Benfield, J.A., Nurse, G.A., Jakubowski, R., Gibson, A., Taff, D ...

454430EABXXX10.1177/00139165124544 

30Environment and BehaviorBenfield et al. 

© 2012 SAGE Publications 

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Testing Noise in the 

Field: A Brief Measure of 

Individual Noise Sensitivity 

1 Pennsylvania State University–Abington, USA 

2 University of Arizona, Tucson, USA 

3 JVA Consulting, Denver, CO, USA 

4 Utah State University, Logan, USA 

5 Colorado State University, Fort Collins, USA 

Environment and Behavior 

XX(X) 1 –20 

© 2012 SAGE Publications 

Reprints and permission: 

sagepub.com/journalsPermissions.nav 

DOI: 10.1177/0013916512454430 

http://eab.sagepub.com 

Jacob A. Benfield 1 , Gretchen A. Nurse 2 , Robert 

Jakubowski 3 , Adam W. Gibson 4 , B. Derrick Taff 5 , 

Peter Newman 5 , and Paul A. Bell 5 

Abstract 

Noise, or unwanted sound, exposure has been shown to have a wide range 

of negative physical and psychological effects. Although situational context, 

sound characteristics, and individual expectation affect the experience of 

noise and its related outcomes, the personality trait of noise sensitivity also 

plays a critical role in assessing noise impacts. As the most widely used 21item 

Noise Sensitivity Scale measure of sensitivity is often too long to 

administer in time-sensitive field settings, the authors conducted five studies 

to create and validate a shortened, field friendly version of the original, 

longer measure of noise sensitivity. The resulting five-item measure of noise 

sensitivity was shown to be internally consistent, temporally stable, highly 

correlated with the original measure, and predictive of noise-related outcomes 

such as attitudes toward specific noise, acceptability ratings of noise 

events, and motivations for visiting quiet locations. The applied value of the 

scale and implications for facilitating future research are discussed. 

Corresponding Author: 

Jacob A. Benfield, Division of Social Sciences, Pennsylvania State University–Abington, Abington, 

PA 19001, USA 

Email: jab908@psu.edu

2 Environment and BehaviorXX(X) 

Keywords 

noise, personality, environmental stressor, noise sensitivity, recreation 

Unwanted sound, or noise, is considered one of the most ubiquitous of ambient 

environmental stressors. Noise generated by transportation, workplace 

technology, and other humans has been shown to be detrimental in a number 

of ways. For example, the presence of unwanted sounds has been related to 

higher stress levels (e.g., Ising & Kruppa, 2004), increased cognitive errors 

(e.g., Benfield, Bell, Troup, & Soderstrom, 2010b; Stansfeld et al., 2005), 

reduced health and well-being (e.g., Stansfeld & Matheson, 2003), disturbances 

in sleep cycles (e.g., Muzet, 2007), negative changes in affect (e.g., 

Benfield, Bell, Troup, & Soderstrom, 2010a; Mace, Bell, & Loomis, 1999), 

and lower amounts of prosocial behaviors (e.g., Moser, 1988). Increasingly, 

scientists are recognizing the physical health consequences of noise (e.g., 

Babisch, 2006; Fritschi, Brown, Kim, Schwela, & Kephalopoulos, 2011) and 

consequently adding “harmful” to its definition (e.g., “unwanted or harmful 

sound”). In doing so, they embrace beliefs that noise can be measured not just 

as a subjective annoyance but as an objective harm quantifiable by its health 

consequences across populations or by a “reasonable person” standard of 

assessment. In short, exposure to noise is a problem physiologically and psychosocially, 

so understanding the components of noise and psychological perceptions 

of noise as they relate to these outcomes is important. 

