Subjective linguistic vitality (SEV) of German among minority German speakers in an English-majority-language community

Abstract

Purpose:

One important motivation to maintain a minority language is the perception that it allows people to connect meaningfully with other people who speak that language. Subjective ethnolinguistic vitality (SEV) refers to the perception that a language is important and useful. The primary purpose of this study was to test whether the language of the interview affected SEV of German among participants living in an English-majority-language part of Canada. A secondary purpose was to test whether cumulative use/exposure of German was related to German SEV.

Approach:

To address the primary purpose, we tested whether the language of the interview affected participants’ SEV. To address the secondary purpose, we tested whether length of residency and/or German proficiency were predictors of German SEV in a linear regression.

Data:

Speakers of German in an English-majority-language community (N = 30) were interviewed in both German and English about the SEV of German (about a month between language sessions). According to census data, German speakers make up 2% of the local population.

Findings:

The participants estimated the local German-speaking population at 16% (on average), regardless of the interview language. On other questions, participants estimated a moderate SEV for German (averaging around 4 on a scale of 1–10), with no difference by interview language. Length of residency was a negative predictor of participants’ estimates of the local German-speaking population. German proficiency was a positive predictor of SEV ratings.

Implications:

These results suggest that personal experience and motivation to maintain German might contribute to more positive SEV of German.

Originality:

This study found no evidence that the language of the interview affected participants’ SEV of a minority language. There were different predictors of SEV, depending on which aspect of SEV was considered (i.e., estimate of the local population vs. other measures of SEV).

Keywords

Subjective ethnolinguistic vitality foreign language effect minority languages estimation German as a minority language heritage languages

Introduction

For a language to survive, it must be actively used (Fishman, 1966; Nagpal & Nicoladis, 2010; Tseng, 2021). One important factor in language survival is ethnolinguistic vitality (Jamallullail & Nordin, 2023; Ravyse, 2021; see Giles et al., 1985, for the theory of ethnolinguistic vitality). Ethnolinguistic vitality refers to the variables that lead a group to behave as a distinctive group when interacting with other groups (Giles et al., 1977; Landry & Allard, 1994). For example, Italo-Canadians living in Toronto can (and often do) conceptualize themselves as an ethnolinguistic group (Feuerverger, 1989). Ethnolinguistic vitality can be measured either objectively or subjectively. Objective ethnolinguistic vitality (OEV) is determined based on economic/social status, demography, and institutional support (e.g., education and government services and media availability) as measurement references (Giles et al., 1977). Minority languages often have low OEV, as they are low in status, are spoken by relatively few people, and might be supported by only a few government or other institutional services, if at all.

Research has shown that OEV rarely has direct effects on language maintenance and survival. In contrast, psychological variables have often been shown to be strong predictors of language maintenance, including people’s confidence in speaking a language (Freynet & Clément, 2015), their pride in knowing the language (Gibbons & Ramirez, 2004), positive attitudes towards the language (Bourhis & Sachdev, 1984; Choi, 2003; Gibbons & Ramirez, 2004), their feelings of self-determination (Landry et al., 2022), feeling that the language is an integral part of one’s identity (Comanaru & Noels, 2009; Noels, 2005), wanting to use the language to connect with other group members (Belmar et al., 2019; Rosiak, 2023), and how useful they imagine the language will be in their future (Belmar et al., 2019; Rasinger, 2010). In this study, we focus on subjective ethnolinguistic vitality (SEV).

SEV refers to ethnolinguistic vitality as it is perceived by individuals who are part of the group (Bourhis et al., 1981; Bourhis & Sachdev, 1984; Giles et al., 1985; Landry & Bourhis, 1997). SEV can, therefore, subsume some of the psychological variables mentioned earlier, like using language to establish connections with other people in the group. Many studies have shown that high SEV is related to frequent use of the relevant language (Allard & Landry, 1986, 1994; Currie & Hogg, 1994; Dragojevic et al., 2018, 1994; Landry & Allard, 1994; Rasinger, 2010; Yagmur, 2009; Yagmur & Kroon, 2003; Yagmur et al., 1999; cf. Labrie & Clément, 1986). For example, one study showed that minority Bashkir informants in Russia perceived the ethnolinguistic vitality as strong, a factor that likely contributed to the dynamic revitalization efforts of Bashkir (Yagmur & Kroon, 2003). Another study in Romania found that SEV mediated the effect of OEV on the use of Hungarian as a minority language (Dragojevic et al., 2018). This result emphasizes that people’s perceptions of the vitality of their language can be an important factor in their use of a minority language and, therefore, the survival of that language (cf. Hogg & Rigoli, 1996; Yagmur, 2011).

