EVALUATION OF THE INFLUENCE OF THE SURVEY OPERATORS’DEMOGRAPHIC CHARACTERISTICS ON THE

WORK ENVIRONMENT USING MULTIDIMENSIONAL ANALYSIS

Alina MoroşanuAlexandru Ioan Cuza

Iaşi/RomâniaE-mail [alynamorosanu@yahoo.com]

Abstract

The purpose of this study is to evaluate the influence of the survey operators’ demographic characteristics on the work environment. The data were collected by a survey aiming at identifying and studying the aspects that can contribute towards the improvement of the professional competence of the survey operators employed by a branch of a private company specialized in public survey. Using statistical software SPSS and applying Multiple Correspondences Analysis (AFCM) method the results showed that the studies and the age of the survey operators have the greatest influence on the work environment.

Keywords Survey operators’characteristics, work environment, multiple correspondence analysis

1. Survey operators’demographic characteristics

A survey, in the broadest sense of the word, is a means of investigation of the environment. Ever since ancient days, the survey was one of the main sources of information about events or about the people’s opinion on the administration. The sociologist Septimiu Chelcea (2006), writing about the beginnings and the development of the surveys, states that “in every age, at all the peoples we find (…) the vision on the psychological and moral changes that the persons undergo when they are gathered in a crowd.” Initially, the collecting of data by asking the opinion did not start in an organized manner: the people’s behaviour during a consultation or a trial was analysed.

In the course of time, this means of analysis proved very important. Jean Stoetzel and Alain Girard (1975) motivate this: “first of all, the governments always wanted to be informed on the state of mind of the people, that they regarded as a subject or as a sovereign, recruiting in case of need informers outside their social circle.” The sociologist Septimiu Chelcea has opinions that are similar to those of Jean Stoetzel and Alain Girard on ancient survey operators. He states that those responsible with collecting data were people from the palace, in disguise, who went round the markets in order to see the people’s state of mind, people who belonged to a network of local correspondents or spies of the police. Later, at the beginning of the 19th c, when elections surveys/polls appeared, some reporters asked the passers-by what their opinions were.

Today, surveys are made by specialized companies that collect their statistical data with the help of survey operators. Their selection is made according to the purpose of the survey, to their abilities, but also to some demographic characteristics. The sex, the age, the

studies and the experience of the operators are characteristics that can influence the process of obtaining answers to the questions of a questionnaire.

For each respondent, when he/she answers the questionnaire, it may matter if the question is asked by a man or a woman. Authors such as Diane O’Rouke et al. (1995), Francesco Flores-Macias and Chappell Lawson (2008), Herbert H. Hyman (1954), Mario Callegaro et al. (2001) have showed that the sex of the survey operators can influence the interpretation of the questions and the wording of the answers. Septimiu Chelcea (1975) agrees with this: ”In a verbal conditioning experiment, the subjects uttered more hostile words when the experimenter was a good-looking female than when the experimenter was male. A simple task of sorting was better fulfilled when the subjects and the experimenters were of opposite sexes. It has been proved that in analyzing the results one must see if the survey operators and the subjects are of the same sex. Female survey operators are more frequent than male survey operators. Jean Stoetzel and Alain Girard (1975) show that in 1960-1970 in “four companies, 1 German and 3 American, that had 800, 1200, 1200 and 300 operators, 97% were women.” A possible explanation for this situation is provided by Eugen Avram and Cary L. Cooper (2008): ”at the social level there is a series of expectancies regarding the way a man or a woman behaves in social circumstances.” Women are preferred for a job as a survey operator because they are more affectionate, empathic, relationships-oriented (negotiation and sharing information), attentive to the needs of the others, gentle, understanding and they inspire with confidence. As for men, they have an overbearing style and they are more assertive, active, domineering, control-oriented, self-confident, ambitious and independent. These characteristics confirm that women are more strongly oriented towards establishing and maintaining positive relationships while men are oriented towards accomplishing (or not) their tasks.

Another very important demographic characteristic that can significantly influence the surveys is the operators’age. Many authors have tried to underline this aspect in their studies. The sociologist Septimiu Chelcea, for example, writes that survey operators “are often recruited from retired intellectuals and middle-class housewives.” Other authors, such as Traian Rotariu and Petru Iluţ (2006) noticed that “a certain age correlated to physical qualities that inspire with confidence are elements that ensure the operator’s success, but it is very difficult to confine them into precise standards.” By analyzing these opinions we can infer that some qualities of the operators, such as self-confidence, a good tone of voice, empathy or professionalism, can be associated with age. They have a positive influence on the operator’s achievements; this is also seen in the studies made by Eleanor Singer et al. (1983) or Marc L. Berk and Amy B. Bernstein (1984).

