The career anchors concept is an approach to understanding career orientation and motivation. The original career anchor model was introduced by Schein in 1974. Several investigators have created revisions of the model to make it more useful. This dissertation is a continuation of the quest to evaluate the original model and the revised models with respect to empirical support. This study is the first of two studies in which measurement methods are devised to solve the psychometric problems of previous measures. In this study we create and test an "economic exchange" model to correct the problem of acquiescent bias. We test five career anchor models and this new scaling method against two sets of data. The first consists of data from 330 participants we collect in the present study, and the other is a set of correlation matrices from Barclay's dissertation meta-analysis of six previous studies from the literature. We find that the economic exchange method creates greater variances in the ratings (both within each person and across persons) as predicted, but the hypothesis of predicted increase in the range of correlation coefficients for this method is not supported. In its present form the economic exchange method is not found to be superior to the standard Likert scale method. In addition, the oppositionality of career anchor choices does not increase for older respondents as expected. From a confirmatory factor analysis test of goodness of fit of the five models against the six datasets of this dissertation and the six studies from Barclay's meta-analysis, we find no evidence for one best career anchors model. That is, the five competing theoretical models seem to each be "best" in some situations or populations.

This study focuses on the development of a validated and confirmed employee engagement measuring model for use by managers and academia. Data was collected from an array of South African managers by employing a structured 5-point Likert scale questionnaire. A total of 260 usable questionnaires could be analysed, signifying a high response rate of 80%. The Statistical Package for Social Sciences software (Version 18, Version 22.0 and AMOS for Windows) was used as the quantitative analytical software. The following statistical techniques were employed to analyse the data, namely the Kaiser-Meyer-Olkin measure of sampling adequacy, Bartlett‟s test of sphericity, Cronbach Alpha reliability coefficients, Exploratory factor analysis, Confirmatory factor analysis and the Pearson correlation coefficient. The development of the Measure Employee Engagement model wielded theoretical and empirical research. The format was structured into four logical stages, hence the presentation of the study in the approved article format. The study covers the following four steps (as per articles): Article one departed by performing a literature study of employee engagement constructs and its measuring criteria. It examined the application of a myriad of models in various application settings to identify the relevant constructs and measuring criteria. From these constructs and criteria, a draft questionnaire was constructed to collect the data on 11 employee engagement constructs. Validation of measuring criteria was performed to ensure that the criteria accurately measure the specific employee engagement construct. The data was also tested for acceptable reliability levels. The second article departs on the validation of the constructs and its measuring criteria, this time as a unified model and not, as performed in Article 1, the construct validation individually. The objective of this article was to simplify the complex model without deterioration of the measuring contribution thereof. This was achieved by employing factor analysis, and after four rounds of eliminating low-loading and dual-loading criteria, the questionnaire was reduced by 25 measuring criteria and seven factors were extracted explaining a favourable 69.75% of the variance. The simplified model was scrutinised to ascertain statistical validity thereof, an objective achieved with flying colours. The inter-correlations between the seven factors were satisfactory, underpinning the validity of the model. The third article focuses on confirming the employee engagement constructs statistically by means of Confirmatory Factor Analysis as well as to determine the goodness of the model fit. The results confirmed that all seven constructs were significant (p<0.05) and important according to the standardised regression weights. Surprisingly, the most important respondent construct Behavioural engagement had the lowest regression weight, while the lower rated Career growth opportunities showed a much higher regression weight – signifying a higher importance and influence on employee engagement. Regarding goodness of model fit, the CFI, RMSEA and Hoelter‟s indices‟ were used. These indices showed that the model as stated above to measure employee engagement is a good fit and that it can be operationalised to be employed in managerial application settings. Article four operationalised the model validated in Articles 2 and 3. The article thus reports on the actual measurement of the different employee engagement constructs as perceived by the respondents. The results showed that the respondents regarded all seven the constructs as important, with Behavioural employment being regarded as the most important one. Career growth opportunities, surprisingly, was rated the least important construct of employee engagement. Correlational analysis indicated that no significant correlation coefficients exist between the demographic variables and the constructs of employee engagement. The study consisted of both a literature study as well as an empirical study. The university libraries of the North-West University and Management College of South Africa‟s Business School were used to source reference materials with the aid of a specialised research librarian at the North-West University to assist in the location of the most appropriate sources. Apart from the conclusions based on the results obtained in model development, generalised conclusions include the development of a successful model development methodology and guidance in the use of a number of the statistical techniques. This could greatly assist future researchers in the design of their studies, even outside the discipline of employee engagement. / PhD (Business Administration), North-West University, Potchefstroom Campus, 2015