The primary purpose of this study is to test whether language choice makes a difference in SEV. To understand our hypothesis, it will be helpful to better understand how individuals might answer questions about SEV. SEV is often assess through self-report questionnaires, asking participants to estimate various aspects of language use, such as how frequently participants use a particular language (Allard & Landry, 1986, 1994; Currie & Hogg, 1994; Dragojevic et al., 2018, 1994; Landry & Allard, 1994). As with many other estimation tasks, when asked about the SEV of a language, individuals are likely to answer these questions, not be reviewing the total set of experiences that they have had in life, but by constructing an answering based on readily accessible information (Brown et al., 1985; El Haj et al., 2017; Kemp, 1988; Strack & Mussweiler, 2004). Accessible information for SEV could include the number of people that an individual knows who speak the target language, recent interactions with people who speak that language, and/or a mental representation of how much, where, when, and with who the minority language is spoken (Brown et al., 1985; Reisberg, 2005, see also Hayakawa & Keysar, 2018). Previous studies have shown that mental imagery underlies the construction of meaning in the context of understanding language (Sadoski, 2001; Sadoski et al., 1988). Accessing information to answer questions about SEV, therefore, likely depends on mental imagery.

Researchers have long recognized that bilinguals can access mental imagery through both of their languages (Paivio & Desrochers, 1980). However, some studies have shown that the formation of mental imagery can be done more vividly in bilinguals’ first or more proficient language (Hayakawa & Keysar, 2018; Hyusein & Göksun, 2025; Jansson & Dylman, 2021). For example, one study found reduced vividness in a recalled negative autobiographical memory, if the memory was recalled using a foreign language (Jansson & Dylman, 2021). The ability to form vivid mental imagery has been shown to be related to depth of emotional response (Sadoski et al., 1988). Many studies have shown that language choice can affect bilinguals’ thinking and reasoning, particularly in domains affected by emotion (Caldwell-Harris, 2014; Corey et al., 2017; Hu et al., 2022; Purpuri et al., 2024; Zhang et al., 2020). These studies have generally shown increased psychological distance and a reduction in emotional response when using a foreign language than when using a first language. SEV can be a highly emotional domain for at least some speakers (Gibbons & Ramirez, 2004). As proficiency in a language increases, the link between mental imagery and emotional responses within that language strengthens (Krasny & Sadoski, 2008).

In this study, most of our participants were first-language speakers of German who also spoke English (the majority language in this part of Canada). We, therefore, predicted that their SEV of German would be higher when asked in German than in English. We reasoned that their ability to construct accessible information about the SEV in German would be more vivid in German than in English. We further reasoned that speaking German could act as a cue to retrieve German-related memories, even when the participants were not first-language speakers of German, further strengthening our prediction that they would rate German SEV higher when speaking German than when speaking English.

A secondary purpose of this study was to test two predictors of German SEV: the amount of time individuals had lived in the area and German proficiency. We reasoned that residency time would contribute to participants’ cumulative experience with German in the local area. This experience might, therefore, be a positive predictor of German SEV, as more examples of interacting in German could come more readily to mind. We also predicted that German proficiency might be a positive predictor of German SEV for two reasons. First, studies on the effects of language and thinking and reasoning have shown that higher proficiency predicts deeper emotional processing (Purpuri et al., 2024), at times through increased vividness in mental imagery (Krasny & Sadoski, 2008). Second, when participants’ German proficiency is high, they might be more likely to meet and engage with other German speakers. Before turning to the design of this study, we briefly characterize the current state of German in the local area.