Some specialists, such as Marc I. Berk and Amy B. Bernstein (1984), Eleanor Singer et al (1983) or Andrew Williams et al. (1999), have analysed in their studies the influence of the operators’ studies on the answer rate. Their results show that survey operators that have higher education get fewer non-answers than the operators that only graduated from high school. And also, the operators that know and are able to use the specific words, concepts and techniques collect more efficiently data in a complex survey. Moreover, better educated operators can manage other tasks that those of their contract. Therefore, the survey costs less. A similar opinion is held by Richard Newton (2006), who says that “a smaller group of more capable, better educated, (…) more motivated people“ can achieve more than a group twice or three times larger of less capable people. Nevertheless, the involvement of highly educated people depends on the size, the complexity and the field of the survey.

The experience of the operators in surveys is another characteristic that can have a major impact on the quality of the recorded data, bat also on productivity. Regarding the impact of the operators’ experience on the quality of the recorded data, Frank J. Mierzwa et

al. (2002), Kristen M.Olson and Andy Peytchev (2007) have shown that it is strong. They have underlined that although experienced operators persuade more easily the respondents to the survey and have fewer non-answers, there is the possibility of less precise surveys. For example, a big number of non-answers to the most delicate questions of the questionnaire reduce the quality of the survey. One of the reasons could be the speed of asking questions. Sometimes the respondents lose their patience and do not answer all the questions or they do not pay attention to the answers. Another reason could be that senior operators can become less motivated to fulfill their duties. Jean Stoetzel and Alain Girard realized that “the best survey operator can become with time the worst. His/her task, far from easy, implies great psychological wearing out. The operator takes or receives more than he/she gives. Disappointment can overcome even the most conscientious. On the other hand, using experienced operators implies lower training costs. With time they get used to the techniques and the instruments they use and training sessions are mostly necessary for inexperienced operators.

Taking into consideration the aspects that have been analysed, we can see the great importance of the demographic characteristics of the survey operators in collecting precise and correct data.

2. The work environment of the survey operators

The concept of work environment is very complex and it has been analysed in studies by many management researchers. Trying to define the work environment, Eugen Avram and Cary L. Cooper express their opinion that this syntagm refers to “the contextual aspects, the dimensions associated to it depending on ad hoc evaluations made by the employees.” In the opinion of other Romanian specialists the work environment is “the whole of the social and human characteristics of the organization as a complex system: the style of decision-making in the hierarchy, the way the boards of managers work, the fulfillment of the company’s social functions, the cooperation relationships of the work teams, of the sections, of the departments, of the workshops and of the offices.” [Septimiu Chelcea et al., 1980, p.217] From the point of view of some foreign researchers, the work environment shows “the state of mind of the employees that tends to prevail in a certain period, reflecting both the organizational culture and the recent evolution of the organization.” [Eugen Avram, 2008, apud Cherrington, 1989, p.99] or is ‘an example of perception of the behavior, of the attitudes and of the impressions that characterize an organization’s life.

There are several studies that define the significant variables of the work environment, and also their connection to achievement. For example, some researchers define as variables of the work environment the internal communication, the organizational structure, the political climate, the opportunities of career development, the evaluation, the promotion, the support in dealing with problems, the satisfaction at work (defined in its turn by the following subfactors: the participation to decision-making, the autonomy, the relationships with the superiors, the salary package and the advantages of the job) [Eugen Avram, 2008, p. 181]. Other authors underline the following things: administrative efficiency, authority, involvement, task fulfillment, innovation, sociability, bureaucratic rules or work standards.

Taking these into consideration, if we want to make an evaluation of the work environment of the survey operators we must pay attention to the work satisfaction, the work climate, the involvement, the quality of collaboration, the salary system, and the opportunities of career development. Usually, public survey companies either have a network of operators that covers the whole region of activity (hierarchically organized, with places of command and control at the centre of an important subregion) or a network of

persons near the location of the company that are sent where needed. [Traian Rotariu, Petru Iluţ, 2006, p. 233]

We can also take into consideration the fact that providing a proper work environment implies innovation, change and creativity. The combination of these elements provides the basis of a set of decisions that show a powerful dynamic organization.

