Moderating Effect of Gender on the Association Between Occupational Aspiration and Carrier Maturity

Rimsha Lakesh*

Department of Home Science, Govt. D.B. Girls P.G. College, Raipur, India

*Corresponding author: rimshalakesh26@gmail.com

[Received: 31 January 2019; Revised version: 18 April 2019; Accepted: 22 April 2019]

Abstract: The objective of the present empirical piece of research work is to examine the moderation effect of gender on the relationship between occupational aspiration and career maturity. Following the stratified random sampling technique 1000 students were drawn from different schools at Durg city, to serve as participants in the present research work. Career maturity was measured by Career Maturity Inventory (Gupta, 1989). Occupational aspiration was measured by Grewal (1975). Moderation effect was worked out through hierarchical multiple regression analysis. Result of the study indicated that, gender was significant moderator on the relationship between occupational aspiration and career maturity. It is concluded that there is sufficient empirical and statistical evidence of the moderation effect of gender on the relationship between relationship between occupational aspiration and career maturity.

Keywords: Gender, Occupational aspiration, Career maturity.

Introduction

The problem of choosing, preparing and entering into careers have existed since the dawn of civilization. The process of vocational development denoting psychological, sociological, cultural and economic ingredients across time results in outcome which are effective in vocational behavior, decision–making ability and vocational maturity. Just as physical and intellectual development can be stunted if appropriate interventions are not applied so can be the normal development process of vocational development be stunted if appropriate interventions are not available in a planned and systematic way. There are specific skills that should be constructed and maintained throughout the lifespan in order to deal with career choice and management task at any given point of time (Super, 1990).

The fast and complex changes in present job conditions have made the work more complicated and difficult, as skilled manpower is an essential pre-requisite for quality and efficient production and adaptation in new technologies. Most of the students stand behind, majority of them lack in clear objectives and they amble through courses without acquiring much knowledge of preparing themselves for a certain future. Only few students pursue their education or choose their career with clear as to what they would eventually like to become.

In everyday usage, the term career seems to be associated with upward inability advancement or getting on via a series of related jobs. Oxford English Dictionary (1961) (Vol VI) defined career as “a person’s course or programs through life.” While Schein (1977) was of the opinion that career is a planned direction that an individual follow over time and space, which includes involvement in a specific role. But Super (1990) views career as a lifelong experience filled with decision that an individual has to make.

Initially called ‘vocational maturity’ the term now known as Super 45 proposed ‘career maturity’ years ago (Super, 1990). Super had become interested in describing people’s career - related behaviors in developmental terms more than 15 years previously advocating that occupational choice should be viewed, ‘as an unfolding process, not a point in time event’, which is regarded by some as his single most important idea. Though specifying career development stages from early adolescence through to retirement age, Super and his colleagues devoted their initial efforts theorizing about and studying adolescents in their high school years and early adulthood. Super’s work spanning form 1953 to 1996 can be seen as one of the most prominent among the career development theories of the previous century. It is a well - respected theory.

That provides a basis for the understanding of the construct of career concerns as moderated by the various stages of development of a person’ s life. Seen as a segmented theory by many (Salomon, 1982) it may be regarded as one of the most inclusive theories describing the factors affecting a person’s career. Career concerns can be operationally defined by means of the Adult Career Concerns Inventory (Super & Thompson, 1979).

Concept of career maturity 

The vocational or role self-concept develops and changes in accordance with perceived reality, developing vocational identity, the process of differentiation of the self from others and simultaneously the process of identification with others. Role-playing, from childhood, stimulates identification and the development of the vocational self-concept onwards. Reality testing which occurs during the adolescent years helps the individual to modify vocational decisions.

Super (1990) defined career maturity as the individual’s readiness to successfully cope with the developmental tasks at any given stage of life, career and the expectations; it is the stage of exploration. In Supers theory, when they are required to master the tasks of defining their self- concept and relate it to the world of work. Langley, Du Toit and Herbst (1996) defined career maturity as the extent to which an individual is able to master certain career developmental tasks that are applicable to his/her life stage. It is extremely important to identify and individuals state of career maturity in order to give appropriate career guidance.

Alvareg-Gonzalez et. al (2007) defined career maturity as behavior that a person manifests in the intent to carry out different career developmental tasks, appropriate to each stage of maturity.