This study focuses on the development of a validated and confirmed employee engagement measuring model for use by managers and academia. Data was collected from an array of South African managers by employing a structured 5-point Likert scale questionnaire. A total of 260 usable questionnaires could be analysed, signifying a high response rate of 80%. The Statistical Package for Social Sciences software (Version 18, Version 22.0 and AMOS for Windows) was used as the quantitative analytical software. The following statistical techniques were employed to analyse the data, namely the Kaiser-Meyer-Olkin measure of sampling adequacy, Bartlett‟s test of sphericity, Cronbach Alpha reliability coefficients, Exploratory factor analysis, Confirmatory factor analysis and the Pearson correlation coefficient. The development of the Measure Employee Engagement model wielded theoretical and empirical research. The format was structured into four logical stages, hence the presentation of the study in the approved article format. The study covers the following four steps (as per articles): Article one departed by performing a literature study of employee engagement constructs and its measuring criteria. It examined the application of a myriad of models in various application settings to identify the relevant constructs and measuring criteria. From these constructs and criteria, a draft questionnaire was constructed to collect the data on 11 employee engagement constructs. Validation of measuring criteria was performed to ensure that the criteria accurately measure the specific employee engagement construct. The data was also tested for acceptable reliability levels. The second article departs on the validation of the constructs and its measuring criteria, this time as a unified model and not, as performed in Article 1, the construct validation individually. The objective of this article was to simplify the complex model without deterioration of the measuring contribution thereof. This was achieved by employing factor analysis, and after four rounds of eliminating low-loading and dual-loading criteria, the questionnaire was reduced by 25 measuring criteria and seven factors were extracted explaining a favourable 69.75% of the variance. The simplified model was scrutinised to ascertain statistical validity thereof, an objective achieved with flying colours. The inter-correlations between the seven factors were satisfactory, underpinning the validity of the model. The third article focuses on confirming the employee engagement constructs statistically by means of Confirmatory Factor Analysis as well as to determine the goodness of the model fit. The results confirmed that all seven constructs were significant (p<0.05) and important according to the standardised regression weights. Surprisingly, the most important respondent construct Behavioural engagement had the lowest regression weight, while the lower rated Career growth opportunities showed a much higher regression weight – signifying a higher importance and influence on employee engagement. Regarding goodness of model fit, the CFI, RMSEA and Hoelter‟s indices‟ were used. These indices showed that the model as stated above to measure employee engagement is a good fit and that it can be operationalised to be employed in managerial application settings. Article four operationalised the model validated in Articles 2 and 3. The article thus reports on the actual measurement of the different employee engagement constructs as perceived by the respondents. The results showed that the respondents regarded all seven the constructs as important, with Behavioural employment being regarded as the most important one. Career growth opportunities, surprisingly, was rated the least important construct of employee engagement. Correlational analysis indicated that no significant correlation coefficients exist between the demographic variables and the constructs of employee engagement. The study consisted of both a literature study as well as an empirical study. The university libraries of the North-West University and Management College of South Africa‟s Business School were used to source reference materials with the aid of a specialised research librarian at the North-West University to assist in the location of the most appropriate sources. Apart from the conclusions based on the results obtained in model development, generalised conclusions include the development of a successful model development methodology and guidance in the use of a number of the statistical techniques. This could greatly assist future researchers in the design of their studies, even outside the discipline of employee engagement. / PhD (Business Administration), North-West University, Potchefstroom Campus, 2015

Discrete-time survival analysis as an educational research technique has focused on analysing and interpretating parameter estimates. The purpose of this study was to examine the effects of certain data characteristics on the hazard estimates and goodness of fit statistics. Fifty-four simulated data sets were crossed with four conditions in a 2 (time period) by 3 (distribution of Y = 1) by 3 (distribution of Y = 0) by 3 (sample size) design.

