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| Year : 2006
| Volume
: 8 | Issue : 33 | Page
: 154-160 |
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| The development of Weinstein's noise sensitivity scale |
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H Kishikawa1, T Matsui1, I Uchiyama1, M Miyakawa2, K Hiramatsu3, SA Stansfeld4
1 Department of Urban and Environmental Engineering, Kyoto University, Japan
2 Department of Environmental Risk Management, Kibi International University, Japan
3 Graduate School of Asian and African Area Studies, Kyoto University, Japan
4 Centre for Psychiatry, Queen Mary, University of London, United Kingdom
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Many studies have shown the significant correlation between noise annoyance and noise sensitivity identified by Weinstein's noise sensitivity scale (WNS). However, the validity of the scale has not been sufficiently assessed. This study was designed to investigate the validity of each question in WNS and to develop a more valid noise sensitivity measurement scale. A questionnaire study was conducted in a residential area along trunk roads in Kusatsu, Japan, and 301 responses were collected. In this paper, noise sensitivity was defined as the factor that induced individual variability in reactions caused by noise exposure and that is not affected by the noise exposure. The relationship between noise exposure and answers to each question in WNS was investigated by multiple logistic regression analysis, and the influence of response bias on the score of WNS was examined. The results showed that WNS contained some questions that were inappropriately related to noise exposure level and that the score was affected by response bias. The reported correlation between annoyance and the score of WNS could be confounded by noise exposure and response bias. A noise sensitivity measurement scale named WNS-6B was newly developed, excluding the biased questions from the original WNS and applying binary coding to six-response options in order to reduce the response bias. WNS-6B seemed to be more appropriate to assess noise sensitivity than the original scale. Keywords: Annoyance, noise sensitivity, questionnaire survey, response bias, Weinstein′s noise sensitivity scale
How to cite this article:
Kishikawa H, Matsui T, Uchiyama I, Miyakawa M, Hiramatsu K, Stansfeld S A. The development of Weinstein's noise sensitivity scale. Noise Health 2006;8:154-60 |
Many papers have reported the relationship between negative reactions (e.g., annoyance, dissatisfaction, and health effects) and noise exposure level. However, the noise exposure level may not be the primary cause of the reactions, and the individuals may react quite differently even in the same acoustical conditions. Some papers have shown that a variety of personal characteristic factors influence the reactions to noise. [1],[2],[3],[4] Individual noise sensitivity is a factor that explains this difference. [5],[6],[7],[8],[9],[10],[11],[12],[13],[14],[15] Noise sensitivity is often measured as subjective noise sensitivity (SNS) by a questionnaire. If individuals who easily react to noise can be distinguished by SNS, it may be possible to devise effective countermeasures against noise pollution. [16],[17]
Subjective noise sensitivity is usually measured by a self-reported questionnaire both in the field and laboratory studies. Some questionnaires, which consist of multiple questions, have been proposed with rating scale asking for agreement for some statements (e.g., "I find it hard to relax in a place that's noisy"). Weinstein's noise sensitivity scale (WNS) [18],[19] is widely used to assess SNS. The psychometric properties (external validity, reliability, internal consistency, factor structure, and construct validity) of WNS have been reported previously. [18],[20]
Many studies have shown a correlation between the WNS scores and annoyance. However, the WNS score could be confounded by noise exposure, since WNS includes some questions which ask about annoyance at noise exposure. The validity of each question in WNS has not been fully examined. Further investigation will assist in developing a more accurate prediction of reactions to noise. The principle aim of this study is to ascertain the validity of each WNS question and to develop a more appropriate method to estimate SNS based on a cross-sectional field study conducted on residents living along trunk roads.
| Defining Noise Sensitivity | |  |
The concept of noise sensitivity is not defined consistently; however, the following definition is offered: "noise sensitivity refers to the internal state of any individual which increases their degree of reactivity to noise in general." [21] An additional requirement is that SNS should not be affected by noise exposure from the view point of risk assessment. This requirement makes it possible to obtain a closer estimate of reactions of residents who move to a noisy area after living in a quiet area. In this study, noise sensitivity is defined as the factor that induces individual variability in reactions caused by noise exposure and that is not affected by noise exposure.