Hall (2008) conceptualized career maturity as an individual’s readiness to make well-informed, age appropriate career decision and to shape ones career carefully in the face of existing societal opportunities and constraints.     

Career Maturity occupies an important place in adolescent life and has been found to be effected by some psychological, social and demographic factors differentially in different culture, race and sex groups. Career Maturity, for most people is lifelong process of engaging the work world through choosing among employment opportunities made available to them. Each individual undulating the process is influenced by many factors, including the context in which they live, their personal aptitudes and educational attainment.

Occupational Aspiration

Occupational aspiration has been defined as orientation towards developmental goal (Haller & Miller, 1967). It has been considered as a concept which is logically a special instance of the concept level of aspiration. According to Sewell (1957) the term "Level of occupational aspiration' and the 'level of educational aspiration' are both the special in stances of the concept of level of aspiration.

Since, level of aspiration is defined as orientation towards a goal so level of occupational aspiration indicates orientation toward an occupational goal. The concept of level of aspiration was first introduced by Haller and Miller (1967) while making a reference to the degree of difficulty of the goal which a person is striving. A series of experiments were conducted by Franken (1994) and others revealed that the level of performance in a task is not judged as 'success' or 'failure' in terms of the absolute degree of accomplishment, but in terms of level of 'aspiration' or goal one sets in that particular line of achievement. The level of aspiration gives direction to the behaviour of a person.   As it has been pointed out that the level of aspiration presupposes a goal, viz. ideal goal, this ideal goal is the inner structure of the level of aspiration, the ideal goal may be too easy for the person to achieve or may be too difficult for him. Knowing the ideal goal the goal seeker may set his goal at a place for the next action, this action goal is actually the realistic goal.

The realistic goal is the goal for which the goal seeker is sure to achieve. Contrary to it the realistic goal has been defined as the goal for which the individual is free to choose but is not sure of getting it achieved. Haller and Miller (1967) indicate that idealistic occupational level of aspiration is the job for which one would choose if he/she is free to choose. Whereas, realistic level of occupational aspiration is the goal for which the individual is really sure that he or she can get it.

Thus, the idealistic vocational aspiration refers to that aspiration which the individual considers best for him/her if he or she is free to choose any vocation. Realistic vocational aspiration is that vocational aspiration for which the individual is sure of getting it without any difficulty. Gender as a factor associated with career maturity operates differentially in different cultures (Lawrence and Brown, 1976). So it is clear that under Indian cultural set up gender as a determinant of career maturity might operate differentially than the western countries. No such systematic study has yet been conducted under Indian conditions. Since it has been demonstrated that different factors associated with career maturity operate differentially in different race, culture and gender groups (Lawrence & Brown, 1976) and since there is dearth of such systematic study under Indian cultural set up, it is appropriate to investigate empirically that, moderating effect of gender on the association between occupational Aspiration and carrier maturity among high school adolescent students.

Method

Research Design

In this research correlational research design was employed. Gender is the moderate on the link between occupational aspiration and career maturity.

Participants

High school students are target population in the present research. Participants from Hindi and English medium school both are included at Durg district, Chhattisgarh. Further, long time school absentee students were excluded in this research.  In this proposed research work 1000 of class 10^th within the age range of 14 to 16 years, were included. The stratified random sampling technique will be used. Stratification will be done on the basis of local, English/Hindi medium and government/private.

The participants from government school 44.2% and private school 55.8% were included in this study. Age ranged of participants from 13 to 16 years [13-14 (25.2%), and 15-16 (74.0%)]. The percentages of participants belonging to nuclear and joint families were 66.8% and 33.2% respectively. Total number of participants medium of education, Hindi and English were 442 (44.2%) and 558 (55.8%) respectively. The majority of participants belong to urban area (45.0%). Participants were boys 500 (50.0%) and girls 500 (50.0%) respectively in present study. The percentage value of participants concerning to total family income (per month) 10,000-15,000 (24.8%), 15,001-20,000 (34.9%), 20,001-25,000 (21.8%), 25,001-30,000 (12.7%) and 30,000> (5.8%).

