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\definecolor{GJBlue}{HTML}{273B81} \definecolor{GJLightBlue}{HTML}{0A9DD9} \definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{ Saif-ud-Din} \affil[1]{ Qurtuba University Dera Ismail, Khan kpk, Pakistan.} \renewcommand\Authands{ and } \date{\small \em Received: 16 August 2012 Accepted: 15 September 2012 Published: 27 September 2012} \maketitle \begin{abstract} Research on job satisfaction is replete with the studies exploring the impacts of personal and demographic attributes of the employees on their organizational attitudes. Demographics are tested as the predictor of job satisfaction or dissatisfaction and its consequences like involvement, commitment, absenteeism and turnover. Mixed results have been reported by the researchers showing that different demographic factors play different roles in diverse settings, for example, in advanced and developing countries. Thus, researchers have proved that demographic analysis is indispensable to understand the employee attitudes. This paper is a part of PhD research project on ?job satisfaction of academicians in the HEIs of KPK, Pakistan.? \end{abstract} \keywords{Job Satisfaction, , , Demographics, Best Fit Models} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \par satisfaction of their workforce \hyperref[b15]{(Lise and Judge, 2004)}. Organizations want their employees to be satisfied to become productive, efficient committed (Shamil and Jalees, 2004). Job satisfaction is the degree of an employee's affective orientation towards their job \hyperref[b41]{(Tsigilis, Zachopoulou, and Grammatikopoulos, 2006)}. Thus, job satisfaction is a very important attribute that is frequently measured by all types of organizations (Beyth-Marom, Harpaz-Gorodeisky, Bar-Haim, and Godder 2006; Wikipedia, 2009; Sattar, Khan, and Nawaz, 2010).\par Job satisfaction is mostly predicted either with the factors of job satisfaction or demographic and personal attributes of the employees. The factors include pay, work, supervision, promotion, work environment, and coworkers (Williams and Sandler 1995; \hyperref[b39]{Stacey, 1998;}\hyperref[b12]{Ellickson and Logsdon, 2001}; DeVane and Sandy, 2003; \hyperref[b15]{Lise and Judge, 2004)}. Other investigators have used the concepts like personal and organizational factors \hyperref[b30]{(Saiyadain, 1998)}, personal \& job characteristics \hyperref[b37]{(Sokoya, 2000)}, challenging work, equitable rewards, encouraging working conditions, supportive co-workers, personality \hyperref[b20]{(Naval and Srivastava, 2004)}, and the "demographic relationships" between satisfaction and the faculty (Shamil and Jalees, 2004; \hyperref[b41]{Tsigilis et al., 2006}; Saifuddin, Khair-uz-Zaman, and Nawaz, 2010).\par Employees are diverse in their demographic attributes, which have implications for the degrees of satisfaction from job dimensions like pay, work, promotion etc \hyperref[b37]{(Sokoya, 2000)}. For example, gender, age, education, designation, numbers of years in organization and marital status of the employees have widely been found critical in determining job satisfaction \hyperref[b39]{(Stacey, 1998;}\hyperref[b19]{Marion, 2001;}\hyperref[b3]{Bas and Ardic, 2002;}\hyperref[b33]{Shah and Jalees, 2004;}\hyperref[b6]{Chughtai and Zafar, 2006;}\hyperref[b11]{Eker, Anbar, and Dirbiyik, 2007;}\hyperref[b1]{Asadi, Fadak, Khoshnodifar, Hashemi, and Hosseininia, 2008;}\hyperref[b18]{Malik et al., 2010;}\hyperref[b32]{Sattar, et. al., 2010)}. This study explores the issue of job satisfaction among the academicians in public and private sector universities of Khyber Pakhtunkhwa, Pakistan by empirically recording their attitudes and demographic attributes. Stepwise regression has been used to compute the significance of employeedemographics on their attitudes. \section[{II.