WNS seems to be appropriate to assess SNS, since no significant correlation was observed between the WNS score and the noise exposure level. [22] However, arguably, there are several problems in WNS.
First, WNS contains some irrelevant questions, for example "sometimes noises get on my nerves and get me irritated" and "I get annoyed when my neighbors are noisy." These questions ask about the respondents' annoyance toward noise. The answers to these questions would be affected by the type of noise exposure. The correlation between the score of each question of WNS and noise exposure level has not been examined.
Secondly, WNS adopts a Likert scale of six-response options ranging from 0 to 5. There is a possibility that the reported relationships between the WNS score and the subjective reactions to noise were confounded by response bias; respondents who answered exaggeratedly to one question were likely to answer exaggeratedly to another question, and a correlation between the two questions would be observed.
| Materials and Methods | | |
Sample
A questionnaire study was carried out in a residential area in Kusatsu, Japan. Two trunk roads with heavy traffic during the day and night passed through the study area (300 × 300 m 2 ). All adult residents living in the study area (n = 468) were asked to complete the questionnaire after signing a consent form for the study.
Noise measurement
Sound levels were measured at 44 points in the area for 24 hours. Day-night average sound levels (Ldn) were calculated, and the noise contour was obtained. The primary noise source was the traffic on the trunk roads.
Questionnaire
Age, gender, and occupation of the householder were to be provided on the front sheet of the questionnaire. Noise annoyance during the entire day was measured using a rating scale of five categories: 1) not at all annoyed, 2) slightly annoyed, 3) annoyed, 4) extremely annoyed, and 5) intolerably annoyed. Various disturbances of daily life by noise exposure (e.g., "sleep disturbance" and "interference with speech communication") were also measured under five categories: 1) never, 2) rarely, 3) sometimes, 4) often, and 5) always. In the questions about disturbances, a dummy question asking about cracking of windows, which is never induced by road traffic noise in the sample area, was included to identify the response bias.
SNS was measured using WNS, which consisted of 10 questions [Table - 1] asking about attitudes toward noise under various situations encountered in everyday life. The degrees of agreement on the statements were asked with six response options ranging from 0 to 5 (from "agree strongly" to "disagree strongly"). The sum of all items (after recoding the 7 items) yielded the respondent's SNS. A higher score denoted a higher sensitivity to noise.
Statistical analysis
According to its definition, SNS should not be related to noise exposure. Multiple logistic regression analysis was applied to assess the correlation between Ldn and the answer to each question of WNS. Questions having an association with Ldn were regarded as inappropriate questions to assess SNS. An alternative scale was developed for the prediction of SNS, excluding the inappropriate questions from WNS.
To reduce the influence of response bias, binary coding was applied to the six-response options (0 or 1 point was given to the options) and a total SNS measurement score was calculated. The availability of binary coding was investigated by the dummy question, which was included to identify the response bias. Fisher's exact test was used for comparing the distribution of the answer to the dummy question between a sensitive group and an insensitive group, which were separated by the SNS measurement scale.
Correlations between Ldn and the total scores of the SNS measurement scales were investigated by multiple logistic regression analysis.
The relationship between noise annoyance in the highest noise-exposed area and SNS was examined by Spearman's rank correlation coefficient. Differences in dose-response curves of annoyance between a sensitive group and an insensitive group were obtained by multiple logistic regression analysis. All statistical analyses were performed with SPSS version 12.0.
| Results and Discussion | | |
Study population
Four hundred and thirteen responses were collected and the response rate was 88.2%. The age of the respondents ranged from 20 to over 80 years. In the analysis, the respondents aged over 70 years were excluded, because the number of these respondents was small. Three hundred and one questionnaires with complete responses for age, gender, and occupation of the householder and WNS questions were included for the analysis.
