Because the final sample employed in the present study was one-third of the original, to compare the possible restriction of range, descriptive statistics were calculated for each of the predicted variables, both in the final sample and the original sample (Table 1). As can be observed in the data, the means and standard deviations are quite similar between them.

In Table 2, the correlation matrix among variables is included. As can be observed, mean academic achievement only has a positive correlation with Conscientiousness, whereas occupational attainment does not show significant correlations with the rest of the variables.

With respect to the possible underestimation of the real correlations due to the restriction of the range in the employed sample of this study, observation of the data in Table 1 does not seem to indicate that they are strongly affected by the low variability of the sample employed in this work. For example, the real observed correlation between intelligence general factor score (“g” factor) and career general grade is r=−.0200, whereas the real corrected correlation as determined by employing the formula of Guiselli, Campbell, and Zedeck (1981) (in Salgado, 1997) is r=−.0202.

Logistic regression mixture models ranging from a 1-class latent model to a 3-class mixture model were tested. Based on empirical and substantive consideration, a 2-class logistic regression model was selected as optimal. In this 2-class model, the regression coefficients and error variance were class dependent and were freely estimated without any equality constraints. In our work, we follow the procedure described by Nylund, Asparouhov, & Muthén (2007, p. 543) to estimate the BLRT (Bootstrap Likelihood Radio Test), that is, the log likelihood difference distribution to obtain a p value, which indicates whether the k-1 class model is rejected in favour of the k class model. In a similar way, other indicators were also considered.

In the 1-class logistic regression model, L2=299.27, the bootstrap p-value=.30 and the standard error (SE)=.02. In the 2-class logistic regression model, L2=245.93, the bootstrap p-value=.09, and the SE=.01. In the 3-class logistic regression model, L2=191.42, the bootstrap p-value=.008, and the SE=.004. The model L2 statistic indicates the amount of association among the variables that remains unexplained after estimating the model; the lower the value, the better the fit of the model to the data. Other criterion for determining the number of class is to examine the p-value. Among models for which the p-value is greater than .05 (provides an adequate fit), the 2-class model is more explicative/predictive. In the logistic regression, the overall R2 indicates how well the dependent variable is predicted by the model overall, similar to standard R2 measures. In the 1-class logistic regression model, R2=.05, whereas in the 2-class logistic regression model, the overall R2=.983, with class one R2=.97, and class two R2=.86. The 3-class logistic regression model does not increase the variance explained by the model; the overall R2=.987.

To assess model improvement by using a conditional bootstrap, the difference (Bootstrap -2LL Difference) in L2 between the 1-class and 2-class models is a measure of the amount of fit improvement associated with the 2-class model over the 1-class model. The results indicated (-2LL Difference=53.33) that the estimated p-value associated with the increase in classes was .03 (with standard error of .008); therefore, as p<.05, this means that the 2-class model does provide a significant improvement over the 1-class model. Furthermore, the prediction error in the 1-class model was .2006, which in percentage terms is 20.06% (63 of the 314 participants); meanwhile, the percentage error in the 2-class model was .000 (all participants were well-classified).

Examination of the class-specific probabilities in the 2-class model shows that overall class 1 members are most likely to be working (97%) and class 2 members are most likely not to be working (80%). Class 1 consisted of 77% of the total sample, of which 64% were female and 36% male; among this 77% of the total sample, 24% were from fields 3 and 5, 21% from field 2, 18% from field 6, and only 5% from field 1. Class 2 consisted of 23% of the total sample, of which 72% were female and 28% male; class 2 had 31% students from field 5, 26% from field 3, and only 6% from field 6 and 4% from field 4.

Table 3 provides the regression coefficients for each of the two latent classes and the regression coefficients from the conventional logistic regression analysis, along with the estimated class proportions and covariates. The beta-effect estimates under the column labelled Class 1 indicates that class 1 is influenced in a positive way by the variable extraversion and conscientiousness; that is, a higher score in extraversion and conscientiousness is linked to having a higher chance of being employed (Yes) in class 1.

The beta effect is estimated under the column labelled Class 2, showing that Class 2 is influenced in a positive way by the variables TMMS-Attention, by extraversion, and by conscientiousness, indicating that a higher score in these variables is linked to a greater chance of entering the labour market, whereas Class 2 is influenced in a negative way by openness, indicating that a lower score in openness is linked to those who are employed (Yes), and vice versa, a higher score in this variable is more common in those who are unemployed (No).

Extraversion had more or less the same influence on both classes. The Wald statistic indicates that the difference in these beta effects in the classes is not significant (W=0.0032, p=.96). This means that the two classes showed extraversion to the same degree. A similar situation was noted with conscientiousness (W=0.50, p=.48).

The gamma parameters of the model for the latent distribution appear under the heading “covariates”. The p-value associated with the Wald statistic shows that the overall effect for gender was non-significant (W=3.26, p=.07), whereas the effect for the field of study was significant (W=15.05, p=.01). Figure 1 displays a profile plot for the 2-class model. By default, the last categories for gender and field of study variables are displayed.

Figure 1.

Profile plot for the 2-class model.

(0.08MB).