}]{II.} \section[{Literature Review}]{Literature Review}\par Experience tells that satisfied worker is involved and committed to his/her duties while dissatisfied workers practice negative attitudes of absenteeism and turnover {\ref (Locke and Latham, 2000:249-250;}\hyperref[b3]{Bas and Ardic, 2002)}. Job satisfaction relates to an individual's perceptions and evaluations of the job, which are affected by the needs, circumstances, and expectations (DeVane and Sandy, 2003). It is an emotional response to a job situation that is determined by how well outcomes meet or exceed expectations, for example, if employees are treated unfairly, work hard but rewarded less, they are likely to develop negative attitudes toward their job, officers and colleagues. However, if they are treated fairly and paid well, they are expected to have positive attitudes for the organization {\ref (Luthans, 2005:212)}. Thus, job satisfaction is the expression of contentment by an employee with regard to different dimensions of job \hyperref[b42]{(Wikipedia, 2009;}\hyperref[b18]{Malik et al., 2010;}\hyperref[b32]{Sattar et al., 2010)}.\par Job satisfaction of academicians is well reported and certain factors of job satisfaction have been researched over and over showing that work, pay, promotion, supervision, co-workers and environment collectively defines the job satisfaction of the employees \hyperref[b3]{(Bas and Ardic, 2002;}\hyperref[b31]{Santhapparaj and Alam, 2005;}\hyperref[b4]{Beyth-Marom, et. al., 2006;}\hyperref[b11]{Eker, et. al., 2007;}\hyperref[b18]{Malik et al., 2010)}. Literature also provides evidence that employees express diverse attitudes about these factors of satisfaction due to their demographic diversities \hyperref[b32]{(Sattar et al., 2010)}. Different surveys are coming up with a variety of results where some demographics are emerging as having significant implications while other attributes have no or little impacts on the responses. \section[{a) Job Satisfaction (js)}]{a) Job Satisfaction (js)}\par Job satisfaction is a general attitude which is determined by the factors of job satisfaction (such as, pay, work, superior's attitude, environment etc.); personal characteristics of the worker (demographics); and social or group factors {\ref (Shajahan and Shajahan, 2004:116)}. People working in the private or government organization have certain needs to satisfy, which must be understood by the human resource management of the respective organization like university (Malik, Nawab, Naeem, and Danish, 2010).\par Job satisfaction is defined as the contentment felt of the workers after a need is fulfilled \hyperref[b43]{(Williams and Sandler 1995;} {\ref Robins, 1998: 170)}. It is a general attitude which is determined by the job predictors (i.e. pay, job, superior behavior and environment etc.) and the personal attitude (demographics) and other social and group factors {\ref (Shajahan and Shajahan, 2004:116)}. People working in the private or government organization bring with them certain needs that affect their performance therefore, understanding how these needs are related with performance and how rewards can lead to job-satisfaction are the urgent issues for every organization working at any level \hyperref[b32]{(Sattar et al., 2010;}\hyperref[b18]{Malik et al., 2010)}.