Prediction of Work Efficiency in Early Adolescence under the Effects of Noise
Fosnaric, Samo, Planinsec, Jurij, Adolescence
Our environment is becoming more polluted and the causes are detectable, especially in the rapid development of industry, traffic, and other "civilization needs." This "aggressive development" does not spare educational institutions, as schools become more endangered each day. In particular, noise is the most prevalent problem. The effect on early adolescents can be physiological, according to a study on heart rate and blood pressure (Neus, Ruddel, Schulte, & Von Eiff, 1983; Regecova & Kellcrova, 1995). Kasdorf and Klappach (1968) found that children in quieter school environments have fewer problems with high blood pressure, while children, especially boys, in city centers, have higher blood pressure. It is common knowledge that noise also causes stress (Ewans, Lercher, Meis, Ising, & Koler, 2001; Ising, Babisch, & Kruppa, 1999).
In studying the second level psychological effects, focus is on the effect of noise on attentiveness at work (Kyzar, 1977), memory (Hygge, 1993; Fosnaric & Planinsec, 2006), and speech perception. Research has noted that noise can cause numerous diseases. In the past, researchers believed that noise had a harmful effect only on the hearing organs, but today the effects have been shown to be much wider. Besides its harmful effect on the health of young people, noise indirectly affects their work. The greater the noise, the more intense its effect. Such noise disturbs both teachers and students. It especially disturbs normal conversation (Crook & Langdon, 1974; Ko, 1981; Sargent, Gidman, Humphreys, & Utley, 1980).
The effect of noise on the learning process has received considerable study both in schools and in laboratory settings. In contrast, relatively little research has been performed on how noise affects the work performance of early adolescents The present study examines the effects on specific school tasks.
The study sample consisted of 20 boys from Slovenia; 13.5 years (SD [+ or -] 0.25). All adolescents had been previously informed of the nature of the study, and written consent obtained from their parents.
The study was performed in artificially created work conditions--in a "climate chamber" where we could determine and vary the parameters of the sound, lighting, and thermal environment. In this way we were able to combine noise parameters--L (two levels: optimal-normal and increased-maximal noise level) with other stresses such as lighting--E (three levels: low-minimal, optimal-normal, and increased-maximal along with climate (three levels: low-minimal, optimal-normal, and increased-maximal climate stresses expressed in effective temperature--ET) (McIntyre, 1980).
Monotonous work operations are connected with the perception of certain "rare" signals which are related to longer time periods and are a basis for research on attentiveness at work. In investigating the influence of noise on attentiveness we used the Signal Detection Theory (Baker, 1959; Macworth, 1957; Swets, Tanner, & Birdsall, 1961). Attentiveness can change quickly depending on numerous factors which we can measure with a specially designed computer program. This program was designed and adjusted for special test needs. The program calls for the adolescent to carefully monitor the trail of a "carriage" on a monitor throughout the study. The carriage moves across the monitor at different time intervals and at different speeds. Based on the positions of the cargo on the carriage, the adolescents had to determine which carriage would tip over as a result of an incorrect arrangement of the cargo by responding "Yes," and which carriage had a correct load by responding "No." The monitoring of these monotonous tasks was also conducted on a computer wherein the number of "Commissions" (false signals noted by the user) and the number of "Omissions" (correct signals) were determined. A simultaneous analysis also showed the number of correct and incorrect detections and determined the sensitivity and criterion for each of the four sequences of the entire experiment. The result of the measurements was calculated on the basis of a random choice of the number of stimulations for the analysis.
Procedure and Analyses
The effect of individual factors of the thermal environment was expressed by only one index--effective temperature (ET) which results from a subjective comparison between a "thermal feeling' in a certain climate and the referential climate. Thus, the effective temperature is that of almost still air (speed = 0.1 m/s) filled with steam (RH = 100%) which creates a feeling of warmth. This feeling is identical to that produced by the combination of air temperature, humidity, and speed of air movement in the analysis room. Our climatic parameters were based on the recommendations of ASHRAE, 1992 and McIntyre, 1980.