German in the local area

This study took place in the city of Kelowna, British Columbia, Canada, located in the interior of the province of British Columbia (i.e., approximately 400 km east from the coast of the Pacific Ocean). Kelowna is a small city, with a population of more than 220,000 according to the 2021 Census (Statistics Canada, 2021). The vast majority of the local population (86%) speaks English as a first language (Statistics Canada, 2021). German is learned as a mother tongue by approximately 2.1% of the population (Statistics Canada, 2021). Most German speakers in the area are either themselves immigrants to Canada or descendants of immigrants, with many moving to the area originally for work (Hodge, 2022). There is a German community centre in the city that traditionally runs an annual Oktoberfest. There is also a German language school in a city about 50 km away, but no German language school in the city proper.

We suspect that the OEV of German is waning in the local area. First-language speakers of German made up less of the local population in 2021 (2.1%; Statistics Canada, 2021) than in 2016 (2.8%; Statistics Canada, 2017). While German classes are offered to adults both at the local university and at the local college, many of the participants in this study told us that the first-language German-speaking population is ageing and many of the children are not learning to speak German as at least one of their first languages.

This study

The primary purpose of this study was to test whether the language choice of the interview affected participants’ SEV of German. We predicted that participants would report higher German SEV when interviewed in German than when interviewed in English. A secondary purpose of this study was to test two predictors of German SEV: (1) length of residency in the local area and (2) German proficiency. We expected that these variables would be positively related to German SEV.

Method

The participants of this study were 30 fluent speakers of German (and English) living in Kelowna. Participants were recruited both on the local university campus (N = 10) or in the community, through an open Reddit page forum of Kelowna, word of mouth, and posters in local restaurants and shops rumoured to cater to German speakers. The average age of the participants was 46.9 years (SD = 20.1; range = 18–90). All spoke English fluently: their self-rated proficiency on a 10-point scale (1 to 10) averaged 9.0 (SD = 1.2; range = 6–10). All had a minimum of high intermediate proficiency in German but their proficiency in German averaging 8.9 (SD = 1.7; range = 5–10). Most of the participants spoke German as a first language, from birth (N = 27). The remaining three learned German as a second language (age of acquisition: 3, 5, and 12 years of age). These three were not outliers on any of the analyses included in this manuscript.

All of our participants were born somewhere other than Kelowna, averaging 23.0% (SD = 10.6%; ranging from 7% to 50%) of their lives in Kelowna. Most participants were born in Germany (N = 22), some elsewhere in Canada (N = 6), and two in German-majority-language countries (Austria and Switzerland). The percentage of a participant’s life spent in the area was used as the operationalization of the residency time in the local area.

Material

To assess the SEV of German, we invited participants to participate in a structured interview, consisting of 10 questions, modelled on questions included in the work by Bourhis et al. (1981). Before we conducted this study, we did not know whether participants could read and write in German, given the paucity of formal education opportunities in German in the area. We, therefore, adapted the questions in the work by Bourhis et al. (1981) to fit the oral interview style. The questionnaire can be found in Appendix 1, in the English version, and in Appendix 2, for the German version. In fact, all our participants were literate in German (as well as in English).

For the first SEV question, participants were asked to estimate the German-speaking population in the local area. For the remaining nine questions, participants were asked to use a Likert-type scale, ranging from 1 to 10. As the first question and the remaining nine are on different scales, we analysed them separately. Moreover, participants’ responses to the estimated percentage of German speakers and the remaining SEV questions were not correlated, either when answering in German, r(28) = .228, p = .23, or in English, r(28) = .195, p = .30.

We refer to the nine SEV questions answered with a Likert-type scale as the SEV ratings. Note that Questions 7 to 10 were reverse scored. To test whether all nine questions were reliably measuring the same underlying construct, we calculated a Cronbach’s alpha for the German answers (α = .77) and for the English answers (α = .71). An α value of .7 is usually considered acceptable. To obtain a single score for the SEV ratings, we averaged participants’ answers to Questions 2 through 10. The SEV ratings could range between 1 and 10, with greater numbers indicating higher German SEV.