\par Research on the role of demographic factors in determining organizational attitudes is going on across the world by using a variety of statistical tools \hyperref[b3]{(Bas \& Ardic, 2002;}\hyperref[b33]{Shah and Jalees, 2004;} {\ref Smith, Candall, and Hulin, 1969;}\hyperref[b23]{Oshagbemi, 1999)}. Similarly, 'regression tools' have been used to predict worker behavior wherein both demographics and factors of job satisfaction has been used as predictors \hyperref[b31]{(Santhapparaj and Alam, 2005;}\hyperref[b6]{Chughtai and Zafar, 2006;}\hyperref[b4]{Beyth-Marom et al., 2006;} {\ref Karimi, 2007;}\hyperref[b11]{Eker et al., 2007)}. It is therefore critical for every HEI to perform demographic analysis and then use the findings in decision making thereby increasing the chances of job satisfaction \hyperref[b29]{(Saifuddin et al., 2010)}. \section[{b) Demographic Impacts on Job Satisfaction}]{b) Demographic Impacts on Job Satisfaction}\par Several studies have explored the demographic attributes by using them as predictors of organizational attitudes, for example, gender, sector, designation, marital status, age, qualification, and experience \hyperref[b30]{(Saiyadain, 1998;}\hyperref[b20]{Naval and Srivastava, 2004)}. The catalyst role of employee's personal attributes and demographic characteristics is recorded by almost every researcher on job satisfaction. Almost all the researchers have identified 'demographics' as the change agents, which modify employee's attitude towards different aspects of his/her job \hyperref[b3]{(Bas and Ardic, 2002;}\hyperref[b9]{DeVane and Sandy, 2003)}.\par Demographics also affect workers attitudes in terms of productivity, involvement and commitment on one hand and on the other hand the degrees of absenteeism and turnover or intention to leave (Shamil and Jalees, 2004). Another group of researchers have recorded that age, gender, experience, department, foreign qualification or exposure to different culture, and technological challenges always influence the overall satisfaction of the employees \hyperref[b40]{(Tella, Ayeni, and Popoola, 2007;}\hyperref[b1]{Asadi, et al., 2008;}\hyperref[b32]{Sattar et al., 2010;}\hyperref[b18]{Malik et al., 2010)}.\par So there are several demographic variations among the workforce which influence the degrees of satisfaction from pay, supervision, work, and environment etc. for example sector (public and private), age, gender, education, qualifications, length of service and marital status etc. of the workers have widely been found critical in determining the satisfaction level \hyperref[b28]{(Rocca, and Kostanski, 2001} Given that, researchers have also identified the impact of demographic variables on overall job satisfaction and its different elements and workers related attitude such as, pay, coworkers, supervision, promotions, physical conditions, teaching and research, governance, productivity, involvement, and commitment in different work settings including academic environments \hyperref[b22]{(Oshagbemi, and Hickson, 2003;}\hyperref[b24]{Oshagbemi, 2003} ii. \section[{Analysis III}]{Analysis III}\par The third variable tested for demographic implications was absenteeism and turnover. Again gender is most important rather only factor, which divides the respondents (?=.325, p<0.05). There is difference of opinion between the males and females about the nature and process of absenteeism and turnover. They have different experiences about this variable therefore hold diverse attitudes. The best fit therefore is: A\&T= a+? 1GDR +e A\&T= 4.369+.325+1.13397 V. VI. \section[{Summary of Analysis}]{Summary of Analysis} \section[{Discussion}]{Discussion}\par Table \hyperref[tab_12]{12} gives interesting findings with regard to the roles played by the demographic attributes of the academicians in HEIs of KPK. The leading points to revisit gender the single most important predictor of all the research variables having significant influence on overall job satisfaction and its outcomes. Male and females are significantly different in their opinion with respect to overall job satisfaction, involvement and commitment as well absenteeism and turnover. These findings are also in the line of \hyperref[b21]{Okpara, Squillace, and Erondu, (2005)}, in United States where they have identified gender discrimination in higher education and provide evidence that male teachers were more satisfied than their female counter parts. Gender differences are also found by previous researchers in their different cultural perspective like Base and Ardic (2002), in Turkey and Shah, and Jalees, (2004) in Pakistan.