Methodology: Filtering – Grouping – Analysis

The present data record represents a map of the real work situation. It was not collected within the context of the kind of properly proportioned investigation design that is to be found in laboratory investigations and it can be analysed only with the techniques of descriptive statistics. The stringent design of an intervention study in which precisely one parameter is varied cannot be applied to this kind of field study. It is not possible to compare two consecutive situations because the effect of interference variables can be limited but never eliminated. In section 4.1 of this chapter information was given about the selected data record, also outlining the particular features of the two schools; the Baumberge-Schule and Grundschule Stichnathstraße.

In practice there are usually two methods used for the data analysis in order to counter these difficulties in verification. The first is to use the largest possible quantity of representative data records and the second is to observe situations with maximum concurrence. The two selected data records respectively meet one of these preconditions. The data from the Grundschule Stichnathstraße covers eight classes while the data from the Baumberge Schule covers one class which was observed for one week before and one week after the intervention measure.

Both schools provide the following data, described several times already, for the purposes of evaluation.

• Noise level, 1sec. intervals

• Heart rate, 15sec. intervals

• Lesson observation (11 parameters), 1sec. intervals.

This gives a total data record per lesson (45 min) of:

• 180 heart rate values

• 2,700 SPL values

• 8,100 values for the shares of speech of the teaching staff

• 5,400 values for the shares of speech of the students

• 13,500 values relating to teaching methods

This permits a 1sec. time interval for the analysis, excluding the heart rate data which is recorded every 15 sec. Shorter time slices for the heart rate monitoring for the purposes of stress analysis in field studies relating to occupational science questions are not usually set due to difficulties in verifying the situation (see SCHÖNWÄLDER ET AL., 2001).

A verifiable handling of this very complex data record of a single lesson or a day of lessons generally requires a three-step approach. The first step is to filter the data records using the parameters defined so far. The second step involves breaking down the data records into time units which are defined on the basis of the relevant key questions. The final descriptive data analysis is carried out on this basis.

4.3.1 Filtering

As outlined above in 4.2.1 the data records are sorted according to both room acoustic as well as pedagogical features for the purposes of comparison. We proceed from the workplace model which has rarely been applied in this complexity, and when it has been, then predominantly in office workplaces. Little is generally spoken about the ergonomics of schools. Filtering the data records permits the isolation of individual effects and subsequently the combined effects of the individual

influential values. In this case two reactions values must be taken into consideration, working noise and stress. A basic filter consists of the choice of data records that are to be analysed. This process however is not referred to as filtering in this case but rather a pre-selection. It relates to the exclusion of data which does not contribute to answering the questions posed here, e.g. lessons carried out under unusual conditions, e.g. sport or music lessons that do not take place in the actual classrooms, or lessons with incomplete data records, e.g. noise level data without lesson observation.

4.3.2 Grouping

The basic question with regard to the grouping of data concerns the time slices to be applied. It is necessary to define time slices within which the respective data records can be related to one another within the context of each question. These time slices are formed primarily on the basis of the time structure which regulates the working rhythm of the school. The first important time interval is undisputedly the lesson itself.

To be able to analyse the teaching process, however, the individual lessons must be broken down into smaller time units. In the following, the 5 min time interval is used.

The lessons are thus divided into nine equal parts. Justification for this time window comes from several sources. In mathematics, a polynomial approximation to the third or fifth degree is used to describe the sequences of events that we find in schools, which is quite feasible with nine sampling points (see ULLRICH, 1981). One suspects that shorter time slices will deliver no new information (see 4.2.2). An analysis of the noise level in teaching at 5min. time slices allows both the calculation of the average value LAeq,5min as well as of the basic SPL LA95. The 300 individual LAeq,1sec values provide a more reliable basis for determining this 95% level. The 5min. time window has also been proven appropriate in determining a basic activation from the heart rate data (see SCHÖNWÄLDER ET AL., 2003, TIESLER ET AL., 2002).

It also shows the significance of the high level of temporal resolution with which the data was originally obtained. As far as we can tell the time slices imposed here allow a teaching-related data evaluation not only on a lesson basis but also on the basis of shorter time units. This process enables, for example, an internal analysis of lesson phases which are dominated by a particular teaching method.

4.3.3 Analysis

The analysis of the selected data after filtering and grouping is generally descriptive.

The data is not suited even for random comparisons. It is often based on the very different data quantities which remain after filtering and grouping and often on very different data structures. One thinks in this case of the observation data. Analysis of the frequency distributions in particular and pure average value comparisons are two methods used for the comparison of two data groups. This is generally more reliable in the given context, particularly if, as is often the case with recordings of heart rate or noise level, the data is not normally distributed. At some points, attempts are made to represent the relationship between two values by means of linear regression in order at least to illustrate trends. However, the stringent statistical preconditions required for this (see L^IENERT) are not present.

In the context of the noise level analysis it is necessary to point out a problem concerning averages. If for example the average level of noise is calculated for a lesson or any time interval, it refers to an energetic average, as is standard in noise research. The averaging is first carried out at the sound intensity level and is then

converted into the logarithmic level representation. Mathematical averaging is used, however, when comparing different time slices and/or situations. In this case the result therefore does not include an “Overall average level” from all 1sec. values of these time slices but is actually the value mathematically determined from the original average levels of the individual time slices.

None of the standard statistical significance tests are used in the course of the data analysis. The key characteristic of this approach, however, lies in the fact that it can provide a very precisely timed view of the work process as it is dominated by external factors.