implemented in the first year of the panel’s operation (last wave of 2014). In total, 4,344 respondents were invited of whom 3,775 completed the survey attitude scale (wave response: 88.7%)

(Struminskaya, Schaurer, and Enderle, 2015). For more information about the GESIS panel see http://www.gesis-panel.org.

Item analyses

As the samples in this study are large, the fit of the model was evaluated by three established fit indicators: CFI, TLI, and RMSEA. Generally recognized criteria are for CFI and TLI that a value of 0.90 indicates acceptable fit, and 0.95 and higher values indicate a good fit. For the RMSEA values below 0.08 indicate acceptable fit, and values below 0.05 indicate good fit (Kline, 2016).

The basic theoretical model is a confirmatory factor model with three factors, enjoyment, value, and burden, and with questions only loading on their intended factor. In a preliminary analysis, we checked if a single factor indicating a general survey attitude would suffice. We used Mplus 8.2 with robust maximum likelihood estimation (Muthén and Muthén, 2017). The single factor model was clearly rejected in all three samples, the fit indices were far from their acceptable values. Next, the theoretical model was estimated separately in all three samples. The theoretical three-factor model fitted moderately well. Fit indices were: for the GESIS data, χ2 (df = 24) = 653.3, CFI = .92, TLI = .88, RMSEA = .08, for the LISS data, χ2 (df = 24) = 1381.8, CFI = .91, TLI = .84, RMSEA = .10, and for the PPSM data, χ2 (df = 24) = 1255.3, CFI = .90, TLI = .86, RMSE = .09. In all three analyses, modification indices suggested the same two additional loadings: enjoyment question 3 (surveys are interesting) received an additional loading on the value factor, and value question 3 (surveys are a waste of time) received an additional loading on the burden factor. This model fitted very well in all three panels: for the GESIS panel data, χ2 (df = 22) = 102.8, CFI = .99, TLI = .98, RMSEA = .03; for the LISS panel data, χ2 (df = 22) = 350.4, CFI = .99, TLI = .98, RMSEA = .03; and for the PPSM panel data, χ2 (df = 22) = 137.1, CFI = .99, TLI = .99, RMSEA = .03. Figure 1 depicts the modified model.

Figure 1. Final factor model for the survey attitude scale

Thus, the factor structure of the survey attitude scale was established using data from three probability-based panels in two countries. In the analyses reported here, there were two cross-loadings. One enjoyment question (surveys are interesting) also had a loading on the value factor, and one value question (surveys are a waste of time) had a loading on the burden factor. These double loadings make sense: when a survey is evaluated as “interesting,” it is usually also perceived to be valuable, and when a survey is evaluated as “a waste of time,” it can be perceived as burdensome. This factor structure was replicated in all of the three panels, GESIS, LISS, and PPSM.

Item parameters

Below we present the item characteristics for each panel in a separate Table. (3A-C). Reported are the mean, standard deviation, skewness, kurtosis and item-total correlation (selectivity) for each of the nine questions. The item-total correlation (selectivity) was calculated for each subscale (i.e., enjoyment, value, and burden) separately. That is for the questions E1 to E3 the item-total correlation was calculated with Enjoyment, for the questions V1 to V3 the item-total correlation was calculated with Value, and for the questions B1 to B3 the item-total correlation was calculated with Burden,

Table 3A GESIS Panel

Means, Standard Deviations, Skewness, Kurtosis, and Selectivity (item-total correlations) of the Manifest Items:

Note. Scale ranging from 1 to 7, listwise N = 3,655.

Table 3B LISS Panel

Means, Standard Deviations, Skewness, Kurtosis, and Selectivity (item-total correlations) of the Manifest Items:

Note. Scale ranging from 1 to 7, listwise N = 6,802.

Table 3C

Means, Standard Deviations, Skewness, Kurtosis, and Selectivity (item-total correlations) of the Manifest Items: PPSM data

Note. Scale ranging from 1 to 7, listwise N = 5,838.