3. The hypothese of the study

In order to improve the environment in which the survey operators work and to get the best results in the company, we want to evaluate the influence of the demographic characteristics on the work environment. This evaluation could be useful in choosing a management strategy that could solve the problems of the department.This argument leads to the following hypothesis:

H1: There are statistical connections between the variables that express the demographic characteristics of the survey operators and the variables that define the work environment.

4. The research method

Measuring the influence of the demographic characteristics of the survey operators on the work environment implies choosing a research method that leads to useful and correct information. In order to do this, we used statistical data collected by a survey made in a branch of a private company specialized in public surveys. The data were collected from 7 th

January to 20th February 2008. The necessary information on the employees that were working as survey operators has been provided by the human resources department of the company (table 1).

Table 1. Characteristics of the studied population

(N) PercentageTotalSexMaleFemaleAgeUnder 20 years20-24 years25-30 yearsOver 30 yearsStudiesHigh school graduateUndergraduate studentBachelor’s degreeStudying for a Master’s degreeMaster’s degreeSeniority Less than 6 months0.5-1 year1-1.5 years1.5-2 yearsOver 2 years

(107)

(12)(95)

(7)(67)(30)(3)

(27)(48)(21)(2)(9)

(13)(50)(28)(14)

100.0

11.288.8

6.562.628.02.8

25.244.919.61.98.4

1446.726.213.1

We found out that the staff of the survey operators department is made of 107 young people (20-24 years) 62,6%, of which 44,9 % are students, 88,8% are female, 46,7% have 0,5-1 year of seniority and that the personnel turn-over is high (60%). Therefore the new-comers have not been included in the survey: they did not know the staff and did not have enough experience or information to answer the questions.

The estimation of the resources necessary in order to conduct the survey was made by making a schedule of the activities in order to know when and for how long an activity takes place. The monitoring of the survey activities was made by a time budget and for the other category of resources we have made an aggregate budget. A Gantt diagram based on the list of the main activities of the survey (for which we estimated the time and the resources) proved very useful.

The technique used in the interviews was face-to-face. In turns, the operators were asked to stop working, being invited into another room in order to answer the questions. This lead to a higher degree of objectivity and provided good conditions for the interview.

The questionnaire was made of questions that provided objective information (age, sex and studies) and subjective information (opinions, attitudes and motivations). In order to explain the purpose of the survey there was a short introduction, and then there were questions about the person’s profile (sex and age). Afterwards, in order to get a better statistical classification of the answers, we introduced the studies and the seniority. The questionnaire contains questions about the work environment, the quality of the collaboration, the salary package, the career management. There were a few delicate questions where some people showed reticence. At the end of the questionnaire there are also some thanks to the asked person: “Thank you for your time!”

In order to make sure that this questionnaire is totally suitable to the objectives, well formulated and without words leading to misinterpretations and confusion, we have conducted a pre-survey on an echantillon of 20 survey operators. A lot of attention was paid to data authenticity and quality, aspects that improved the efficiency of the observation. All the employees of the survey operators department gave their consent, therefore we had 107 interviews.

5. Variables

As we want to evaluate the influence of the demographic characteristics of the survey operators on the work environment, we had 2 categories of variables. The first category represents some characteristics of the operators. Each operator is characterized by sex, age, studies and seniority.

In order to classify the operators according to their belonging to a characteristic we used nominal scales. This implied allocating a cod to each characteristic. This way, the variables have the following codes in the data base: the person’s sex (Q1) (1-Male and 2-Female), age (Q2) (1-under 20 years; 2-20-24 years; 3-25-30 years; 4-over 30 years), studies (Q3) (1-Highschool graduate; 2-Undergraduate student; 3-Bachelor’s degree; 4-Studying for a master’s degree; 5-Master’s degree), seniority (Q4) (1-less than 6 months; 2-0,5-1 year; 3-1-1,5 years; 4-1,5-2 years; 5-over 2 years).

Taking into account the dimensions of the work environment identified in scientific literature, the second category of variables expresses the satisfaction of the survey operators with: the work environment (E1), the quality of the collaboration (E2), the salary package (E3) and the career management (E4). These variables were given numerical codes for each category in the data base. In order to do this, we used the simple ordinal scale (1-Very dissatisfied, 2-Rather dissatisfied, 3-Neutral, 4-Rather satisfied and 5-Very satisfied).

6. Data analysis

In order to analyse the connections between the demographic characteristics of the survey operators and the work environment we have chosen some variables and, according to their nature, some methods of analysis. In the first stage a descriptive data analysis was made. The second stage implied the use of one of the multivariate analysis methods.