The individuals were divided into three groups based on their age: 20-39 years (n = 82), 40-59 years (n = 144), and 60-69 years (n = 75). One hundred and forty four respondents (47.8%) were male. Occupation of the householder was used as a measure of socio-economic status and was classified as white-collar (n = 229) and blue-collar (n = 72). Noise exposure level that ranged from 47.9 dB to 69.2 dB in Ldn was divided into three levels: under 55 dB (n = 155), 55-65 dB (n = 77), and over 65 dB (n = 69).
Validity of each question in WNS
Correlations between Ldn and the answer to each question were investigated with multiple logistic regression analysis. The percentage of the residents considered to be sensitive to noise was calculated based on the answers to each question with adjustment for age, gender, and occupation of the householder. Age distribution was adjusted to the WHO world standard population. [23] The ratio of male and female was adjusted to 50:50. The distribution of the occupation was adjusted to that of the whole sample. The results from the answers to Question 1 and 5 are shown in [Figure - 1].
Question 5 asks about the annoyance of respondents and the answer was positively correlated with Ldn. Question 1 refers to the sound from a stereo system and the score was negatively correlated with Ldn. The percentages of the residents considered to be sensitive to noise from the answer to Question 1 decreased and the percentage from the answer to Question 5 increased, because the sound from stereo system was masked by the road traffic noise. It seems to be inappropriate to include these questions in the SNS measurement scale, because the answers to Questions 1 and 5 depended entirely on the type of noise exposure. Questions related to noise exposure might confound the correlation between the SNS score and the reactions to noise.
Question 3 asks about annoyance caused by neighborhood noise. The answer to this question depended on the type of dwellings, such as detached houses, terrace houses, or apartments. [24] The question that asked about the respondent's annoyance should not be included in the SNS measurement scale, since the score could be confounded by the noise exposure. Question 6 asked about the respondents' sensitivity toward music. While measuring the sensitivity to noise, the sensitivity toward music may differ from that toward traffic noise. Therefore, Question 6 was regarded as inappropriate to assess SNS toward traffic noise. However, further investigation is necessary on the differences between sensitivity toward traffic noise and sensitivity toward music. On excluding these four questions (Question 1, 3, 5, and 6), a new SNS measurement scale, which consisted of the other six items, was developed. This modified scale, named WNS-6, seems to be more appropriate to estimate SNS than the original WNS.
Coding of the response options in WNS
The response option for WNS was converted into a dichotomous variable in order to reduce the response bias (0 or 1 point was given to each question based on whether the respondents agreed or disagreed with the questions). WNS-6B was developed as an SNS measurement scale and binary coding was applied to WNS-6. The distribution of the score on WNS and WNS-6B is shown in [Figure - 2],[Figure - 3], respectively. A strong relationship was observed between the two scores (Spearman's r = 0.837, p < 0.0001).
Availability of binary coding was investigated using the answers to the dummy question. The response options to the dummy question were separated into negative answers "never" and "rarely" and positive answers "sometimes," "often," and "always." Respondents who were easily affected by response bias had a tendency to report a positive answer to the dummy question. The percentages of respondents who gave a positive answer to the dummy question were compared between a sensitive group and an insensitive group, which were separated by the score of WNS-6 and WNS-6B with medians as cut-off points (17/18 for WNS-6 and 4/5 for WNS-6B). [Table - 2] shows the results of Fisher's exact test on the difference of the percentages. The number of valid answers was 299, since two respondents did not provide a valid answer to the dummy question. After applying binary coding, no significant difference was found between the sensitive group and the insensitive group, while a significant difference (p = 0.008) was observed in WNS-6. It appears that the influence of response bias is reduced by binary coding.
Relationship of the total score to noise exposure
Many previous studies have reported that no significant correlation was observed between SNS and Ldn. [22] In this study, relationships of the score of WNS and WNS-6B to Ldn were investigated by multiple logistic regression analysis. The scores were converted into dichotomous variables with medians as cut-off points. The results were adjusted for age, gender, and occupation of the householder. The score of the original WNS had no particular relationship with noise exposure level, although answers to Questions 1 and 5 in WNS related to the noise exposure. This result can be interpreted as follows [Figure - 1]: the increase in the percentage on Question 5 canceled out the decrease in the percentage on Question 1, and as a result, no relationship between the total score of WNS and the noise exposure level was found.