Objectivity

The Survey Attitude Scale is developed for scientific research into survey climate and nonresponse and not for individual diagnostic purposes. The Survey Attitude Scale is not a personality test, or otherwise diagnostic instrument. Hence, objectivity in evaluation and interpretation of individual scores and norm tables does not apply. Our overarching goal was to develop and validate a concise and effective measure of feelings about surveys and about responding to surveys, that can be successfully implemented in cross-sectional surveys, longitudinal studies, and respondent panels to investigate survey climate and the willingness to participate in surveys in order to improve survey effectiveness.

There are several factors supporting objectivity. First of all, a standardized questionnaire format including response categories and brief instruction is used for the Survey Attitude Scale. Second, this questionnaire was developed to be mode independent to ensure successful implementation in face-to-face, telephone, paper mail, and web survey modes. We followed the rules for uni(fied) or omni mode design as described by Dillman, Smyth, and Christian (2014, chapter11) and by Schouten, van de Brakel, Buelens, Giesen, Luiten, and Meertens, 2022, section 6.6). Third, a cognitive pretest was done of the Dutch version, which indicated that the respondent understood the instruction, question text and response format as intended (see also section Application Field). Furthermore, statistical analysis of the GESIS data, in which both the paper mail (offline) and web (online) mode was used showed scalar measurement equivalence which means that the survey mode (online vs. offline) did not affect the measurement model. Finally, it was investigated whether the Dutch and German version of the Surveys Attitude Scale had measurement equivalence. Multigroup Confirmatory Factorial Analysis indeed showed that measurement equivalence was found cross-culturally between the Netherlands and Germany (see section Further quality criteria). In sum, the Survey Attitude Scales has objectivity for mode and cross cultural implementation.The scores can be calculated directly and objectively, no interpretation from the investigator is needed.

Reliability

As an indicator of reliability, we calculated McDonald’s coefficient Omega (McDonald, 1999, p. 89) for each subscale and for the total scale using the software Factor (Lorenzo-Seva and Ferrando, 2013). Coefficient Omega gives a lower bound for the reliability and can be interpreted as the proportion of “true” score variance in the observed scores. It is similar to Cronbach’s coefficient Alpha, but requires weaker assumptions. If the assumptions for coefficient Alpha are met, Omega and Alpha are equal. Table 4 presents the coefficient omega for all subscales and the total scale, with coefficient Alpha in parentheses.

Table 4

Reliability of Survey Attitude (Sub-)Scales: Coefficient Omega (Coefficient Alpha)

Note. The reliability was indicated by McDonald’s Omega; Cronbach’s Alpha in parentheses

 Four main conclusions can be drawn from Table 4. Firstly, the two reliability coefficients are highly similar across the three panels. Secondly, two of the three subscales have good reliability for such short scales; only the subscale “burden” has a relatively low reliability. Thirdly, combining the three subscales into one global attitude scale is not worthwhile: the reliability does not increase and using the subscales as separate predictors in further analyses is more informative. Finally, the estimates for coefficient Omega and Alpha are very close, which implies that the assumptions underlying the use of coefficient Alpha are met. This is important since this justifies using simple sum scores for the scales.

In sum, the anticipated three-factor structure fitted the data well across the three panels and the reliability of the three subscales was sufficient.

Validity

Construct validity

There are indications for the construct validity of the survey attitude scale. During the recruitment interview for the PPSM panel, respondents were asked about their past survey behaviour and the reason why they had cooperated. Potential reasons for cooperation were rated on a 7-point scale. The correlations between the survey attitude subscales and the reason for cooperation are summarized in Table 5.

Table 5

Correlations between Survey Attitude Scales and Reasons for Previous Survey Participation Questions: PPSM Panel

Note. All correlations significant at p < .05 unless marked ns.

 The correlations were in the expected directions. For instance, persons who scored high on general willingness to cooperate also scored high on survey enjoyment (r_{enjoy,willing} = .58), relatively high, but slightly lower on survey value (r_{value,willing} = .41), and clearly did not see surveys as a burden (r_{burden, willing} = -.26). Similar patterns were seen for persons who thought the topic was interesting and had the feeling that they could say something about the topic, while persons who said that they just could not say “no” to a request scored low on survey enjoyment (r_{enjoy, not no} = -.19), low on survey value (r_{value, not no} = -.15), and high on survey burden (r_{burden, not no} = .15). Finally, persons who emphasized the scientific nature of the survey as a reason to cooperate or were more altruistic only scored high on survey value (r_{value, scientific} = .17; r_{value, help} = .16).