Different theoretical perspectives have been used to explain the process 

underlying these negative effects, but most rely heavily on the interaction 

between the physical sound stimuli and the perceiver of the sound. For 

instance, arousal and stress perspectives posit that noxious sound increases 

negative affect, generalized anxiety, and frustration in the listener, which in 

turn causes stress and/or arousal with subsequent changes in blood pressure, 

sleep patterns, immune functioning, and hormone levels (Cohen, Evans, 

Stokols, & Krantz, 1986). In these models, the individual’s reaction to the 

sound (e.g., annoyed, frustrated) drives the negative consequence rather 

than the sound itself. Similarly, sounds that are perceived as unnecessary or 

detrimental to something the listener values are more annoying and psychologically 

damaging. The same is true of noises that are considered by the 

perceiver to be hazardous to his or her health (Green & Fidell, 1991). Noise 

generated by someone seemingly unconcerned with the listener’s well-being 

is also evaluated negatively (Miedema & Vos, 1999). In short, people are 

generally motivated to avoid noise, but individual differences in listeners

Benfield et al. 3 

and their perceptions of the stimuli contribute heavily to noise-induced outcomes 

and experiential reports of noisy environments. 

Examples of these individual differences in sound and noise perception 

are numerous. For instance, individuals trained in music or singing respond 

differently when compared with novices (Williams, 2005). Similarly, military 

combat veterans can be hypersensitive to the holiday sights and sounds 

of New Year’s eve (e.g., flashing lights, loud explosions) because similar 

stimuli have become associated with mortal danger (Chemtob, Roitblat, 

Hamada, Carlson, & Twentyman, 1988). More generally, the rumbling of a 

thunderstorm can be an exciting and pleasant experience to some but terrifying 

or depressing to another. Likewise, a parent trying to lull a newborn to 

sleep or a night shift worker trying to rest during the day perceives bird 

chirps, garbage trucks, and telephone rings differently from those who are 

currently less motivated for quiet conditions. 

Given the definition of noise (i.e., unwanted or harmful sound), an emphasis 

on the perceiver and his or her individual perceptions or traits makes sense 

if researchers want to truly differentiate between effects caused by the sound, 

effects caused by the listener, and effects that depend on the interaction 

between the two. Consequently, individual differences are often tested and 

controlled for in sound-related research to partial out which effects are universal 

and which are more person dependent. 

Individual Differences in Noise Sensitivity 

For example, the Big Five personality characteristics of extraversion and 

neuroticism have been shown to affect participant perceptions of and performance 

during exposure to noise. Extraversion—the tendency to be gregarious, 

be assertive, and seek out social stimulation—has been shown to be 

related to preference for greater sound intensity (Geen, 1984) and reduced 

distraction by extraneous noise (Belojevic, Slepcevic, & Jakovljevic, 2001; 

Daoussis & McKelvie, 1986; Furnham, Gunter, & Peterson, 1994). Likewise, 

neuroticism—the tendency to be anxious, emotionally negative, and less 

emotionally stable—has also been shown to be positively correlated with 

annoyance during noise exposure (Öhrström, Bjorkman, & Rylander, 1988) 

and with noise-induced sleep disturbances (Jakovljevic, Belojevic, Paunovic, 

& Stojanov, 2006). Neurotics when compared with more emotionally stable 

individuals also perform worse on semantic retrieval tasks (von Wright & 

Vauras, 1980) and prose memorization tasks (Nurmi & von Wright, 1983) 

under noisy conditions.


Aside from the previously mentioned personality variables, one individual 

difference has been particularly salient in research on noise-related 

outcomes. Noise sensitivity refers to an individual aversion toward and/or 

reactivity to noise and noisy environments (Job, 1999; Weinstein, 1978). 

Research on individual noise sensitivity has shown that it is conceptually 

distinct from extraversion or neuroticism, uncorrelated with noise exposure, 

and largely independent of auditory acuity or function (Ellermeier, 

Eigenstetter, & Zimmer, 2001; Job, 1988; Miedema & Vos, 2003; Stansfeld, 

Clark, Turpion, Jenkins, & Tarnopolsky, 1985). Higher noise sensitivity is 

related to higher skin conductance responses in the presence of noise 

(Stansfeld & Shine, 1993), greater reported annoyance (Belojevic & 

Jakovljevic, 2001), elevated heart rate (Abel, 1990), sleep loss (Jakovljevic et 

al., 2006), psychiatric disturbance (Stansfeld, Clark, Jenkins, & Tarnopolsky, 

1985), and slower habituation in the presence of noise (Stansfeld & Shine, 

1993). As made obvious by those findings, the inclusion of some form of 

measurement for noise sensitivity is paramount to most noise- or soundrelated 

studies. Although this is accomplished using several different strategies 

ranging from single-item indicators to standardized scales to noise 

complaint frequency, some measures have been favored more than others. 