Measures

Career Maturity Inventory:

To measure the career maturity of Subject the Indian adaptation of Career Maturity Inventory (CMI) by Gupta (1989) was used. The inventory was originally constructed and standardized by Crites (1973, 1978). It measures the maturity of attitudes and competencies that are critical in realistic career decision-making. The items of the inventory are suitable for the students of class IX and X. It has six independent dimensions- (a) attitudinal (b) self-appraisal, (c) occupational information (d) goal selection, (e) planning, and (f) problem solving. In the present sample the internal consistency (α) was .809 for attitudinal, .852 for self-appraisal, .839 for occupational information, .865 for goal selection, .832 for planning and .827 for problem solving.

The Occupational Aspiration Scale:

The occupational aspiration scale as abbreviated by O.A.S. is constructed and standardized by Grewal (1975). The scale is meant for measuring realistic and idealistic occupational aspiration of adolescents. In the scale 80 occupations with different prestige values are arranged in mixed order in eight multiple-choice items. The score of each item ranges from ‘0’ (lowest) to ‘9’ (highest). An individual’s score for the whole inventory ranges from ‘0’ to ‘72’. In the present sample the internal consistency (α) was .829 for idealistic occupational aspiration and .878 for realistic occupational aspiration.

Procedure

Prior to initiation of the study, all participants gave their informed and written consent. The study obtained ethics approval of the research degree committee for education of the Pt. Ravishankar Shukla University, Raipur, India. Introductory interview with the participants was made at different school at Drug district, Chhattisgarh.  They were aware about the objective of the research. Introductory interview, each participant was also illustrated the temperament of the research and the participants were illustrated about the privacy regarding acquaintance collected from them. They were urged to complete the questionnaire as per the instructions and after completion they returned the test and were acknowledged for their collaboration.

Result and discussion

All 1000 cases were included for data calculation. Hierarchical multiple regression models were used to examine the role of gender on the relationship between occupational aspiration and career maturity. Control variables were entered in model-1, moderate variable in model-2, predictive variables in model-3, and the interaction term (predictive X moderate) entered in full model-4. The observed changes in the level of significance (viz. DF) were indicators of significance role of moderators (Aiken & West, 1991). SPSS version 22.0 was used for prediction and moderation analyses.

Moderating effect of gender on the relationship between idealistic occupational aspiration and different dimensions career maturity

Attitudinal

Table-1 shows that, in the first model control factors (socio-demographic factors) explaining 25.00% of total variance (R²=.250; F _{(8, 991)} = 80.512; p<0.01).  School of the participants (1= government, 2=private) was positively associated with attitudinal (.116, p<0.05). This shows that, participants from private school reported high attitudinal. Age of the participants was positively associated with attitudinal (.208, p<0.01). This shows that, increasing age of participants reported high attitudinal.  Family of the participants (1=Nuclear, 2= Joint) was found negatively associated with attitudinal (-.129 p<0.05). This shows that, participants from nuclear family were reported high attitudinal. Teaching medium of school (1= Hindi, 2= English) was positively related with attitudinal (.237, p<0.01). This shows that, participants from English medium school were reported high attitudinal. Locality of participants was positively linked with attitudinal (.275, p<0.01). This shows that, participants from urban locality were reported high attitudinal. Fathers education of participants was positively associated with attitudinal (.216, p<0.01). This shows that, increasing level of participants fathers education reported high attitudinal. Mothers education of participants was positively associated with attitudinal (.291, p<0.01). This shows that, increasing level of participants mothers education reported high attitudinal. Total family income of participants were positively associated with (.283, p<0.01). This suggested that, participants belong to higher family income reported high attitudinal.  

In the second model, gender explained an additional 5.6% (DF_{(1, 990)} =  6.953, p<0.05) of the variance. Gender (1=boys, 2=girls) positively associated with attitudinal (.296, p<0.01).  This shows that, girls participants were reported high attitudinal. In the model-3 with idealistic occupational aspiration accounted for an additional 6.7% of variance (DF _{(1, 989)} = 7.137, p<0.01).  

`Idealistic occupational aspiration were positively associated with attitudinal (.280 p<0.01). This indicated that, those who had higher levels of idealistic occupational aspiration were reported high attitudinal.