The levels of lighting were chosen according to the regulations for classroom lighting. We also took into consideration the possible difference in difficulty of the work conditions. The levels of lighting were based on the existing regulations, combining international recommendations and official standards such as German DIN, IES, and TGL (IES Nomenclature Committee, 1979).
It was difficult to establish the noise parameters since it demanded different recordings of real work environments and a simulation in the climate-chamber space. It should be emphasized that the climate-chamber contained a constant noise source from the environment produced through a loudspeaker system. Additional measurements were taken of the noise levels using valid European standards and the data from our measurements in the fieldwork.
Twenty students went through measurements in 18 different working combinations for a total means of 360 measurements. Values for noise, lighting, and climatic conditions were determined on the basis of similar measurements in classrooms of 60 schools across Slovenia. The data acquired were later used in setting the values of the stresses in artificial conditions (see Table 1).
RESULTS AND DISCUSSION
After performing the measurements, all gained values were analyzed. Even though each of the factors of the work environment such as climate, light, and noise, have an independent effect on success, the context of their interactions shows that noise plays the primary role. To determine how all the factors affect attentiveness, the theory of the detection of "rare" signals was used. With the help of this theory various reactions of the adolescents were analyzed. These were: confirmation of the false signal in a task (Commission); omission of the correct signal (Omission); correctly detected signal in a task (Correct) and correctly omitted signal (Omitted).
Table 2 shows that adolescents under conditions of increased sound pressure achieve poorer results with the criterion confirmation of the false signal in a task--Commission (M = 5.09, SD [+ or -] 4.24) and omission of the correct signal--Omission (M = 8,75, SD [+ or -] 5.78), compared to adolescents who work in an undisturbed sound environment ([M.sub.comissions] = 2.73, SD [+ or -] 3.22) and ([M.sub.omissions] = 5.08, SD [+ or -] 4.59). The results in these two examples are statistically significant (p < .001). Similar results appear in the analysis of the criterion correctly detected signal--Correct (p < .02) and correctly omitted signal--Omitted (p < .05). As seen in Table 2, the number of correct detections and correct omissions is considerably lower when adolescents work in a noisier environment.
These results confirmed the earlier findings of Hambrick-Dixon (1986), that adolescents who live in a noisy environment have problems with attentiveness. These adolescents are more careless and in general achieve worse results. Thus we found that with an increase of noise in school environments there is a decrease in attentiveness to work tasks and, therefore, a decrease in efficiency.
Later in the study we performed prediction formulations for factors of work efficiency in performing tasks within a framework of set conditions. We discovered that the multiple correlation between the criterion variable Commission and prediction variables ET, E, and L is 0.309. The multiple correlation is statistically relevant (F = 12.548, p < .001). All predictors together explain 9.6% of criterion variation. With gradual inclusion of predicors, we discovered that one predictor has a statistically relevant influence: L--noise. If we exclude other predictors with the stepwise method, the noise predictor ([beta] = 0.307, p < .001) explains 94% of this criterion variation. These three predictors provide an important statistical explanation of criterion Commission (9.6%). Among the three criteria, only one is statistically relevant; noise accounts for 9.4% and other predictors (thermal environment and lighting) 0.2%.
Based on these results, a general formulation for calculation of criterion Commission, taking into account standard values according to the enter method (see Table 3): [F.sub.(Commission) = 2.020 + (3.26. [10.sup.-3] ET) + (-4.37 [10.sup.-5] E) + (7465.511 L). Since some predictors are not statistically relevant (ET and E), we used a different formation with the stepwise method (see Table 4), considering non-standard values of only statistically relevant predictors: [F.sub.(Commission) = [C.sub.(c)] + (7645.511 L);) [F.sub.(Commision)--factor of work success for criterion Commission; [C.sub.(c)]--calculated constant with a value of 2.711; L--noise, expressed as density of sound force (W/[m.sup.2])
We can assume that the evaluated (prognostic) results of work success within the criterion Commission will occur between the values of 2.52 and 5.29. In other words, work performed within the values of noise expressed in density of sound force [W.sub.(opt)] = 1.0 x [10.sup.-8] (W/[m.sup.2]) and [W.sub.(MAX)] = 3.2 x [10.sup.-4] (W/[m.sup.2]) will be less successful when [F.sub.(Commission)] approaches a value of 5.29 and more successful when the value is closer to 2.52.