To contrast the latent logistic regression analysis with the conventional logistic regression analysis, a logistic regression analysis was performed with the same dependent variable and independent variables, while controlling for gender and field of study. The results are shown in the right column of Table 1. It can be seen that occupational attainment was significantly related to TMMS-attention in a positive way and to TMMS-clarity in a negative way. It is important to note the low percentage of variance explained by the conventional logistic regression (R2=.05).

Furthermore, the 2-class model helps to interpret the results obtained with the entire sample, considering the different subpopulations that comprise it, given that a single equation for the entire sample does not make adequate predictions and incorrectly classifies the participants who are unemployed (20.06%), whereas the 2-class model classifies the participants correctly.

The results show the advantages of using linear regression mixture analysis instead of the conventional regression model when analyzing the relationships between independent variables and dependent variables. When comparing the conventional regression analysis–or logistic regression analysis–this assumes that one equation would fit all participants. A latent class logistic regression analysis can provide a description of subpopulations of participants within a sample. Thus, latent class regression analysis may improve predictability because the subgroup differences are systematically classified to form homogeneous groups, which supports the first hypothesis suggested in this study. In the present study, the results of latent class logistic regression analysis are evidenced with the existence of two subpopulations with specific patterns of regression function.

Second, the findings indicated that the personality traits of extraversion and consciousness were statistically significant in predicting occupational attainment throughout both latent classes, but other predictors such as openness or TMMA-attention were statistically significant only for distinct subgroups of graduates (class 2).

Regarding the first group of results, they show the positive correlation with the occupational attainment criteria, in line with the results obtained in other studies regarding these variables and other professional success criteria, such as salary (Gelissen & De Graaf, 2006; Judge, Higgins, Thoreson, & Barrick, 1999; Seibert & Kraimer, 2001; Sutin, Costa, Miech, & Eaton, 2009) or job satisfaction (Boudreau et al., 2001; Seibert & Kraimer, 2001). However, they also show the most generalizable character of the relationship of these variables based on a larger variety of different samples.

With regard to the second type of results, these bring to light the importance of the personality trait Openness (negatively), which confirms the second hypothesis and helps to resolve the discrepancies about the direction of the influence of this factor in previous studies, given that in some cases positive relationships with similar criteria had been found (Ng et al., 2005; Van der Linden, Te Nijenhuis, & Bakker, 2010), whereas in other cases there were negative relationships (Furnham, Taylor & Chamorro-Premuzic, 2008; Gelissen & De Graaf, 2006) or no relationship (Boudreau et al., 2001).

Respecting the positive relationship of the dimension of emotional intelligence TMMS-attention (not provided in the hypothesis), the results are aligned with previous studies for other success criteria (Bozionelos, 2004; Gelissen & De Graaf, 2006; Seibert & Kraimer, 2001), but these effects did not occur for the complete sample and only appear in group 2, which makes them different from the first group in the lesser likelihood of working.

An explanation of this different effect, according to the class that they belong to, is that participants with a lower probability of working are more sensitive to the negative effect of openness and positive TMMS-attention, unlike participants who achieve a job with a higher probability compared to those who would not be affected by having a higher distraction level or larger professional interests, or paying more attention to their own emotions, aspects that can distance people from their objectives. In summary, it is apparent that the dispersion or breadth of interests or objectives, and the capability to attend one's own emotions, distinctly affect graduates according to the success they have had in finding employment. For those who do not find work, being more aware of their own emotions and not being open to multiple options when looking for a job at the beginning of the professional career could be the key for this group, whereas for those who are working, these variables are not relevant.

When evaluating the results, it is important to consider the study's strengths and limitations. The main limitation of this study concerns the sample size. The present study may have lacked sufficient power to corroborate the statistical significance of the relationships that have been found using larger samples. Another limitation, derived from the size of the sample, is the impossibility of disaggregating the samples for close examination of the possible different behaviors of the studied variables. Moreover, latent class regression analysis has its own limitations. For example, if there exists non-normality within the classes, non-normality of observed variables, or non-linearity, the latent class may simply describe the skewness and may not reflect the latent classes of the individuals in the sample (Bauer & Curran, 2003). We must recognize that classifying individuals into latent classes is model dependent and is not intrinsic to the individuals in the sample (Lubke & Muthén, 2005).

What makes this contribution of interest is that this is the first time that this new approximation has been used, which enables an improvement in the precision of prediction equations in the field of career success, based on individual variables, cognitive, personality, and emotional intelligence, using a longitudinal approach. Including the variability resources that are not observed in the object study samples will allow us to establish more precise relationships between these individual variables and occupational attainment. If the relationships between these predictors and success at the initial phases of a career are more precisely known, adequate policies and interventions could be designed to improve the quality of selection processes on behalf of organizations, and orientation, training, and development programs for graduates on behalf of educational institutions.

This study shows the importance of including new methods of analyzing data, such as logistic regression analysis, in studies on predictive validity both in the field of psychology at work and at organizations, and in the field of education, which will allow us to perfect the recruitment processes and use it as a base to develop training actions on social and emotional competences aimed at university students. These types of formative actions, which could be taught not only during the university courses, but also in bachelor or job training, could be used in attention-training workshops to focus on more important aspects of job search or one's own emotions. The development of this focused process feedback has been shown to be the key not only in this field but also in many other walks of life, as Goleman (2013) has noted.

The authors of this article declare no conflict of interest.

This study was part of the research project ref. PSI2009-12696, funded by the Spanish Ministry of Science and Innovation.