The Weinstein (1978) Noise Sensitivity Scale (NSS), developed with a 

college student sample, is considered the most well-researched and widely 

used formal measure of noise sensitivity (Job, 1999; Miedema & Vos, 2003; 

Zimmer & Ellermeier, 1999). It has been shown to be psychometrically valid 

(Ekehammar & Dornic, 1990; Zimmer & Ellermeier, 1999) as well as temporally 

stable (Stansfeld, 1988; Zimmer & Ellermeier, 1999), and the majority 

of the research cited previously used this 21-item instrument. 

The Current Studies 

As stated previously, noise is an ever-present environmental stressor linked 

to a host of adverse effects that manifest themselves to varying degrees 

depending on individual differences in the perceiver of the sound. As a 

result, characteristics of the listener need to be included in sound research 

to allow for clear distinctions to be made between effects caused by the 

stimuli and effects facilitated by the individual. Because noise sensitivity 

as measured by Weinstein’s (1978) NSS has been shown to be a critical 

individual difference factor in the perception of noise and the evaluation of 

noise-related outcomes, it should probably be measured in most humanbased 

noise research projects. Exploring noise effects in applied research 

contexts such as museums and national parks is becoming more common 

(e.g., Jakubowski, 2011; Pilcher, Newman, & Manning, 2009). As the


compliance rate for agreeing to intercept interviews with participants in 

these applied settings declines with the length of questionnaires employed 

(Loomis, 1987), the 21-item version of the NSS can make it less likely that 

potential participants will complete any questionnaire. Accordingly, we 

report herein the development of a short, field-useful version of the NSS 

that is psychometrically reliable, valid, and representative of the original. 

This was accomplished across five studies. 

Study 1 

Using exploratory factor analysis on three separate samples (college students, 

adults), the first study was designed to isolate five to seven items that 

best represented the construct of noise sensitivity. Those items would serve 

as the NSS short form (NSS-SF) used for later studies on reliability, validity, 

and confirmatory factor structure. 

Method 

Participants. Three separate samples of participants were recruited through 

either laboratory research projects (two student samples; n = 390 and 308) or 

an online survey community (adult sample; n = 202). Demographics for each 

sample are given in Table 1. 

Materials and Measures. Participants were given the Weinstein (1978) 

NSS that consists of 21 items measured along a 6-point scale ranging from 

“disagree strongly” to “agree strongly.” This measure has been shown to 

have strong internal consistency (α = .86) and to be psychometrically 

robust (Ekehammar & Dornic, 1990; Weinstein, 1978; Zimmer & Ellermeier, 

1999). 

In addition, participants in Sample 3 (adults) completed the Big Five 

Inventory–54 item (BFI-54; John & Srivastava, 1999) during the survey. The 

BFI-54 requires participants to rate agreement with personally descriptive 

items using a 5-point scale and provides subscale scores for extraversion (9 

items; α = .84), agreeableness (9 items; α = .82), conscientiousness (9 items; 

α = .85), emotional stability (9 items; α = .89), and openness (18 items; α = .88). 

This measure was included based on previous literature showing a relationship 

between noise sensitivity, and the extraversion and neuroticism factors 

of the Big Five (Dornic & Ekehammar, 1990). 

Procedure. Participants completed the questionnaire(s) as either part of a 

larger laboratory research study involving noise (student samples) or as part 

of an online survey created exclusively for this project (adult sample). All 

three samples also completed a brief demographic questionnaire.