Procedure

Interviews were held through the video-conference platform Zoom. The interviewers for the German and the English sessions were native speakers of the target language of the session. The order of the language sessions was counterbalanced across participants, with 16 participants doing the German interview first and 14 the English interview first. To set the language mode of the session, the participants were first invited to talk about how they had learned German (in the target language of the session). Their responses to this question were not recorded. Participants were then asked the 10 SEV questions (see Appendices) and their responses written down by the interviewer.

The two language sessions were scheduled about 6 weeks apart, averaging 44.5 (SD = 23.1) days apart. There was considerable variability as the interviews were scheduled to accommodate participants’ availability. In an initial set of statistical analyses, we tested whether the number of days between interviews affected the results. We found no evidence that the number of days was related to any of the analyses presented here and, therefore, did not consider that variable in the analyses presented in the ‘Results’ section.

Analyses

We tested whether the language of the interview affected German SEV, we compared participants’ estimates of the percentage of German speakers and their SEV ratings with an analysis of variance (ANOVA). In this analysis, we also tested effects of language of the first interview, as previous studies on estimation of shown that a participant’s initial estimation can affect their subsequent judgements (Mussweiler & Strack, 2000).

To test whether residency in the local area and proficiency predicted German SEV, we entered those variables as predictors in a linear regression analysis. In one analysis, the estimated percentage of German speakers was the dependent variable, whereas in another analysis, the SEV ratings were used. In these analyses, we used only the SEV responses from the German interviews, as we will show that they were highly correlated with the SEV responses from the English interviews.

Results

Language of the interview

To see whether the language of the interview affected participants’ German SEV, we analysed participants’ estimates of the percentage of German speakers with a 2 × 2 (language of first interview × interview language) ANOVA, with the interview language as a repeated measure. The results showed no main effect of language of first interview, F(1, 28) = .03, p = .86, η² = .001, no main effect for interview language, F(1, 28) = .04, p = .84, η² < .001, and no interaction effect, F(1, 28) = 2.36, p = .14, η² = .009. The participants estimated the German population in the local area at 16.5% (SD = 15.1%) in the German interviews and at 16.1% (SD = 14.6%) in the English interviews. Their estimates of the percentage of German speakers in the German interviews were highly correlated with their estimates in the English interviews, r(28) = .761, p < .01.

We next analysed the SEV ratings, also with a 2 × 2 (language of first interview × interview language) ANOVA, with the interview language as a repeated measure. The results showed no main effect of language of first interview, F(1, 28) = 2.62, p = .12, η² = .073, no main effect for interview language, F(1, 28) = .46, p = .50, η² = .002, and no interaction effect, F(1, 28) = 1.66, p = .21, η² = .008. The participants rated the SEV of German at an average of 4.0 (SD = 1.5) in German and 3.9 (SD = 1.3) in English. The participants’ SEV ratings in the German interviews were highly correlated with their SEV ratings in the English interviews, r(28) = .716, p < .01.

In sum, these results show no effect of the language of the interview on German SEV. Participants estimated the German-speaking population as around 16% of the population, regardless of the language of the interview. The SEV ratings of German averaged around a moderate 4 on a 10-point scale, regardless of the language of interview. The responses in one language were highly correlated with their responses in the other language.

Predictors of German SEV

Before testing whether residency in the local area and/or German proficiency predicted participants’ German SEV, we examined the simple Pearson correlations between these variables, along with the participants’ age (Table 1). For the participants’ estimates of the percentage of German speakers, age was positively correlated, and length of residency negatively correlated. It is important to keep in mind that age and length of residency were highly correlated to each other, a negative correlation, meaning that the older participants generally had spent less of their lives in the local area. For the SEV ratings, the only variable to reach significance was a positive correlation with German proficiency.

Table 1.

Correlations between variables.

	1	2	3	4
1 % German speakers	–
2 SEV ratings	.228
3 Age	.547**	.144
4 German proficiency	−.017	.411*	−.141
5 Residency in local area	−.382*	−.201	−.884**	.064

% German speakers = the participants’ estimates of the percentage of German speakers in the area; SEV = subjective ethnolinguistic vitality.

p < .01. *p < .05.