6.1 The descriptive data analysis

The descriptive data analysis was made in order to characterize the statistical distribution of the variables that express the degree of satisfaction of the operators with the work environment, the quality of the collaboration and the satisfaction with the salary package and with the career management (table 2). We used SPSS to analyse the data.

Table 2. Descriptive factors

The results in table 2 show that there 107 valid cases. Most asked persons gave satisfaction scores of 3 and 4 (3-Neutral and 4—Rather satisfied). The small values of the standard error that we got for the first 2 variables indicate a bigger concentration of the scores around the average.

The values for skewness and kurtosis obtained for the analysed variables are different, as shown by the graphs (figures 1, 2, 3 and 4). The distributions show small values both for skewness and for kurtosis. This values show skewness towards the left for 3 of the variables and a bigger concentration around the central value for the variables “Work climate” and “Quality of collaboration”.

Kolmogorov – Smirnov Test: Z = 2,697 (p = 0,00)

Fig. 1. Distribution of the answers regarding the survey operators’satisfaction with

the work climate

Kolmogorov – Smirnov Test: Z = 2,823 (p = 0,00)

Figure 2. Distribution of the answers regardingthe operators’ satisfaction with the

quality of the collaboration

Kolmogorov – Smirnov Test: Z = 2,697 (p = 0,00)

Fig. 3. Distribution of the answers regarding the operators’ satisfaction with the reward system

Kolmogorov – Smirnov Test: Z = 2,697 (p = 0,00)Fig.4. Distribution of the answers regarding the

satisfaction of the operators with thecareer management

The value of the significance test Sig. obtained for the Kolmogorov-Smirnov test is inferior to 0.05 for all the analysed variables. These results confirm that the distributions differ significantly from the normal distribution.

6.2 The analysis of the connections between the characteristics of the survey operators and the work environment

In order to identify and describe some connections between the characteristics of the survey operators and the work environment we used the Multiple Correspondences Analysis. It is included in the set of statistical methods that reduce the dimensionality resulted from the combination of the initial data and it was developed by Jean-Paul Benzécry in 1960-1970. The method of multiple correspondence analysis implies a larger number of variables, that is why it is considered an extension of the correspondence analysis. Generally, its purpose is to show (frequently by using graphical representations) the coordinates regarding the connections (the correspondences) of the modifications of the variables and to find the best values in order to delimit the categories of variables as clearly as possible. This implies that homogenous values must be projected close to each other and those that do not belong to this category must be projected far from each other on the graph. [Donna L. Hoffman, Jean de Leeuw, 1992, p. 260] Our data analysis using this method of multidimensional statistics lead to the results that shall be presented below.

In table 3 we notice that the first column shows the maximum number of factorial axes, that is the difference between the total number of categories and the total number of statistical variables without missing values. A small number of dimensions would be easier to analyse, but if there were more dimensions their significance/relevance would become at a certain point negligible. In our case, the maximum number of dimensions is 2. The first dimension accounts for 35,8% of the inertia of the cluster of points and the second dimension-for 30,8%.

Table 3 Values of the first two factorial axes

The graph obtained in figure 5 shows the projection by points of the different values of the analysed variables onto the first 2 dimensions. The colour of the lines and the points projected in the graph helps us to identify better the elements belonging to each category. The place of a point in respect to the origin shows the standard variance of the answer tendency. The variables that have characteristics that belong to the most frequent category are placed close to the origin, while those that have unique characteristics are projected further from the origin.

The first dimension of the graph (the horizontal axes) separates the answers regarding the satisfaction of the operators with “Career”, “Salary” and “Work climate” from the other variables according to “Studies”. The second dimension (the vertical axis) delimits the variables “Work climate”, “Career”, “Quality of collaboration” and “Studies”. By

transforming figure 5 we obtained a representation of the values of the analysed variables (figure 6).

Fig. 5. Graphical representation ofthe connections of the variables Fig. 6. Graphical representation of the values

of the analysed variables

We notice that the variables that express the satisfaction with “Career” and “Work climate”. “Quality of collaboration” and “Studies” make a good delimitation for both dimensions having big values of the coefficients.

Fig. 7 Object scores plot

We infer from figure 7 that the variables “Salary” and “Age” have a high value/score of delimitation for the first dimension and a low value/score for the second dimension. The variables “Seniority” and “Sex” are very close to the origin of the graph and have the smallest influence. “Work climate”, “Career”, “Quality of collaboration” and “Studies” are the variables with rather high values/scores for both dimensions.