All three panels asked the same three evaluation questions about the survey; for the LISS and the GESIS panel, these were asked at the end of the welcome survey, for PPSM at the end of the recruitment interview. The questions were based on the standard evaluation questions at the end of each LISS-questionnaire: respondents were asked ‘whether they thought the topic was interesting’, which gives an indication of saliency, ‘whether the questions were difficult to answer’ where a negative evaluation indicates burden, and ‘whether the questionnaire got them thinking about things’, which can be viewed as a generally positive evaluation of the survey (Schonlau, 2015). The correlations for these survey evaluation questions and the survey attitude subscales for the three panels are presented in Table 6.

Table 6

Correlations Between Survey Attitude Scales and Survey Evaluation Questions for Three Panels: GESIS, LISS and PPSM Panel

Note: All correlations significant at p < .05 unless marked ns.

Although the absolute values of the correlations differ, all three panels showed the same pattern in the correlation matrix. The correlations between the survey attitude subscales and the evaluation of the survey are in the expected directions for all three panels. Respondents, who scored high on survey enjoyment and value and did not see surveys as a burden, rated the topic of the survey as interesting. On the other hand, respondents, who scored high on survey burden and did not value or enjoy surveys, rated the questions as difficult. Finally, respondents, who scored high on survey enjoyment and value, more often stated that the questionnaire got them thinking about things, while there was no clear relation with survey burden.

In sum, there are indications for construct validity. The survey attitude scales were related both to reasons why one had cooperated in previous research and to survey evaluation.

Predictive validity

There are indications for the predictive validity of the survey attitude scale. A previous study involving the Dutch CentER panel, an online panel that was established in 1991, used logistic regression to predict nonresponse from March 2007 until August 2008 (de Leeuw et al., 2010). Survey enjoyment, value, and burden all predicted panel nonresponse. The effects were small but significant and in the expected direction with survey enjoyment as the strongest predictor (B_Enjoy = -.13, B_Value = -.02, B_Burden = .06).

During the recruitment interview for the LISS panel, one question from the survey value subscale was asked: “V1: Surveys are important for society.” At the end of the recruitment interview, respondents were asked if they were willing to become a panel member. The correlation between this question on survey value and the stated willingness to participate in the panel is .24. The correlation between survey value and active panel membership (defined as completing the first self-administered online panel questionnaire) was slightly lower: r = .18. Both correlations were significant at p < .01 (de Leeuw, Hox, Scherpenzeel, and Vis, 2008).

At the end of the recruitment interview for the PPSM panel, respondents were asked if they were willing to be surveyed again. The correlations between willingness and the three survey attitude subscales were all significant (p < .01) and in the expected direction: .31 between survey enjoyment and willingness to participate, .24 between survey value, and willingness, and -.20 between survey burden and willingness.

Finally, for the GESIS panel, the correlations between the survey attitude subscales and participation in the very next panel wave were low but significant and in the expected direction: .04 for survey enjoyment, .05 for survey value, and -.05 for survey burden (all p < .01).

Summing up, the three subscales predicted stated willingness to participate and actual participation consistently, which is in line with the findings of Rogelberg et al. (2001), who reported that indicators for survey enjoyment and survey value were both positively related to stated willingness to complete telephone, in-person, and mail surveys.

Descriptive statistics and Scaling

Scores for the subscales are computed as the mean of the available (recoded) questions (V3 (waste of time should be recoded). When calculating the mean, it should be indicated that at least two items must have valid values, otherwise the subscale is assigned a missing vale.

Table 7 provides the means, standard deviations, skewness and kurtosis for the three subscales, for each of the panels. The survey attitude scales are not intended for diagnostic purposes on individual (person) level, therefore there are no standardized or normed scores.