We next analysed the participants’ German SEV with residency in the local area, and German proficiency as predictors. The overall regression for the participants’ estimates of German speakers did not reach significance, F(2, 29) = 2.31, p = .12, R² = .146. The Variance Inflation Factor (VIF) for both individual predictors was 1.00, usually considered an acceptable level of collinearity. Table 2 summarizes the betas for the individual predictors. As can be seen in that table, only length of residency in the local area is a significant (and negative) predictor of the participants’ estimates of the percentage of German speakers in the local area. The overall regression for the SEV ratings was significant, F(2, 29) = 3.84, p = .03, R² = .221. The VIF for both individual predictors was 1.00. As can be seen in Table 2, Germany proficiency was a significant positive predictor of the SEV ratings.

Table 2.

Summary of linear regression results with length of residency and German proficiency as predictors of German SEV.

Predictor	Percentage of German speakers		SEV ratings
Predictor	Beta	p	Beta	p
Residency	−.383*	.04	−.229	.19
German proficiency	.007	.97	.426*	.02

%German speakers = the participants’ estimates of the percentage of German speakers in the area; SEV = subjective ethnolinguistic vitality.

p < .05.

In sum, we found that length of residency was a negative predictor of participants’ estimates for the percentage of German speakers in the area. In other words, the shorter time they had resided in the area, the higher their estimate of the local German-speaking population. German proficiency was a positive predictor of SEV ratings of German.

Discussion

The primary purpose of this study was to test whether the language of the interview would affect participants’ SEV of German in an English-majority-language part of Canada. We reasoned that participants would associate speaking German with vivid imagery of using German and, therefore, would rate the German SEV higher when speaking German than when speaking English. Alternatively (or additionally), they may have associated speaking German with instances in which German was used, thereby estimating the SEV of German highly. However, the results showed that there was no effect of the language of the interview on participants’ rating of SEV. Participants estimated the percentage of German speakers in the area at 16% on average, regardless of language. Similarly, they gave moderate SEV ratings (i.e., on other questions about German SEV), with no difference by language.

Why were there no effects of the language of the interview in this study? One possibility is that SEV is not a sufficiently emotional domain to show effects of language choice. Recall that previous studies have found that language choice affects thinking and reasoning, particularly in domains that are highly emotional (Purpuri et al., 2024; Zhang et al., 2020). While this interpretation is theoretically possible, we think it unlikely: some previous studies have shown that talking about SEV can elicit strong emotional reactions (Gibbons & Ramirez, 2004). Moreover, speakers of German in the neighbouring province of Alberta report that learning and speaking German is deeply linked to their self-concept (Noels, 2005) and self-concept is strongly connected with emotions (Noels et al., 2014; see also Gibbons & Ramirez, 2004). A more likely possibility is that researchers do not yet understand when language choice will affect thinking and reasoning. Many studies have shown that language choice affects thinking and reasoning (Hu et al., 2022; Purpuri et al., 2024; Zhang et al., 2020). However, not all studies have found language effects in reasoning, even in emotional domains (Mills & Nicoladis, 2023). The null results here can contribute to a better understanding of when and how language choice affects thinking and reasoning. For example, one study reported that language is more likely to affect reasoning when bilinguals’ languages come from two different language families than when the two languages are from the same language family (like German and English; Circi et al., 2021). Future studies can test if language affects SEV when the languages are from different families.

A secondary purpose of this study was to test whether length of residency and/or German proficiency predicted participants’ German SEV. We reasoned that these measures would tap the cumulative frequency with which participants had had meaningful connections in German so that these would be positive predictors of German SEV. Surprisingly, there were different predictors, depending on the measure of SEV. The participants’ length of residency was a negative predictor of their estimates of the percentage of German speakers in the area. In other words, the longer the participants had lived in the area, the lower their estimate of the German speakers in the area. In interpreting this result, it may be important to keep in mind that there was a negative correlation between length of residency and age. Recall that the age of our participants varied between 18 and 90 years, with six of our participants over the age of 65 years. One possible interpretation of these results is that some of the older adult participants moved to the local area to live close to family members. If so, then the older participants might interact frequently with family members with whom they speak German, contributing to their high estimates of German speakers in the local population. To test that interpretation, future studies can include more in-depth interviews with participants about how often and with whom they speak German.