In full model-4 the interaction term (gender X idealistic occupational aspiration) was entered; this interaction added 10.1% (DF _{(1, 988)} = 9.421, p<0.01) to the explained variance of attitudinal.

Self-appraisal 

Table-2 shows that, in the first model control factors (socio-demographic factors) explaining 19.00% of total variance (R²=.190; F _{(8, 991)} = 65.853; p<0.01). The type of Schools of the participants (1= government, 2=private) was positively associated with self- appraisal (.107, p<0.05). This shows that, participants from private school reported high self- appraisal. Teaching medium of school (1= Hindi, 2= English) was positively related with self-appraisal (.108, p<0.05). This shows that, participants from English medium school were reported high level of self-appraisal. Fathers education of participants was positively associated with self-appraisal (.228, p<0.01). This shows that, increasing level of participants fathers education reported high self-appraisal. Mothers education of participants was positively associated with self-appraisal (.231, p<0.01). This shows that, increasing level of participants mothers education reported self-appraisal. Total family income of participants were positively associated with (.210, p<0.01). This suggested that, participants belong to higher family income reported self-appraisal.

In the second model, gender explained an additional 3.5% (DF_{(1, 990)} =  5.872, p<0.05) of the variance. Gender (1=boys, 2=girls) positively associated with self-appraisal (.225, p<0.01).  This shows that, girls participants were reported high self-appraisal.

In the model-3 with idealistic occupational aspiration accounted for an additional 2.5% of variance (DF _{(1, 989)} = 6.341, p<0.01). Idealistic occupational aspiration were positively associated with self-appraisal (.280 p<0.01). This indicated that, those who had higher levels of idealistic occupational aspiration were reported high self-appraisal.

In full model-4 the interaction term (gender X idealistic occupational aspiration) was entered; this interaction added 3.8% (DF _{(1, 988)} = 5.981, p<0.01) to the explained variance of self-appraisal.

Occupational information 

Table-3 shows that, in the first model control factors (socio-demographic factors) explaining 21.00% of total variance (R²=.210; F _{(8, 991)} = 75.653; p<0.01).  School of the participants (1= government, 2=private) was positively associated with occupational information (.239, p<0.01). This shows that, participants from private school reported occupational information.

Age of the participants was positively associated with attitudinal (.090, p<0.05). This shows that, increasing age of participants reported occupational information. 

Family of the participants (1=Nuclear, 2= Joint) was positively associated with occupational information (.206 p<0.05). This shows that, participants from joint family were reported high occupational information. Teaching medium of school (1= Hindi, 2= English) was positively related with occupational information (.109, p<0.05). This shows that, participants from English medium school were reported high occupational information.   Locality of participants was positively linked with occupational information (.231, p<0.01). This shows that, participants from urban locality were reported high occupational information.

Fathers education of participants was positively associated with occupational information (.225, p<0.01). This shows that, increasing level of participants fathers education reported occupational information. Mothers education of participants was positively associated with occupational information (.231, p<0.01). This shows that, increasing level of participants mothers education reported high occupational information. Total family income of participants were positively associated with occupational information (.210, p<0.01). This suggested that, participants belong to higher family income reported high occupational information. In the second model, gender explained an occupational information 4.5% (DF_{(1, 990)} =  6.012, p<0.05) of the variance. Gender (1=boys, 2=girls) positively associated with occupational information (.267, p<0.01). This shows that, girls participants were reported high occupational information.

In the model-3 with idealistic occupational aspiration accounted for an additional 3.4% of variance (DF _{(1, 989)} = 5.637, p<0.01). Idealistic occupational aspiration were positively associated with occupational information (.234, p<0.01). This indicated that, those who had higher levels of idealistic occupational aspiration were reported high occupational information.

In full model-4 the interaction term (gender X idealistic occupational aspiration) was entered; this interaction added 9.3% (DF _{(1, 988)} = 7.326, p<0.01) to the explained variance of occupational information.

