The sample of 5,853 respondents was more or less representative of the professional working population, with overrepresentation of males (73 percent) and professional (white-collar) workers (only 1 percent blue collar workers). Different age groups, educational backgrounds, marital statuses, sectors of employment, education levels, and management levels were well represented. In order to compare motivational variables and working hours, only answers from full-time workers were analyzed. Out of 5,853 respondents working full time, 3,314 could be identified as working in the private sector and 409 in the public sector. An additional 782 respondents belonged to the so-called hybrid sector (Wittmer 1991). A total of 424 were employed in education and 358 in health care. Finally, 1,258 respondents replied “other,” such as the unemployed, farmers, lawyers, students, and retired persons; 90 respondents did not answer the question. Private and public sector employees did not differ statistically on gender (both groups had about 26 percent female respondents), reported management level (on a five-point scale ranging from employee to senior manager), education, and marital status. The only statistically significant difference was age: The public sector group was significantly older than the private sector group (average age was 39 for public sector respondents and 36 for private sector respondents; t=5.73; p <.001). Any large-scale survey is hindered by the problem of missing values. The share of missing values was 1.19 percent, ranging from 0.35 percent to 0.50 percent for questions on gender, age group, and education to 4 percent on more personal questions. These figures are well below the 5 percent that is considered acceptable (Cohen and Cohen 1983). To deal with foutliers, a straightforward policy was adopted. Typographical errors were corrected and other data were respected as much as possible. The highest reported number of working hours was 120. Inspection of this case revealed that the person reportedly slept only four hours per night, did not invest in family life, and took only one hour off per week. About 1 percent of respondents reported total activities that required more than 168 hours per week. In those cases, we limited the reported off-work time so that the maximum of 168 was not surpassed. The Problem of Autoselection Our sample is clearly autoselective. This can cause problems of external validity (Rogelberg and Luong 1998). The only meaningful solution for problems of autoselection is to determine the variable by which respondents autoselect themselves and incorporate this variable into the final analysis (Heckman 1979). In practice, this variable is seldom known. If this hypothetical variable (e.g., interested in surveys, reading a widely distributed newspaper, higher socioeconomic class) is introduced in the analysis-in our case, a simultaneous regression analysis- the only assumption that must be made is independence of this hypothetical variable and our focal dummy variable (public sector employee versus private sector). This assumption is certainly realistic. It is difficult to conceive of a variable that would be an important determinant of autoselection and, at the same time, strongly correlates with the difference between the private and public sectors and does not strongly correlate with the other variables in the model. To put it differently, even if such an autoselection variable existed, its influence would be extremely small because most of its influence would be absorbed by the other variables, such as age, gender, or hierarchical levelvariables that are much more likely to be influenced by autoselection. Of course, if our analyses were based on comparing means and standard deviations with an absolute norm or with averages from other studies, the conclusions could be very misleading. We would report means and standard deviations of respondents ready to answer a survey. However, we primarily report differences. In these cases, autoselection is a much lower threat to external validity. Furthermore, selection by the researcher (e.g., “a large, midwestern municipality”), the most common practice in this kind of research, results in exactly the same problem. Researchers have to assume independence of the selection variable and the criterion variable. In practice, this is often questionable. One has easy access to the “local administration” and difficult access to the company or vice versa. The collection period in both organizations is different, and the distribution of questionnaires within both organizations follows different patterns. Nevertheless, even if this assumption is sometimes questionable, most publications seem to accept it and even seem to accept the clear restriction of range following from this research design. Scale Development Publishing a survey in a well-known, widely distributed magazine has the advantage of reaching a large number of respondents. However, there are also some constraints. Available
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