The empirical testing of theories is an important component of research in any field. Yet despite the long history of science, the extent to which theories are supported or contradicted by the results of empirical research remains ill defined. Quite commonly, support or contradiction is based solely on the "reject" or "fail to reject" decisions that result from tests of null hypotheses that are derived from aspects of theory. Decisions and recommendations based on this forced and often artificial dichotomy have been scrutinized in the past. In recent years, such an overly simplified approach to theory testing has been vigorously challenged in the past.Theories differ in the extent to which they provide precise predictions about observations. The precision of predictions derived from theories is proportional to the strength of support that may be provided by empirical evidence congruent with the prediction. However, the notion of precision linked to strength of support is surprisingly absent from many discussions regarding the appraisal of theories. Meehl (1990a) has presented a logically sound index of corroboration to summarize the extent to which empirical tests of theories provide support or contradiction of theories. The purpose of this study was to evaluate the utility of this index of corroboration and its behavior when employing path analytic methods in the context of social science research. The performance of a multivariate extension of Meehl’s Corroboration Index (Ci) was evaluated using Monte Carlo methods. Correlational data were simulated to correspond to tests of theories via traditional path analysis. Five factors were included in the study: number of variables in the path model, level of intolerance of the theory, correspondence of the theory to the ‘true’ path model used for data generation, sample size and level of collinearity. Results were evaluated in terms of the mean and standard error of the resulting multivariate Ci values. The level of intolerance was observed to be the strongest influence on mean Ci. Verisimilitude and model complexity were not observed to be strong determinants of the mean Ci. Sample size and collinearity evidenced small relationships with the mean value of Ci, but were more closely related to the sampling error. Implications for theory and practice include alternatives and complements to tests of statistical significance, a shift from comparing findings to the null hypothesis, to the comparison of alternative theories and models, and the inclusion of additional logical components besides the theory itself. Lastly, an alternative conceptualization of the multivariate corroboration index is advanced to guide future research efforts.

This thesis consists of four papers that deal with several aspects of the measurement of model fit for categorical data. In all papers, special attention is paid to situations with sparse data. The first paper concerns the computational burden of calculating Pearson's goodness-of-fit statistic for situations where many response patterns have observed frequencies that equal zero. A simple solution is presented that allows for the computation of the total value of Pearson's goodness-of-fit statistic when the expected frequencies of response patterns with observed frequencies of zero are unknown. In the second paper, a new fit statistic is presented that is a modification of Pearson's statistic but that is not adversely affected by response patterns with very small expected frequencies. It is shown that the new statistic is asymptotically equivalent to Pearson's goodness-of-fit statistic and hence, asymptotically chi-square distributed. In the third paper, comprehensive simulation studies are conducted that compare seven asymptotically equivalent fit statistics, including the new statistic. Situations that are considered concern both multinomial sampling and factor analysis. Tests for the goodness-of-fit are conducted by means of the asymptotic and the bootstrap approach both under the null hypothesis and when there is a certain degree of misfit in the data. Results indicate that recommendations on the use of a fit statistic can be dependent on the investigated situation and on the purpose of the model test. Power varies substantially between the fit statistics and the cause of the misfit of the model. Findings indicate further that the new statistic proposed in this thesis shows rather stable results and compared to the other fit statistics, no disadvantageous characteristics of the fit statistic are found. Finally, in the fourth paper, the potential necessity of determining the goodness-of-fit by two sided model testing is adverted. A simulation study is conducted that investigates differences between the one sided and the two sided approach of model testing. Situations are identified for which two sided model testing has advantages over the one sided approach.

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 158 pages. / Thesis (Ph.D.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: The empirical testing of theories is an important component of research in any field. Yet despite the long history of science, the extent to which theories are supported or contradicted by the results of empirical research remains ill defined. Quite commonly, support or contradiction is based solely on the "reject" or "fail to reject" decisions that result from tests of null hypotheses that are derived from aspects of theory. Decisions and recommendations based on this forced and often artificial dichotomy have been scrutinized in the past. Such an overly simplified approach to theory testing has been vigorously challenged in the past. Theories differ in the extent to which they provide precise predictions about observations. The precision of predictions derived from theories is proportional to the strength of support that may be provided by empirical evidence congruent with the prediction. / ABSTRACT: However, the notion of precision linked to strength of support is surprisingly absent from many discussions regarding the appraisal of theories. In the early 1990s, Meehl presented an index of corroboration to summarize the extent to which empirical tests of theories provide support or contradiction of theories. This index is comprised of a closeness component and an estimate of precision. The purpose of this study was to evaluate the utility of this index of corroboration and its behavior when employing path analytic methods in the context of social science research. The performance of a multivariate extension of Meehl's Corroboration Index (Ci) was evaluated using Monte Carlo methods by simulating traditional path analysis. Five factors were included in the study: model complexity, level of intolerance, verisimilitude, sample size and level of collinearity. Results were evaluated in terms of the mean and standard error of the resulting multivariate Ci values. / ABSTRACT: Of the five central design factors investigated, the level of intolerance was observed to be the strongest influence on mean Ci. Verisimilitude and model complexity were not observed to be strong determinants of the mean Ci. The lack of sensitivity of the index to the other design factors led to a proposed alternative conceptualization of the multivariate corroboration index to guide future research efforts. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

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