Goal selection  

Table-4 shows that, in the first model control factors (socio-demographic factors) explaining 23.00% of total variance (R²=.230; F _{(8, 991)} = 80.210; p<0.01). School of the participants (1= government, 2=private) was positively associated with goal selection (.247, p<0.01). This shows that, participants from private school reported goal selection. Family of the participants (1=Nuclear, 2= Joint) was negatively associated with goal selection (-.251 p<0.05). This shows that, participants from nuclear family were reported high goal selection. Teaching medium of school (1= Hindi, 2= English) was positively related with goal selection (.260, p<0.05). This shows that, participants from English medium school were reported high goal selection. Locality of participants was positively linked with goal selection (.282, p<0.01). This shows that, participants from urban locality were reported high goal selection. Fathers education of participants was positively associated with goal selection (.232, p<0.01). This shows that, increasing level of participants fathers education reported higher level of goal selection. Mothers education of participants was positively associated with goal selection (.254, p<0.01). This shows that, increasing level of participants mothers education reported higher level of goal selection. Total family income of participants were positively associated with goal selection (.278, p<0.01). This suggested that, participants belong to higher family income reported higher level of goal selection.

In the second model, gender explained an occupational information 5.2% (DF_{(1, 990)} =  7.158, p<0.05) of the variance. Gender (1=boys, 2=girls) positively associated with goal selection (.254, p<0.01).  This shows that, girls participants were reported higher level of goal selection.

In the model-3 with idealistic occupational aspiration accounted for an additional 3.5% of variance (DF _{(1, 989)} = 5.338, p<0.01). Idealistic occupational aspiration were positively associated with goal selection (.225, p<0.01). This indicated that, those who had higher levels of idealistic occupational aspiration were reported higher goal selection.

In full model-4 the interaction term (gender X idealistic occupational aspiration) was entered; this interaction added 9.8% (DF _{(1, 988)} = 7.905, p<0.01) to the explained variance of goal selection.

Planning   

Table-5 shows that, in the first model control factors (socio-demographic factors) explaining 21.00% of total variance (R²=.210; F _{(8, 991)} = 75.432; p<0.01).  School of the participants (1= government, 2=private) was positively associated with planning (.212, p<0.01). This shows that, participants from private school reported higher level of planning. Family of the participants (1=Nuclear, 2= Joint) was negatively associated with planning (-.234, p<0.05). This shows that, participants from nuclear family were reported high level of planning. Teaching medium of school (1= Hindi, 2= English) was positively related with planning (.245, p<0.05). This shows that, participants from English medium school were reported high planning. Locality of participants was positively linked with planning (.268, p<0.01). This shows that, participants from urban locality were reported high level of planning. Fathers education of participants was positively associated with planning (.257, p<0.01). This shows that, increasing level of participants fathers education reported higher level of planning.

Mothers education of participants was positively associated with planning (.260, p<0.01). This shows that, increasing level of participants mothers education reported higher level of planning. Total family income of participants were positively associated with planning (.212, p<0.01). This suggested that, participants belong to higher family income reported higher level of planning.

In the second model, gender explained an occupational information 4.0% (DF_{(1, 990)} =  5.654, p<0.05) of the variance. Gender (1=boys, 2=girls) positively associated with planning (.256, p<0.01).  This shows that, girls participants were reported higher level of planning.

In the model-3 with idealistic occupational aspiration accounted for an additional 1.0% of variance (DF _{(1, 989)} = 1.887, p>0.05). This is insignificant at 0.05 confidence level. 

In full model-4 the interaction term (gender X idealistic occupational aspiration) was entered; this interaction added 1.5% (DF _{(1, 988)} = 1.931, p>0.05) to the explained variance of goal selection. This is also insignificant at 0.05 confidence level. 

Problem solving    

Table-6 shows that, in the first model control factors (socio-demographic factors) explaining 27.60% of total variance (R²=.276; F _{(8, 991)} = 93.432; p<0.01).  School of the participants (1= government, 2=private) was positively associated with problem solving (.218, p<0.01). This shows that, participants from private school reported higher level of problem solving. Family of the participants (1=Nuclear, 2= Joint) was negatively associated with problem solving (-.157, p<0.05). This shows that, participants from nuclear family were reported high level of problem solving. Teaching medium of school (1= Hindi, 2= English) was positively related with problem solving (.251, p<0.05). This shows that, participants from English medium school were reported high level of problem solving. Locality of participants was positively linked with problem solving (.234, p<0.01). This shows that, participants from urban locality were reported high level of problem solving. Fathers education of participants was positively associated with problem solving (.225, p<0.01). This shows that, increasing level of participants fathers education reported higher level of problem solving.