Table 1. Sample Demographics Across the Five Studies 

Results and Discussion 

Age 

Population 

sampled n M (SD) Sex 

Study 1: Sample 1 College students 390 19.21 (2.17) 200 female, 190 male 

Study 1: Sample 2 College students 308 19.46 (2.69) 210 female, 98 male 

Study 1: Sample 3 Adults 202 36.11 (13.65) 138 female, 64 male 

Study 2 College students 438 19.47 (2.26) 247 female, 191 male 

Study 3 College students 49 21.00 (2.66) 29 female, 20 male 

Study 4 National park 

visitors 

106 47.09 (15.76) 56 female, 50 male 

Study 5 National park 

visitors 

165 48.15 (14.51) 74 female, 91 male 

Three separate exploratory factor analyses were run on the different samples 

to determine which items on the NSS consistently displayed the highest factor 

loadings. Such consistency was interpreted to be an indication that those 

items were regularly demonstrating a strong connection with the larger construct 

being measured across multiple samples. Results showed that 5 items 

consistently had the highest factor loadings (.467-.788) across all three 

samples. Internal consistency among those items was shown to be acceptable, 

especially given the small number of items (α = .73, .78, and .82, 

respectively). Moreover, the 5-item scales were highly correlated (r = .86-.91) 

with the 21-item original NSS. As a result, those 5 items were chosen to act 

as the initial base for the NSS-SF. Table 2 displays the 5 items retained for 

the short form along with factor loadings, reliability information, and correlations 

with the NSS across samples. 

After determining the initial items for the NSS-SF, correlations were run 

between the NSS-SF score and the Big Five personality traits measured in the 

adult sample. As described elsewhere, previous research has shown that extraversion 

and neuroticism (the opposite end of emotional stability) had contrasting 

relationships to noise annoyance perception and outcomes. Extraversion 

was expected to negatively correlate with NSS-SF scores; emotional stability 

(the opposite of neuroticism) was also expected to negatively correlate with 

NSS-SF. As seen in Table 3, both hypothesized relationships were demonstrated, 

with extraversion and neuroticism significantly correlating with NSS-SF scores 

in the hypothesized direction (r = −.15 and −.29, respectively).


Table 2. Item Factor Loadings, Internal Consistency, and Correlation With Original 

NSS for Study 1, 2, 4, and 5 Samples 

Sample 1 

(n = 390) 

Sample 2 

(n = 308) 

Sample 3 

(n = 202) 

Study 2 

(n = 438) 

Study 4 

(n = 106) 

Study 5 

(n = 165) 

NSS 21 

“I am sensitive to noise.” 

NSS 18 

.674 .728 .788 .701 .829 .813 

“I find it hard to relax in 

a place that’s noisy.” 

NSS 19 

.628 .625 .698 .564 .721 .754 

“I get mad at people who 

make noise that keeps 

me from falling asleep 

or getting work done.” 

NSS 7 

.615 .629 .618 .616 .708 .732 

“I get annoyed when my 

neighbors are noisy.” 

NSS 8 

.556 .632 .631 .622 .782 .721 

“I get used to most 

noises without much 

.467 .653 .729 .584 .668 .693 

difficulty.” a 

Internal consistency (α) .73 .78 .82 .75 .80 .80 

Correlation with 21-item 

NSS (r) 

.86**** .91**** .91**** — — — 

Note: NSS = Noise Sensitivity Scale. 

a 

NSS Item 8 is reverse scored. 

****p < .000. 

With five items selected to represent the NSS-SF and internal item consistency 

being acceptable, a solid foundation to build on was established in 

Study 1. In addition, initial tests of convergent validity showed relationships 

that were consistent in terms of direction and magnitude to previous research. 

However, several additional pieces of information related to structure, overall 

stability, and validity were necessary to demonstrate that the scale was 

acceptable for use as a research instrument. 

Study 2 

The original NSS represented a single unified factor, and the five items used 

in the short form should perform similarly. Before attempting to further 

validate the NSS-SF, a confirmation of its factor structure was carried out 

using a new sample and confirmatory factor analysis procedures.


Table 3. Correlations Between NSS-SF and Noise-Related Outcomes (Across 

Studies) 

NSS-SF score 

Study Variable r Significance 

Study 1; Sample 3 

(n = 202) 

Big Five personality 

Extraversion −.148** .035 

Agreeableness −.198*** .005 

Conscientiousness −.014 .845 

Emotional stability −.287*** .000 

Openness −.070 .325 

Study 4 (n = 106) Acceptability of hearing aircraft every . . . 