\par Therefore, the issue should be carefully managed and review the policy of the carefully because 'women in higher education have contributed significant progress' \hyperref[b21]{(Okpara, Squillace, and Erondu, 2005)}. Writers have not only sought to describe where women are within the academic but also to put forward explanations for that position and the differing perspectives offered by economists, sociologists, feminists, and management theorists-provide numerous and potentially conflicting explanations of the gender differences in academia \hyperref[b35]{(Shaw and Cassell, 2007)}.\par Second critical factor is the classification of public and private institutions. The respondents have difference of opinion about both job satisfaction and involvement and commitment. This also supports previous studies like public sector Greek educators were found more satisfied from their compensation and supervisor in comparison to their colleagues from the private sector {\ref (Tsigilis, et.al. 2006}). In Turkey public and private university teachers have also reported significant differences about their satisfaction and suggested to review the personnel policies of the public universities because private universities academicians appear to be significantly different from the public university teachers \hyperref[b3]{(Bas, and Ardic. 2002)}. Since the conditions of private and state owned universities are different in so many aspects, it is meaningful to conduct the research with respect to the satisfaction of their employees separately \hyperref[b14]{(Kusku, 2003)}.\par Marital status of the respondents is significant in determining the job satisfaction and has no role in other hypothesized regression models. It has been found in a previous literature that marital status significantly affect job satisfaction and explore that, when marriage time increases, the job and life satisfaction also increased \hyperref[b10]{(Dikmen, 1995;}\hyperref[b2]{Azalea, Omar, and Mastor, 2009)}. Their results show that "married employees are less satisfied as compare to unmarried". However, the results of Greek academics were found no statistical significant influence of marital status on the job satisfaction \hyperref[b25]{(Platsidou, and Diamantopoulou, 2009)}.\par In addition, it was identify by Alt?nok, (2011), in the public universities in "Ankara province" that marital status significantly affect the life and job satisfaction. Their result reveals that academicians concerning job and life satisfaction feel negativity of being married and the unmarried academic personnel have a higher life and job satisfaction than the married ones. Nevertheless, the results are contradictory in Pakistan where researchers found that marital status has emerged as a consistent predictor of organizational commitment. They reports that married people have more family responsibilities and need more stability and security in their jobs. Therefore research shows in Pakistan that marital status would be positively related to university teachers' commitment \hyperref[b6]{(Chughtai and Zafar, 2006)}. Furthermore department, Designation, Qualification and Age has been found having no effect whatsoever in any of the regression models applied on all three test-variables. \section[{VII.}]{VII.