Table 7

Means, Standard Deviations, Skewness, and Kurtosis of the Scale scores.

Note. Scale –score ranging from 1 to 7.

 Further quality criteria

Since there is a Dutch and a German version, it is important to investigate whether there is measurement equivalence between these two versions. We used the Multigroup Confirmatory Factor Analysis (MG-CFA) to test hypotheses concerning measurement equivalence between groups. If the factor loadings are invariant across all groups, there is metric equivalence (Vanderburg and Lance, 2000). If, in addition, all intercepts are invariant, there is scalar equivalence. Although the ideal situation is achieving complete scalar measurement invariance across all groups, in practice a small amount of variation is acceptable, which leads to partial measurement invariance (Byrne, Shavelson, and Muthén, 1989; Steenkamp and Baumgartner, 1998).

It should be noted that the GESIS panel uses two modes: online and offline (paper mail). Prior to comparing the panels, a MG-CFA with two groups was used to test if there is measurement equivalence between the two modes. Specifying full scalar measurement equivalence led to an excellent model fit (χ2 (df = 58) = 169.3, CFI = .99, TLI = .98, RMSEA = .03). Thus, the survey mode (online vs. offline) did not affect the measurement model.

Measurement equivalence testing using MG-CFA with three groups (GESIS, LISS, and PPSM) revealed partial scalar equivalence. All loadings could be constrained equal across all three panels. There was complete scalar equivalence between the GESIS and the LISS panel, which are both self-administered. In the PPSM model, the intercepts of E1 and V3 had to be estimated separately, indicating partial scalar equivalence for the PPSM, where the data for the survey attitude scale were collected by telephone interviews. With these two modifications, the model fitted well (χ2 (df = 92) = 1590.2, CFI = .96, TLI = .95, RMSEA = .05).

Table 8 presents the unstandardized factor loadings for the GESIS, LISS, and PPSM panels, with values constrained equal across the three panels (measurement equivalence model). A second-order model with a general factor underlying the factors enjoyment, value and burden, specifying full scalar equivalence for the second-order general factor, fits less well (χ2 (df = 98) = 2119.8, CFI = .94, TLI = .94, RMSEA = .06), but was still acceptable. A model that constrained the variances and covariances to be equal across all three panels also fitted less well (χ2 (df = 104) = 2287.3, CFI = .94, TLI = .94, RMSEA = .06), but was still acceptable. The constrained model permits estimating a single set of correlations between the factors. These correlations were .59 between enjoyment and value, -.44 between enjoyment and burden, and -.36 between value and burden. These indicate sufficient discrimination between the three factors, which makes inadvisable to combine the three subscales into a single summated score.

Table 8 Results

Measurement Equivalence Model (with factor loadings constrained equal)

Factor Loadings Survey Attitude Scale (Unstandardized) for the GESIS, LISS and PPSM panels.

Note. The values are based on the unstandardized solution.

In sum, measurement equivalence was found cross-culturally between the Netherlands and Germany. Furthermore, for the German GESIS panel measurement equivalence was also established between the online mode and the paper mail mode.

Further literature

The scale was first published in the journal Measurement Instruments for the Social Sciences. 2019. https://measurementinstrumentssocialscience.biomedcentral.com/articles/10.1186/s42409-019-0012-x

Acknowledgement

The authors thank Remco Feskens for his help in developing the German version of the survey attitude scale; Simone Bartsch, Uwe Engel, Anja Göritz, and Helen Lauff for implementing the first German version; Annette Scherpenzeel for implementing the Dutch version; and Peter Lugtig and Don Dillman, the editors, and two anonymous reviewers of Measurement Instruments for the Social Sciences for their helpful comments. We also thank from Katherina Groskurth from ZIS, GESIS for her work and help in making this excerpt.

• Bella Struminskaya, Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands; b.struminskaya@uu.nl

GESIS panel https://doi.org/10.4232/1.12658),

LISS panel https://www.dataarchive.lissdata.nl/study_units/view/15

PPSM panel https://doi.org/10.5281/zenodo.7111310