As for SEV ratings, German proficiency was a positive predictor, meaning that the higher the participants’ German proficiency, the higher their SEV ratings of German. One interpretation of this result is that participants with greater proficiency in German would like to believe that their continued use of and engagement with German is worthwhile. They might, therefore, conceptualize the SEV of German higher to justify their use and engagement to keep up their proficiency. One way to test for this interpretation would be to track SEV of a minority language longitudinally as learners’ proficiency increases. We might see that SEV increases as proficiency in the relevant language increases.

An important caveat in interpreting the results of this study is that we have been assuming that the SEV of German is related to the continued use of German. Many studies have shown that SEV is positively associated with language use (Dragojevic et al., 2018; Landry & Allard, 1994; Rasinger, 2010; Yagmur, 2009). However, not all studies have (Yagmur, 2011). Future studies can include both measures of SEV and of language use in a longitudinal design to test whether SEV is a predictor of language use in the community in question. Future studies can also address other important limitations of this study. Notably, this study included only 30 participants, and the inclusion of a greater number of participants could increase reliability of the results. This consideration is particularly important in light of our interpretation of our results as involving yet other variables (i.e., personal experience with German speakers and motivation to continue using German). Studies that take those variables into account will benefit from the inclusion of a greater number of participants.

In closing, we found that German speakers living in a majority English-speaking part of Canada rated the ethnolinguistic vitality of German as moderate. We found no evidence to support our prediction that the SEV of German would be rated higher when speaking German than when speaking English. We found that the longer the participants had resided in the local area, the lower they estimated the percentage of German speakers in the local area. We speculate that this result could reflect their personal experience of German speakers since arriving in the local area. In contrast, we found that German proficiency predicted other aspects of SEV. We speculate that this finding reflects participants’ motivation to learn and to continue to use a minority language. Future studies can test the relationship between SEV on one hand and motivation to use that language and personal experience in the community on the other.

Footnotes

Appendix 1

Appendix 2

In den folgenden Fragen werden Sie darum gebeten Ihre beste Schätzung zu teilen, wie sehr die deutsche Sprache in der Okanagan Region und in ihrem Leben vertreten ist. Es macht dabei nichts aus, ob Sie die richtige Antwort zu den Fragen kennen. Was wichtig ist, ist Ihr Eindruck der jeweiligen antworten. Jede Frage bezieht sich einschließlich auf die Okanagan Region, es können daher Informationen außerhalb dieser lokalen Grenze ignoriert werden. Behalten sie das im Hintergedanken, während wir durch die Fragen gehen.

Die nächsten Fragen können auf einer Skala von 1 bis 10 beantwortet werden. Eine 1 bedeutet in diesem Fall ‘überhaupt nicht’ und eine 10 bedeutet ‘sehr hoch’.

Die nächsten Fragen können immer noch auf einer Skala von 1 bis 10 beantwortet werden. Dieses Mal, jedoch, steht eine 1 für ‘Ausschließlich’ oder ‘Immer’ und eine 10 steht für ‘Nie’ oder ‘gar nicht’.

Acknowledgements

The authors thank Sophia Statham and Taylor Robinson for their help in data collection. A big thank you to Annika Voeltz for help in recruitment.

ORCID iD

Elena Nicoladis

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received financial support from a Discovery Grant (#2025-04636) to the second author from the Natural Sciences and Engineering Research Council of Canada.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author biographies

Linus Mumesohn designed this study and instigated data collection when he was an undergraduate student at the University of British Columbia. He graduated with B.A. in psychology in 2023.

Elena Nicoladis is a full professor in the psychology department at the University of British Columbia. Her research focuses on language learning in both children and adults.

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