Mothers education of participants was positively associated with problem solving (.281, p<0.01). This shows that, increasing level of participants mothers education reported higher level of problem solving. Total family income of participants were positively associated with problem solving (.256, p<0.01). This suggested that, participants belong to higher family income reported higher level of problem solving.

In the second model, gender explained an occupational information 8.6% (DF_{(1, 990)} =  7.012, p<0.05) of the variance. Gender (1=boys, 2=girls) positively associated with problem solving (.298, p<0.01).  This shows that, girls participants were reported higher level of problem solving.

In the model-3 with idealistic occupational aspiration accounted for an additional 5.1% of variance (DF _{(1, 989)} = 6.137, p<0.01). Idealistic occupational aspiration was positively associated with problem solving (.209, p<0.05). This suggested that, those who had higher levels of idealistic occupational aspiration were reported higher level of problem solving. In full model-4 the interaction term (gender X idealistic occupational aspiration) was entered; this interaction added 11.5% (DF _{(1, 988)} = 10.215, p>0.05) to the explained variance of problem solving.

Moderating effect of gender on the relationship between realistic occupational aspiration and different dimensions career maturity

Attitudinal

Table-7 shows that, in the first model control factors (socio-demographic factors) explaining 25.00% of total variance (R²=.250; F _{(8, 991)} = 80.512; p<0.01).  In model-2, gender explained an additional 5.6% (DF _{(1, 990)} = 6.953, p<0.05) of the variance.

In the model-3 with realistic occupational aspiration accounted for an additional 7.5% of variance (DF _{(1, 989)} = 8.129, p<0.01).

Realistic occupational aspiration were positively associated with attitudinal (.318 p<0.01). This indicated that, those who had higher levels of realistic occupational aspiration were reported high attitudinal. In full model-4 the interaction term (gender X realistic occupational aspiration) was entered; this interaction added 11.8% (DF _{(1, 988)} = 10.534, p<0.01) to the explained variance of attitudinal.

3.2.2 Self-Appraisal 

Table-8 shows that, in the first model control factors (socio-demographic factors) explaining 19.00% of total variance (R²=.190; F _{(8, 991)} = 80.512; p<0.01).  In model-2, gender explained an additional 3.5% (DF _{(1, 990)} = 5.872, p<0.05) of the variance.

In the model-3 with realistic occupational aspiration accounted for an additional 8.5% of variance (DF _{(1, 989)} = 8.328, p<0.01). Realistic occupational aspiration were positively associated with self appraisal (.326 p<0.01). This indicated that, those who had higher levels of realistic occupational aspiration were reported high self-appraisal. In full model-4 the interaction term (gender X realistic occupational aspiration) was entered; this interaction added 12.5% (DF _{(1, 988)} = 8.328, p<0.01) to the explained variance of self-appraisal.

Occupational information

Table-9 shows that, in the first model control factors (socio-demographic factors) explaining 21.00% of total variance (R²=.210; F _{(8, 991)} = 75.653; p<0.01).

In model-2, gender explained an additional 4.5% (DF _{(1, 990)} = 6.012, p<0.05) of the variance. In the model-3 with realistic occupational aspiration accounted for an additional 6.0% of variance (DF _{(1, 989)} = 7.326, p<0.01). Realistic occupational aspiration were positively associated with occupational information (.318 p<0.01). This indicated that, those who had higher levels of realistic occupational aspiration were reported high occupational information. In full model-4 the interaction term (gender X realistic occupational aspiration) was entered; this interaction added 15.0% (DF _{(1, 988)} = 12.665, p<0.01) to the explained variance of occupational information.

Goal selection

Table-10 shows that, in the first model control factors (socio-demographic factors) explaining 23.00% of total variance (R²=.230; F _{(8, 991)} = 80.210; p<0.01).  

In model-2, gender explained an additional 5.2% (DF _{(1, 990)} = 7.158, p<0.05) of the variance. In the model-3 with realistic occupational aspiration accounted for an additional 11.6% of variance (DF _{(1, 989)} = 8.659, p<0.01). Realistic occupational aspiration were positively associated with goal selection (.385 p<0.01). This indicated that, those who had higher levels of realistic occupational aspiration were reported higher goal selection. In full model-4 the interaction term (gender X realistic occupational aspiration) was entered; this interaction added 14.5% (DF _{(1, 988)} = 15.812, p<0.01) to the explained variance of goal selection.