Results of the research indicate that noise in school surroundings is a major problem, confirming several earlier predictions (Sanz et al., 1993; Fosnaric, 2003). This problem can most easily be seen with early adolescents, especially in their response to defined school tasks. As a matter of fact, noise proved to be the only significant stress factor in the work environment. Only noise affected work efficiency in monotonous work tasks used. Similarly, this has also been borne out in the field of short-term memory (Fosnaric & Planinsec, 2006). We find it especially rewarding that this success can be predicted within the framework of the defined marginal terms.
Further, we were able to confirm that stress in the work environment negatively affects early adolescents in terms of health as well as their success in work tasks (Bullinger et al., 1999; Ewans et al., 2001; Ising et al., 1999). Accordingly, it is important to pay greater attention to the protection of adolescents against noise in our educational institutions.
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Requests for reprints should be sent to Samo Fosnaric, University of Maribor, Faculty of Education, Koroska cesta 160, SI-2000 Maribor, Slovenia. Email: firstname.lastname@example.org
Jurij Planinsec, Faculty of Education, University of Maribor, Slovenia. Email: email@example.com
Table 1: Average values for noise, lighting and thermal conditions in the working environment Climate Values Noise RH=50% and v=0,3 m/s Lighting L (dB(A)) T ([degrees]C) ET([degrees]C) E(lx) MIN. -- 18 153 120 OPT. 35 24 205 350 MAX. 651 30 256 1000 ET - Effective Temperature ([degrees]C) T - Air Temperature ([degrees]C) L - Noise (dB(A)) E - Lighting (lx) Table 2: Results of values of individual research criteria Research [L.sub.opt.] Variables (n=180) Mean SD Comission confirmation of the false signal 2.73 3.22 Omission omission of the correct signal 5.08 4.59 Correct correctly detected signal 101.50 12.39 Omited correctly omitted signal 101.54 9.60 Research [L.sub.max.] Variables (n=180) Significant Mean SD p Comission confirmation of the false signal 5.09 4.24 < .001 Omission omission of the correct signal 8.75 5.78 < .001 Correct correctly detected signal 98.47 12.13 < .02 Omited correctly omitted signal 99.72 6.43 < .05 [L.sub.opt.] - Optimal level of Noise [L.sub.max.] - Maximal level of Noise Table 3: Summary of regression analysis results according to the >>enter<< method Unstandardized Co Standardized Commission Coefficients B SE B [beta] (Constant) 2.020 1.028 ET 3.26 E -02 0.047 0.035 E -4.37 E -05 0.000 0.005 L 7465.511 1224.902 0.307 R= .309 (a) [R.sup.2] = .096 Commission t Sig. (Constant) 1.965 0.050 * ET 0.699 0.485 E 0.090 0.928 L 6.095 0.001 ** p < .001 (a) (a) Predictors: (Constant), L (Noise), E (Lighting), ET (Thermal Environment) * p <.05 ** p <.001 Table 4: Summary of regression analysis results according to the >>stepwise<< method Commission Unstandardized Standardized Coefficient Coefficients B SE B [beta] Constant 2.711 0.277 L 7465.511 1222.329 0.307 R= .307 (b) [R.sup.2] = .094 Commission t Sig. Constant 9.802 0.001 *** L 6.108 0.001 *** p <.001 (b) (b) Predictors: (Constant), L (Noise) *** p <.001…
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Publication information: Article title: Prediction of Work Efficiency in Early Adolescence under the Effects of Noise. Contributors: Fosnaric, Samo - Author, Planinsec, Jurij - Author. Journal title: Adolescence. Volume: 43. Issue: 169 Publication date: March 22, 2008. Page number: 165+. © 1999 Libra Publishers, Inc. COPYRIGHT 2008 Gale Group.
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