5 min −.283*** .004 

15 min −.236** .018 

30 min −.215** .030 

60 min −.173* .080 

Degree to which _____ noises were a problem during your 

visit 

Automotive .246** .011 

Park operation .136 .164 

Visitors talking .314*** .001 

Aircraft overflight .174* .076 

Visitor electronic .263*** .007 

Study 5 (n = 165) Importance of reasons for visiting Tuolumne Meadows 

Being with family/friends .052 .519 

Getting away from Yosemite 

crowds 

.263*** .001 

Getting exercise .062 .438 

Hearing nature sounds .188** .018 

Enjoying peace and quiet .253*** .001 

Experiencing solitude .219*** .005 

Viewing the night sky without 

pollution 

−.136 .095 

Learning Native American culture/ 

history 

.090 .259 

Note: NSS-SF = Noise Sensitivity Scale short form. 

*p < .08. **p < .05. ***p < .01.


Method 

Participants. Some 438 participants were recruited through a university 

research pool to complete the NSS-SF. Sample demographics are shown in 

Table 1. 

Materials, Measures, and Procedure. Participants were recruited for a 

Mass Testing Survey to partially fulfill a course research requirement. Participants 

completed the five-item NSS-SF along with a brief demographic 

questionnaire and several other measures unrelated to this report. 


Confirmatory factor analysis was conducted on the five-item NSS-SF to 

ensure that it conformed to the unified structure demonstrated in the original 

NSS within acceptable boundaries related to model fit. That was indeed the 

case with fit indices being acceptable for the anticipated single-factor structure 

(comparative fit index [CFI] = .97; root mean square error approximation 

[RMSEA] = .078). Factor loadings and internal consistency (α) were 

similar to Study 1 findings and are reported in Table 2. 

With a consistent factor structure and replicated evidence of internal consistency, 

the five-item NSS-SF appeared to be operating in much the same way as 

the original NSS. However, additional tests of validation were necessary to 

ensure that the measure performed similarly to its longer counterpart. 

Study 3 

The first two studies showed that the NSS-SF has the same factor structure 

of the original scale and is internally consistent. One of the remaining questions 

regarding temporal consistency was tested in Study 3 using a student 

sample and a 5-week (35-day) delay between scale administrations. 

Method 

Participants. Undergraduate students (n = 49) completed the NSS-SF for 

course research credit. Sample demographics are listed in Table 1. 

Measurement and Procedure. Participants completed the NSS-SF on 

two separate occasions spaced 5 weeks (35 days) apart. Participants were 

debriefed following each measurement occasion.



A correlation between the two separate NSS-SF measurement occasions as 

a test of temporal stability (i.e., test–retest reliability) was conducted. 

Results of that analysis showed that individual NSS-SF scores were internally 

consistent (α = .79 and .81 at Times 1 and 2, respectively), temporally 

stable (r = .83), and in line with the previously reported stability for the 

original 21-item NSS across a similar time period (r = .87 across 4 weeks; 

Zimmer & Ellermeier, 1999). 

Given these findings, in conjunction with the first two studies, it was 

concluded that the NSS-SF is psychometrically similar to the longer, wellestablished 

NSS. The NSS-SF had an identical factor structure as well as 

acceptable internal and temporal consistency; it was also highly correlated 

with the original scale and converged appropriately with other personality 

constructs (i.e., extraversion and neuroticism). However, given the larger 

purpose of the research project (i.e., to develop a short version of the NSS 

to facilitate the inclusion of individual differences into noise research), 

additional tests of the NSS-SF in field settings were deemed necessary. 

Study 4 

Visitors to national parks are often surveyed by researchers for a wide range 

of purposes including satisfaction with or acceptability of conditions. Noise 

levels in parks are a source of conflict (e.g., Bell, Mace, & Benfield, 2009- 

2010) and are under ongoing study as a result of legislative mandates (e.g., 

National Park Air Tour Management Act of 2000). Consequently, the 

NSS-SF should be of potential usefulness to such research. Study 4 used the 

NSS-SF in a noise-related study taking place in Rocky Mountain National 

Park located near Estes Park, Colorado. 

Method 

Participants. Sample demographics are reported in Table 1. 