Conclusions

The aim of this article was to evaluate the influence of the characteristics of the survey operators on their work environment. In order to do this, we have conducted a survey in a branch of a company specialized in public surveys. Our results show that the most influential characteristic of the survey operators on the satisfaction with the work environment is that of studies. By analysing the data we could notice that a higher education level has a positive impact, because these persons attach more importance to the work climate, the quality of the collaboration, the reward system and the career management. We could also notice that better educated operators expressed their opinions/ideas without hesitation, they realized better the limits/threats, and they were more involved and oriented towards the aspects that lead to a successful fulfillment of the duties. We have also inferred that the operators that know the words, the concepts and the techniques deal better with more complex tasks.

The second demographic characteristic that has a significant influence on the satisfaction with the work environment of the department is the operators’age. Our data analysis has shown that a high percentage of the operators are young (20-24 years) 62,6%. Generally, young people are more enthusiastic, passionate, creative and dynamic. They have an informal style; they look for ways of answering to the superiors’ demands and of approaching a problem. An energetic attitude often gives energy to the colleagues, helping them to cope with everyday problems. Therefore, in many cases energy and enthusiasm are preferred to experience, that was confirmed by the results of the data analysis.

Nevertheless, Nicole and François Berthier (1978) maintain that young operators are “an endangered species”. Young people, although they are more dynamic and sociable, tend to exaggerate, to rebel, to assert themselves at any cost, to be impatient in fulfilling their duties. Young people are often more whimsical, solitary, individualistic, impulsive or extravagant and have difficulties with tasks implying patience. These characteristics of youth can lead to conflicts, tension, resulting sometimes in the failure of the survey.

Therefore we can say that adults are more useful than young people. They are better survey operators because they have more professional and personal experience. They also think and act in a more sensible way. Moreover, the adults have these characteristics “because their life has expanded, they have more preoccupations (economic, educational, leisure, political, domestic etc.), and especially thanks to their participating to these.”[Mielu Zlate, 2008, Vol. I, p. 225)

After this study we can conclude by saying that these results are only valid in certain cases. They are a point of view that can help people to improve the operator’s productivity and to allocate better the resources of a survey.

References

[1] Avram Eugen, Cooper L. Cary, Psihologie organizațional-managerială, Polirom Publishing, București, 2008; [2] Berk L. Mark, Bernstein B. Amy, Interviewer Characteristics and Performance on a Complex Health Survey, pp. 808-812, 1984 (http://www.amstat.org/sections/srms/Proceedings/papers/1984_153.pdf - accessed on 20.02.2010 ); [3] Brenner Michael, Marsh Peter, The Social Context of Method, Croom Helm Publishing, 1978; [4] Callegaro Mario et al., Interviewer Effects in a Rdd Telephone Pre-election Poll in Minneapolis 2001. An Analysis of the Effects of Interviewer Race and Gender, (2001);[5] Chelcea Septimiu, Chestionarul în investigaţia sociologică, Scientific and Encyclopaedic Publishing, Bucureşti, 1975;[6] Chelcea Septimiu, Mărginean Ioan, Zamfir Cătălin, Dezvoltarea umană a întreprinderii, Publishing of Socialist Republic Academy of Romania, București, 1980;[7] Chelcea Septimiu, Opinia publică strategii de persuasiune și manipulare, Economic Publishing, București, 2006;[8] Chelcea Septimiu, Metodologia cercetării sociologice. Metode cantitative şi calitative, Economic Publishing, Bucureşti, 2007;[9] Clausen Sten-Erik, Applied Correspondence Analysis. An Introduction, Quantitative Applications in the Social Sciences, Series/Number 07-121, Sage Publications, 1984; [10] Flores-Macias Francesco, Chappell Lawson, Effects of Interviewer Gender on Survey Responses: Findings from a Household Survey in Mexico (http://web.mit.edu/polisci/students/fflores-macias/FloresMacias%20&%20Lawson%20IJPOR%20forthcoming.pdf - accessed on 17.03.2010), 2008;[11] Gelade A.Garry, Ivery Mark, The Impact of Human Resource Management and Work Climate on Organizational Performance, Personnel Psychology, pp.383-404, 2003 (http://www.business-analytic.co.uk/article-hrm.pdf - accessed on 05.02.2010);[12] Herbert H. Hyman et al., Interviewing in Social Research, Chicago: University of Chicago Press, 1954;[13] Hoffman L. Donna, Jan de Leeuw, Interpreting Multiple Correspondence Analysis as a Multidimensional Scaling Method, Marketing Letters 3:3, pp.259-272, 1992(http://elabresearch.ucr.edu/blog/uploads/publications/Hoffman_de_Leeuw_1992_Mark_Lett.pdf accessed on 05.02.2010); [14] Hutcheson D. John, Prather E. James, Snow E. Rob, The Effects of Interviewer Characteristics on Item Response, pp.372-377, 1979 (http://www.amstat.org/sections/srms/Proceedings/papers/1979_075.pdf -accessed on 30.01.2010);[15] Isaksen G. Scott, The Situational Outlook Questionnaire: Assessing the Context for Change, Psychological Reports, pp.455-466, 2007 (http://www.cpsb.com/research/articles/climate-for-innovation/SOQ-Assessing-Context-for-Change.pdf - accessed on 15.01.2010);[16] Jaba Elisabeta, Niculicioiu P., Cercetarea selectivă, Publishing of ,,Al. I. Cuza" University, Iaşi, 1977;