The score of WNS-6B had no particular relationship with noise exposure level. Since inappropriate questions are excluded in WNS-6B and the effect of the response bias was reduced by binary coding, WNS-6B seems to be a more appropriate SNS measurement scale than either WNS-6 or the original WNS.
In addition, we found a result which supported the advantages of WNS-6B. A significant difference in mental health effects caused by noise exposure was detected between the sensitive group and the insensitive group separated by WNS-6B, while no significant difference was observed in the case of WNS and WNS-6. [25],[26] WNS-6B has a great advantage for predicting noise effects on mental health as well as annoyance. Moreover, the existing data containing the original WNS can be reanalyzed based on WNS-6B, because WNS-6B was developed only by excluding four questions from WNS and by applying binary coding to the response options.
The correlations of the scores of the SNS measurement scales with annoyance in the highest noise-exposed area (Ldn > 65 dB) are shown in [Table - 3]. In all the scales, a significant relationship ( p < 0.0001) was observed and there was no significant difference between the scales. It has been considered that binary coding application results in information loss. However, WNS-6B has a similar correlation coefficient to other scales.
Noise sensitivity and dose-response curve of annoyance
Respondents in the highest noise-exposed area were separated into a sensitive group and an insensitive group by the score of WNS and WNS-6B with medians as cut-off points. The cumulated percentages of answers about noise annoyance during the entire day in each SNS group identified by WNS and WNS-6B are shown in [Figure - 4],[Figure - 5] respectively. Even in the same noise-exposed area, the residents reacted to noise quite differently based on their SNS. This result suggests that SNS does predict an individual's reactions to noise.
Dose-response curves of annoyance were obtained for the sensitive group and the insensitive group by multiple logistic regression analysis. The choice of "extremely/intolerably annoyed" was considered as "highly annoyed (HA)" and the answer was converted into a dichotomous variable. SNS, Ldn, age, gender, and occupation of the householder and the interaction between age and gender were included as independent variables. Age distribution was adjusted to the WHO world standard population. [23] The ratio of male and female was adjusted to 50:50. The distribution of occupation was adjusted to that of the whole sample. The adjusted dose-response curves of %HA are shown in [Figure - 6]. There are remarkable differences between the sensitive group and the insensitive group. In the highest noise-exposed area, 55% of the sensitive residents were highly annoyed, while only 13% of the insensitive residents were highly annoyed. Dose-response curves varied greatly according to their SNS.
| Conclusions | | |
This field study in a residential area along trunk roads in Kusatsu, Japan revealed that the answers to two questions in WNS correlated with noise exposure. Two other questions were also regarded as inappropriate questions to assess SNS due to their intended meanings. These four inappropriate questions were excluded from WNS.
It was found that the answers to questions in WNS were confounded by response bias. To reduce the effect of the response bias, binary coding was applied to the answers and a new SNS measurement scale named WNS-6B was developed.
WNS-6B is considered a more appropriate SNS measurement scale, because the response to each question in this scale as well as the total score is independent of noise exposure and response bias is reduced by binary coding.
Noise annoyance of residents differed greatly in the highest noise-exposed area. The respondents were separated into a sensitive group and an insensitive group based on their WNS-6B score with medians as cut-off points. The dose-response curves of annoyance in the two groups were obtained. The dose-response curves differed dramatically in the two groups.
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Correspondence Address:
H Kishikawa
Department of Urban and Environmental Engineering, Kyoto University, Yoshida-honmachi Sakyou-ku, Kyoto-shi Kyoto-ku, 606-8501
Japan
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/1463-1741.34703
[Figure - 1], [Figure - 2], [Figure - 3], [Figure - 4], [Figure - 5], [Figure - 6]
[Table - 1], [Table - 2], [Table - 3] |
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