Materials and Measures. Participants completed the NSS-SF, a brief 

demographic questionnaire, and a park visitor experience questionnaire. This 

questionnaire contained several items related directly to noise, including one 

set of item asking participants to rate along a 5-point range the degree to 

which different sound sources (i.e., auto traffic, park operations/construction, 

visitors talking, aircraft overflights, and personal electronic devices) were


problematic to the visit. Participants also rated the acceptability of hearing 

aircraft overflights during their visit based on varying amounts of temporal 

frequency (i.e., every 5, 15, 30, or 60 min). 

Procedure. Participants were recruited from the Bear Lake trailhead in Colorado’s 

Rocky Mountain National Park by selecting every nth person (dependent 

on daily visitor volume). These participants were asked to complete the 

survey packet containing the demographics, NSS-SF, and park visitor questionnaire. 

After completing the study, participants were debriefed regarding the 

purposes of the study. All surveying took place between August 9 and August 

13, 2010; 67% of potential participants agreed to take part in the research. 


Correlations were run between the NSS-SF score, and the ratings for the 

problematic noises and the aircraft noise frequency acceptability. Those relationships 

are given in Table 3. Generally, higher NSS-SF scores related to 

lower ratings of frequent aircraft overflight acceptability and higher ratings 

of most sounds as being problematic to visitation. 

However, these findings are not necessarily based on exposure to actual 

noise. Visitors to the national park reported certain sounds as being less 

acceptable or more problematic, but this could reflect attitudinal preferences 

more than the individual response to the actual problematic presence of those 

sounds. Nonetheless, the theoretically consistent relationship between noise 

sensitivity and individual attitudes toward different sounds in context does 

provide evidence that the NSS-SF performs in a manner consistent with theory 

and its longer counterpart. 

Study 5 

A fifth and final study was run to test the relationship NSS-SF score has with 

responses to actual sound exposure in a different sample of national park 

visitors. It was hoped that surveying park visitor responses to a controlled set 

of auditory stimuli while including the NSS-SF would provide sufficient 

evidence when combined with the other studies to support the scale’s use in 

future noise research. 

Method 

Participants. Sample demographics are reported in Table 1.


Materials and Measures 

Sound clips. A set of five audio clips was created using natural and aircraft 

sounds recorded within the national parks system. Each sound clip was 

equivalent in length (48 s) and depicted a natural environment during an air 

tour overflight. The clips differed in the level of noise energy created by the 

overflight, with each clip representing a gain of 7 dBA based on Leq (ranging 

from 36 to 64 dBA). Leq is a measure of sound pressure level that is averaged 

over a time period to provide a summary of the general loudness of a particular 

location. In the context of this research, an Leq of 36 dB represents an 

environment that is on average quieter than an office when no one is talking. 

An Leq of 64 dB represents an environment that is similar to a room where 

many people are talking at once or a lecturer is projecting his or her voice in 

a large lecture hall. 

Acceptability scale and visitor survey. Participants were asked to rate each 

audio clip based on the acceptability of the event occurring during a visit to 

the park. These acceptability ratings were measured using a single item ranging 

from −4 = very unacceptable to +4 = very acceptable that has been used 

extensively within the acceptability literature (see Vaske & Shelby, 2008, for 

a review). Participants also completed a general park visitation survey that 

contained the NSS-SF and demographic questions as well as a set of items 

concerning the importance of different visitation motives, such as “enjoying 

time with family,” “experiencing peace and quiet,” or “learning about Native 

American culture and history.” Those items were rated using a 0 to 4 scale 

(not at all important to extremely important). 

Procedure. Participants were recruited from the Tuolumne Meadows 

region of California’s Yosemite National Park between July 10, 2010 and 

August 6, 2010. Those agreeing to participate were given the visitor survey 

containing the NSS-SF, demographics, and the importance of visitation 

motives questions. They were then given an MP3 player to listen to the five 

audio clips and the acceptability rating sheet. Three separate orders of the 

audio clips were used to prevent order effects; participants were randomly 

assigned to the different orders. After completing the entire visitor packet 

and the audio recording rating task, participants were debriefed and thanked 

for their time. 


Correlations were run between the NSS-SF and the responses to the visitor 

motives questions. As shown in Table 3, NSS-SF score positively correlated 

with the importance of sound-related motives, such as getting away from the


crowds (r = .26, p = .001), experiencing solitude (r = .22, p = .005), and 

hearing the sounds of nature (r = .19, p = .019). NSS-SF score was not correlated 

with other motives such as getting exercise, viewing the night sky 

without light pollution, or learning about Native American culture/history. 