[17] Jaba Elisabeta, Statistica. Sistem metodologic. Aplicaţii, Publishing of ,,Al. I. Cuza" University, Iaşi, 1986; [18] Jaba Elisabeta, Statistică, Ediția a treia, Economic Publishing, București, 2002;[19] Jaba Elisabeta, Grama Ana, Analiza statistică cu SPSS sub Windows, Polirom Publishing, Iaşi, 2004;[20] Jaba Elisabeta, Teoria şi practica sondajului statistic, note de curs, ,,Alexandru Ioan Cuza" University, Iaşi, 2004; [21] Lord B. Vivian, Friday C. Paul, Brennan K. Pauline, The Effects of Characteristics on Arrestees’ Responses to Drug-Related Questions, Applied Psychology in Criminal Justice, pp. 36-55, 2005 (http://www.apcj.org/documents/1_1_interviewer.pdf -accessed on 15.01.2010);[22] Mierzwa J. Frank, Rob Mc Cracken, Vandermaas-Peeler Russ, Tronnier D. Christine, The Impact of Interviewer Characteristics on Survey Participation Findings from NSCAW, American Association for Public Research 2002: Strengthening Our Community – Section on Survey Research Methods, pp.2343-2347, 2002 (http://www.amstat.org/sections/srms/Proceedings/y2002/Files/JSM2002-001040.pdf - accessed on 15.01.2010); [23] Newton, Richard, Managerul de proiect, Codecs Publishing, București, 2006;[24] Olson M. Kristen, Peytchev Andy, Effect of Interviewer Experience on Interview Pace and Interviewer Attitudes, (http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1019&context=sociologyfacpub - accessed on 29.03.2010), 2007;[25] O'Rouke Diane et al., Cultural and Gender Differences in the Response Editing of Health Survey Questions, (www.amstat.org/Sections/Srms/Proceedings/papers/1995_186.pdf - accessed on 17.03.2010), 1995;[26] Rotaru Traian, Iluț Petru, Ancheta sociologică şi sondajul de opinie, Second edition, Polirom Publishing, Bucureşti, 2006; [27] Singer Eleanor, Frankel R. Martin, Galssman B. Marc, The Effect of Interviewer Characteristics and Expectations on Response, Public Opinion Quartely, Vol.47, No.1, pp.68-83, 1983 (http://www.soc.iastate.edu/soc522a/PDF%20readings/Singer.pdf - accessed on 15.01.2010); [28] Stoetzel Jean, Girard Alain, Sondajele de opinie publică, Scientific and Encyclopaedic Publishing, Bucureşti, 1975;[29] V.S.R Vijayakumar, Managemant Styles, Work Values and Organizational Climate, Journal of the Indian Academy of Applied Psychology, Vol.33, No.2, pp.249-260, 2007 (http://medind.nic.in/jak/t07/i2/jakt07i2p247.pdf - accessed on 15.01.2010);[30] Zlate Mielu, Tratat de psihologie organizațional-managerială, Polirom Publishing, Vol. I, București, 2008;[31] Zlate Mielu, Tratat de psihologie organizațional-managerială, Polirom Publishing, Vol. al II-lea, București, 2007;[32] Williams Andrew et al., Retaining Good Interviewers: Characteristics that Affect Interviewer Turnover and Retention in a Capi Survey, (http://www.amstat.org/Sections/Srms/Proceedings/papers/1999_180.pdf accessed on 18.03.2010), 1999.