Planning

Table-11 shows that, in the first model control factors (socio-demographic factors) explaining 21.00% of total variance (R²=.210; F _{(8, 991)} = 75.432; p<0.01).  

In model-2, gender explained an additional 4.0% (DF _{(1, 990)} = 5.654, p<0.05) of the variance. In the model-3 with realistic occupational aspiration accounted for an additional 10.9% of variance (DF _{(1, 989)} = 9.365, p<0.01). Realistic occupational aspiration were positively associated with planning (.325 p<0.01). This indicated that, those who had higher levels of realistic occupational aspiration were reported higher level of planning. In full model-4 the interaction term (gender X realistic occupational aspiration) was entered; this interaction added 13.9% (DF _{(1, 988)} = 14.165, p<0.01) to the explained variance of planning.

Problem Solving 

Table-12 shows that, in the first model control factors (socio-demographic factors) explaining 27.60% of total variance (R²=.276; F _{(8, 991)} = 93.432; p<0.01).  

In model-2, gender explained an additional 8.6% (DF _{(1, 990)} = 7.012, p<0.05) of the variance. In the model-3 with realistic occupational aspiration accounted for an additional 12.0% of variance (DF _{(1, 989)} = 9.859, p<0.01). Realistic occupational aspiration were positively associated with problem solving (.381 p<0.01). This indicated that, those who had higher levels of realistic occupational aspiration were reported higher level of problem solving. In full model-4 the interaction term (gender X realistic occupational aspiration) was entered; this interaction added 15.0% (DF _{(1, 988)} = 14.887, p<0.01) to the explained variance of problem solving.

A perusal of the 3^rd model of table 1-12 revealed it clearly that the construct of occupational aspiration idealistic as well as realistic is positively associated with all the dimensions of career maturity. The reason may be attributed to the following facts-

The participants with high level of occupational information (idealistic or realistic) showed high level of career maturity because the occupational level of aspiration give direction to the behaviour of a person. In fact the level of performance in a take is not judged as success or failure in terms of the absolute degree of accomplishment, but in terms of level of aspiration or goal one set in that particular line of achievement; keeping this fact in mind the better performance of the individuals who are high on occupational aspiration may be explained.

The level of occupational aspiration indicate orientation towards occupational goal and clue to this orientation the participants have strong awareness about the career suitable for them. Their attitude towards career and competencies (viz. self appraisal, occupational information, goal selection, planning and problem solving) became mature because of high level of idealistic and realistic occupational aspiration.

Khan and Alvi (1983) also found occupational aspiration as better predictor of career maturity. They conducted a study on the students of class X and reported that those subjects who have high degree of occupational aspiration they displayed better career maturity than the subjects with poor occupational aspiration.

In India, similar findings have been reported by Grewal (1975) and Hasan (2006) they are reported that the subject with realistic and long range occupational aspiration displayed significantly higher level of career maturity.

From table 1-12 the moderating effect of gender on the relationship between occupational aspiration and all the dimension of career maturity has been given.

After the close examination of these tables it may be safely stated that gender has moderated significantly the relationship between occupational aspiration (idealistic and realistic both) and all the dimensions of career maturity.

The reason may be attributed to the nature of the variables viz. occupational aspiration and gender and their roles in determining career maturity. They very nature of gender with regards to career maturity has already been discussed earlier.

In fact the level of occupational aspiration denotes orientation towards the formation of occupational goal. Consequently such subjects become aware about the process of selecting a suitable career for themselves. They develop positive attitudes and necessary competencies for selecting suitable career.

Due to the very nature of gender and occupational aspiration (idealistic or realistic) their interaction added the significant variance to the explained variance of career maturity.        

Conclusion

Present study concluded that there is sufficient empirical and statistical evidence of the moderating effect of gender on the relationship between occupational aspiration and career maturity. Present research demonstrates thorough understanding of the moderating role of a gender on the relationship between occupational aspiration and career maturity.

References

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