To test the relationship between NSS-SF score and sound clip acceptability 

ratings, five multiple regressions were run with demographic variables 

(age, sex) and NSS-SF score used as predictors of each clip’s acceptability 

rating. As shown in Table 4, NSS-SF score significantly related to decreased 

acceptability ratings of sound clips, but only at the higher volume conditions 

(i.e., 57 and 64 dBA). The general trend of the data suggests that as clip 

volume increased, the importance of NSS-SF also increased. As the final 

two audio clips were also the only clips that had acceptability ratings in the 

“unacceptable” range (i.e., −4 to −1), the results suggest that the predictive 

value of the NSS-SF is particularly meaningful when unwanted sounds (the 

definition of noise) are in the unacceptable range, but not when the sounds 

are acceptable (i.e., are not considered noise). Such findings support the 

construct validity of the NSS-SF. 

When taken together, the results of this study provide additional evidence 

of the NSS-SF’s validity and usability in research contexts. Results 

showed that the scale relates to more positive visitor attitudes toward 

sound-specific visitation motives but not sound-unrelated motives. In addition, 

this study showed that the NSS-SF is predictive for noise conditions 

(i.e., for unwanted/unacceptable sounds), but not for acceptable sounds 

(i.e., nonnoise) in a field setting, which is what one would want for a noise 

sensitivity measure. 

General Discussion 

Across five studies and seven different samples that included students, 

adults, and national park visitors, this project created a five-item version of 

one of the most widely used measures of noise sensitivity—Weinstein’s 

NSS. This field friendly version of the NSS is structurally identical to the 

original with comparable levels of internal consistency and temporal stability. 

It is highly correlated with the original NSS as well as being associated 

with constructs such as extraversion and neuroticism. It also relates to a host 

of noise-related attitudes and outcomes such as more negative attitudes 

toward human-caused noise as well as more positive attitudes toward soundrelated 

motives for visiting national parks. In short, the NSS-SF represents 

the original measure remarkably well and improves on its research value by 

being more practical in item-limited, real-world applications.

14 

Table 4. Regression Results for NSS-SF Predicting Audio Clip Acceptability (Study 5; n = 165) 

Gender (0 = female, 

1 = male) Age NSS-SF 

Constant B (SE) B (SE) B (SE) 

M (SD) Model summary B (SE) β β β 

2.68 (1.88) R 2 = .016 3.22 (.58)** .270 (.21) −.01 (.01) .00 (.02) 

Audio Clip 1 (Leq = 36 

dBA) 

F(3, 315) = 1.657, p = .176 .07 −.10 −.01 

1.31 (2.46) R 2 = .007 2.42 (.76)** −.04 (.27) .00 (.01) −.04 (.03) 


dBA) 

F(3, 316) = 0.688, p = .560 −.01 −.01 −.08 

−0.16 (2.62) R 2 = .008 0.67 (.82) .30 (.30) .00 (.01) −.04 (.03) 


dBA) 

F(3, 316) = 0.882, p = .451 .06 −.02 −.06 

−0.97 (2.46) R 2 = .023 0.60 (.76) .34 (.28) .00 (.01) −.07 (.03) 


dBA) 

F(3, 31) = 2.482, p = .061 .07 −.03 −.12* 

−1.29 (2.57) R 2 = .033 0.65 (.80) .43 (.29) −.01 (.01) −.07 (.03) 


dBA) 

F(3, 316) = 3.566, p = .015 .08 −.08 −.12* 

Note: NSS-SF = Noise Sensitivity Scale short form. 

*p < .05. **p < .01.


In a research context, a shortened version with such fidelity with the original 

scale has several advantages. Most obvious are the possibilities opened 

to applied researchers. Community noise surveys, visitor intercept interviews, 

and fieldwork on sleep disturbances would benefit from the addition 

of an individual difference measure of noise sensitivity but cannot always 

include the additional items necessary for such a measure. Oftentimes, such 

situations necessitate the use of a single-item noise sensitivity question, but 

such measures have been shown to lack reliability (e.g., Zimmer & 

Ellermeier, 1999). The NSS-SF confers all the advantages of the longer, 

original NSS without adding the same burden of items; it could serve as a 

compromise between questionnaire brevity and the need for good measurement. 

Taken a step further, it would not be unreasonable for the NSS-SF to 

be used in laboratory situations in place of the longer original. After all, the 

two scales are structurally identical, highly correlated, and have comparable 

reliability. Why measure something with 21 items if you can accomplish the 

same goals with only 5? 

This is not to say that the NSS-SF should be heralded as the definitive 

measure of noise sensitivity. The original NSS is highly valued within noise 

research and is often the exemplar in discussion related to noise sensitivity 

(e.g., Job, 1999; Miedema & Vos, 2003; Zimmer & Ellermeier, 1999). As 

such, it was an obvious choice for the current research purpose. However, 

other measures such as Zimmer and Ellermeier’s (1998, 1999) Noise 

Sensitivity Questionnaire (LEF) are equally valuable in noise research and 

may even be more appropriate in some contexts. Unfortunately, the LEF 

contains 52 items making it impractical for use in brief surveys. Balancing 

the importance of questionnaire length, research specific goals, and different 

measurement options is something that individual researchers will have to 

decide on themselves, but future research involving the NSS-SF and other 

measures of noise sensitivity can help with those decisions. Perhaps shortened 

versions of other scales could be developed to allow for use in field 

situations to complement the versatility and advantages given by a 5-item 

NSS-SF. 

In the end, a new option for noise researchers has been created that allows 

for survey brevity and measurement validity. It is hoped that such an instrument 

allows for new research avenues to be opened in field settings or while 

addressing applied problems; it is also hoped that such an instrument facilitates 

the inclusion of individual noise sensitivity into noise research generally. 

More broadly, the NSS-SF might be useful in studies of and interventions for 

the serious health consequences of noise, which include among other things 

hypertension, ischemic heart disease, cognitive impairment in children,


tinnitus, and sleep disturbance (e.g., Fritschi et al., 2011). Should the NSS-SF 

be predictive of who is more susceptible to these noise health consequences, it 

could also be used to indicate who might benefit more from interventions to 

curtail noise exposure. 

Declaration of Conflicting Interests 

The author(s) declared no potential conflicts of interest with respect to the research, 

authorship, and/or publication of this article. 

Funding 

The author(s) disclosed receipt of the following financial support for the research, 

authorship, and/or publication of this article: This research was supported by 

Cooperative Agreement No. H2380040002, Metrics of Human Responses to Natural 

Sound Environments, from the National Park Service. Grantees undertaking projects 

under government sponsorship are encouraged to express freely their findings and 

conclusions. Points of view or opinions do not, therefore, necessarily represent official 

National Park Service policy. 

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Bios 

Jacob A. Benfield is an assistant professor of psychology at the Pennsylvania State 

University’s Abington College where he conducts research on environmental stressors, 

privacy/territoriality, and social psychology. 

Gretchen A. Nurse is currently an assistant professor in the family and consumer 

science department at the University of Arizona where she researches sustainable 

consumer behavior, decision making, sustainable agriculture, and attitudes and values 

related to natural resources. 

Robert Jakubowski, senior managing associate of research, evaluation, and innovation 

at JVA Consulting, LLC, is currently researching the impact context-specific stimuli has 

on perceptual, behavioral, and cognitive outcomes in applied environments. 

Adam W. Gibson is a temporary assistant professor of human dimensions of natural 

resources in the environment and society department at Utah State University. 

B. Derrick Taff is a postdoctoral researcher at Colorado State University studying 

protected areas visitor use management with an emphasis on visitor education and 

behavior.


Peter Newman is an associate professor of protected areas management and associate 

dean, in the Warner College of Natural Resources, at Colorado State University, 

where he conducts a program of research exploring the protection of the acoustic 

environment in national parks. 

Paul A. Bell is a professor emeritus at Colorado State University where he has been 

conducting environmental psychology research for more than 35 years.

Benfield, J.A., Nurse, G.A., Jakubowski, R., Gibson, A., Taff, D ...

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