} \section[{Conclusions}]{Conclusions}\par Although the impacts of demographics are widely reported as the significant predictors of the employee attitudes, the current study however, gives surprising results, which are quite contrary to the hypothesized models. Out of seven demographic attributes tested; only three have emerged as critical. Rest of the four factors is playing no role in predicting the values of dependent variables. It is however, alarming that gender differences surface as the most obvious factor showing diversities between males and females. Perhaps it is because of the current political situation in the country.\par The difference of opinion between the respondents from public and private sector institutions is also important and denoting several implications. This classification is predictive of variance in job satisfaction and involvement and commitment. However, it has no role in explaining the absenteeism and turnover. The factor has positive impacts as compared to the mixed role of gender. Finally, the marital status of the respondents has implications for the job satisfaction and relations with the involvement \& commitment as well as absenteeism \& turnover. It is therefore concluded that demographic impacts on the attitudes of academicians in the HEIs of KPK are not significant in terms of their number. However, those few, which have implications, are sensitive; demanding careful handling to keep the related-decisions up and right.\begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-2.png} \caption{\label{fig_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1}\includegraphics[]{image-3.png} \caption{\label{fig_1}Figure 1}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.012962962962962963\textwidth}P{0.31111111111111106\textwidth}P{0.08703703703703704\textwidth}P{0.21481481481481482\textwidth}P{0.09444444444444444\textwidth}P{0.12962962962962962\textwidth}} \tabcellsep Factors\tabcellsep \tabcellsep Groups\tabcellsep Frequency\tabcellsep Percent\\ 1\tabcellsep \multicolumn{2}{l}{Gender -GDR}\tabcellsep Female Male\tabcellsep 74 144\tabcellsep 33.9 66.1\\ 2\tabcellsep \multicolumn{2}{l}{Department -DPT}\tabcellsep Sciences Non-Sciences\tabcellsep 122 96\tabcellsep 56.0 44.0\\ 3\tabcellsep \multicolumn{2}{l}{Public vs. Private -PPR}\tabcellsep Public Private\tabcellsep 169 49\tabcellsep 77.5 22.5\\ 4\tabcellsep \multicolumn{2}{l}{Marital Status -MST}\tabcellsep Married Unmarried\tabcellsep 121 97\tabcellsep 55.5 44.5\\ 5\tabcellsep \multicolumn{2}{l}{Designation -DSG}\tabcellsep AP\&ASP Lecturer\tabcellsep 84 134\tabcellsep 38.5 61.5\\ 6\tabcellsep \multicolumn{2}{l}{Qualification -QUA}\tabcellsep MPhil/PhD Masters\tabcellsep 71 147\tabcellsep 32.6 67.4\\ 7\tabcellsep AGE\tabcellsep \tabcellsep 31-Above 20-30\tabcellsep 96 122\tabcellsep 44.0 56.0\\ \tabcellsep \multicolumn{5}{l}{b) Regression of Demographics on Job Satisfaction (JS)}\\ \tabcellsep i.\tabcellsep \multicolumn{3}{l}{Models, Coefficients \& Excluded Variables (JS)}\end{longtable} \par \caption{\label{tab_1}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.06737804878048781\textwidth}P{0.05701219512195122\textwidth}P{0.4275914634146341\textwidth}P{0.0725609756097561\textwidth}P{0.1114329268292683\textwidth}P{0.04923780487804878\textwidth}P{0.06478658536585366\textwidth}} Model\tabcellsep R\tabcellsep R Square\tabcellsep Adjusted R\tabcellsep Std. Error of\tabcellsep F\tabcellsep Sig.\\ \tabcellsep \tabcellsep \tabcellsep Square\tabcellsep the Estimate\tabcellsep \tabcellsep \\ 1\tabcellsep .464(a)\tabcellsep .215\tabcellsep .211\tabcellsep .61924\tabcellsep 59.200\tabcellsep .000(a)\\ 2\tabcellsep .485(b)\tabcellsep .235\tabcellsep .228\tabcellsep .61275\tabcellsep 33.029\tabcellsep .000(b)\\ 3\tabcellsep .508(c)\tabcellsep .258\tabcellsep .248\tabcellsep .60471\tabcellsep 24.862\tabcellsep .000(c)\\ Detail of\tabcellsep \tabcellsep \multicolumn{2}{l}{a Predictors: (Constant), GDR}\tabcellsep \tabcellsep \tabcellsep \\ the\tabcellsep \tabcellsep \multicolumn{3}{l}{b Predictors: (Constant), GDR, MST}\tabcellsep \tabcellsep \\ Models\tabcellsep \tabcellsep \multicolumn{3}{l}{c Predictors: (Constant), GDR, MST, PPR}\tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \multicolumn{3}{l}{d Dependent Variable: Job Satisfaction (JS)}\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_2}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.02125\textwidth}P{0.15140625\textwidth}P{0.17796875\textwidth}P{0.12218749999999999\textwidth}P{0.14078125\textwidth}P{0.1328125\textwidth}P{0.10359375\textwidth}} Model\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \\ \tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep \\ \tabcellsep Variables\tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep t\tabcellsep Sig\\ 1\tabcellsep (Constant)\tabcellsep 3.890\tabcellsep .072\tabcellsep \tabcellsep 54.036\tabcellsep .000\\ \tabcellsep GDR\tabcellsep .681\tabcellsep .089\tabcellsep .464\tabcellsep 7.694\tabcellsep .000\\ 2\tabcellsep (Constant)\tabcellsep 3.695\tabcellsep .109\tabcellsep \tabcellsep 33.962\tabcellsep .000\\ \tabcellsep GDR\tabcellsep .817\tabcellsep .105\tabcellsep .556\tabcellsep 7.803\tabcellsep .000\\ \tabcellsep MST\tabcellsep .236\tabcellsep .100\tabcellsep .169\tabcellsep 2.366\tabcellsep .019\\ 3\tabcellsep (Constant)\tabcellsep 3.751\tabcellsep .109\tabcellsep \tabcellsep 34.262\tabcellsep .000\\ \tabcellsep GDR\tabcellsep .790\tabcellsep .104\tabcellsep .538\tabcellsep 7.603\tabcellsep .000\\ \tabcellsep MST\tabcellsep .288\tabcellsep .100\tabcellsep .205\tabcellsep 2.863\tabcellsep .005\\ \tabcellsep PPR\tabcellsep -.268\tabcellsep .103\tabcellsep -.161\tabcellsep -2.600\tabcellsep .010\end{longtable} \par \caption{\label{tab_3}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.028176795580110495\textwidth}P{0.05635359116022099\textwidth}P{0.17845303867403314\textwidth}P{0.09861878453038674\textwidth}P{0.09392265193370165\textwidth}P{0.17375690607734806\textwidth}P{0.22071823204419888\textwidth}} Model\tabcellsep \tabcellsep Beta In\tabcellsep T\tabcellsep Sig.\tabcellsep Partial\tabcellsep Collinearity\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Correlation\tabcellsep Statistics\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Tolerance\\ 3\tabcellsep DPT\tabcellsep -.064(c)\tabcellsep -1.076\tabcellsep .283\tabcellsep -.074\tabcellsep .995\\ \tabcellsep DSG\tabcellsep -.024(c)\tabcellsep -.391\tabcellsep .696\tabcellsep -.027\tabcellsep .932\\ \tabcellsep QUA\tabcellsep -.046(c)\tabcellsep -.761\tabcellsep .448\tabcellsep -.052\tabcellsep .944\\ \tabcellsep AGE\tabcellsep .067(c)\tabcellsep .893\tabcellsep .373\tabcellsep .061\tabcellsep .618\end{longtable} \par \caption{\label{tab_4}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.07958801498127341\textwidth}P{0.047752808988764044\textwidth}P{0.2228464419475655\textwidth}P{0.2769662921348315\textwidth}P{0.11779026217228465\textwidth}P{0.047752808988764044\textwidth}P{0.05730337078651685\textwidth}} Model\tabcellsep R\tabcellsep R\tabcellsep Adjusted R\tabcellsep Std. Error of\tabcellsep F\tabcellsep Sig.\\ \tabcellsep \tabcellsep Square\tabcellsep Square\tabcellsep the Estimate\tabcellsep \tabcellsep \\ 1\tabcellsep .675(a)\tabcellsep .456\tabcellsep .453\tabcellsep .95729\tabcellsep 180.753\tabcellsep .000(a)\\ 2\tabcellsep .752(b)\tabcellsep .566\tabcellsep .561\tabcellsep .85715\tabcellsep 139.933\tabcellsep .000(b)\\ Detail of\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{a Predictors: (Constant), GDR}\tabcellsep \\ the\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{b Predictors: (Constant), GDR, PPR}\tabcellsep \\ Models\tabcellsep \tabcellsep \multicolumn{4}{l}{c Dependent Variable: Involvement and Commitment (I\&C)}\tabcellsep \end{longtable} \par \caption{\label{tab_5}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.027803738317757006\textwidth}P{0.11518691588785045\textwidth}P{0.21051401869158878\textwidth}P{0.11915887850467288\textwidth}P{0.1628504672897196\textwidth}P{0.12313084112149532\textwidth}P{0.09135514018691589\textwidth}} Model\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \\ \tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep \\ \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep t\tabcellsep Sig\\ 1\tabcellsep (Constant)\tabcellsep 3.020\tabcellsep .111\tabcellsep \tabcellsep 27.141\tabcellsep .000\\ \tabcellsep GDR\tabcellsep 1.841\tabcellsep .137\tabcellsep .675\tabcellsep 13.444\tabcellsep .000\\ 2\tabcellsep (Constant)\tabcellsep 3.406\tabcellsep .113\tabcellsep \tabcellsep 30.268\tabcellsep .000\\ \tabcellsep GDR\tabcellsep 1.617\tabcellsep .126\tabcellsep .593\tabcellsep 12.799\tabcellsep .000\\ \tabcellsep PPR\tabcellsep -1.057\tabcellsep .143\tabcellsep -.342\tabcellsep -7.377\tabcellsep .000\end{longtable} \par \caption{\label{tab_6}Table 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{7} \par \begin{longtable}{P{0.23350449293966624\textwidth}P{0.06328626444159179\textwidth}P{0.08729139922978177\textwidth}P{0.10365853658536585\textwidth}P{0.03055198973042362\textwidth}P{0.2302310654685494\textwidth}P{0.045827984595635425\textwidth}P{0.05564826700898588\textwidth}} \tabcellsep Model\tabcellsep \tabcellsep Beta In\tabcellsep T\tabcellsep Sig.\tabcellsep Partial\tabcellsep Collinearity\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Correlation\tabcellsep Statistics\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Tolerance\\ \tabcellsep 2\tabcellsep DPT\tabcellsep -.065(b)\tabcellsep -1.453\tabcellsep .148\tabcellsep -.099\tabcellsep .995\\ \tabcellsep \tabcellsep MST\tabcellsep .070(b)\tabcellsep 1.278\tabcellsep .203\tabcellsep .087\tabcellsep .673\\ \tabcellsep \tabcellsep DSG\tabcellsep -.013(b)\tabcellsep -.280\tabcellsep .779\tabcellsep -.019\tabcellsep .960\\ \tabcellsep \tabcellsep QUA\tabcellsep -.069(b)\tabcellsep -1.505\tabcellsep .134\tabcellsep -.102\tabcellsep .961\\ \tabcellsep \tabcellsep AGE\tabcellsep -.047(b)\tabcellsep -.952\tabcellsep .342\tabcellsep -.065\tabcellsep .818\\ ii.\tabcellsep Analysis II\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{excluded from the models through stepwise multiple}\\ \tabcellsep \multicolumn{4}{l}{Involvement and commitment was the second}\tabcellsep \multicolumn{3}{l}{regression (See table 7). The best fit for the dependent}\\ \multicolumn{5}{l}{criterion variable tested for demographic impacts. The}\tabcellsep \multicolumn{2}{l}{variable, therefore, is:}\\ \multicolumn{5}{l}{results (table 7) shows that only gender (?=.593, p<0.05) and sector (?=-+-.342, p<0.05) are the significant factors while rest of the five factors have been}\tabcellsep \multicolumn{3}{l}{I\&C = a+? 1GDR +? 3PPR +e I\&C = 3.406+.593+-.342+.85715}\\ \tabcellsep \tabcellsep \multicolumn{6}{l}{d) Regression of Demographics on Absenteeism \& Turnover (A\&T)}\\ \tabcellsep \tabcellsep i.\tabcellsep \multicolumn{4}{l}{Models, Coefficients \& Excluded Variables (A\&T)}\end{longtable} \par \caption{\label{tab_7}Table 7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8} \par \begin{longtable}{P{0.09902912621359224\textwidth}P{0.033009708737864074\textwidth}P{0.2682038834951456\textwidth}P{0.24344660194174755\textwidth}P{0.1320388349514563\textwidth}P{0.028883495145631065\textwidth}P{0.0453883495145631\textwidth}} Model\tabcellsep R\tabcellsep R\tabcellsep Adjusted R\tabcellsep Std. Error of the\tabcellsep F\tabcellsep Sig.\\ \tabcellsep \tabcellsep Square\tabcellsep Square\tabcellsep Estimate\tabcellsep \tabcellsep \\ 1\tabcellsep .325(a)\tabcellsep .106\tabcellsep .102\tabcellsep 1.13397\tabcellsep 25.516\tabcellsep .000(a)\\ Detail of the\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{a. Predictors: (Constant), GDR (Gender)}\tabcellsep \\ Model\tabcellsep \tabcellsep \multicolumn{4}{l}{b. Dependent Variable: Absenteeism and Turnover (A\&T)}\tabcellsep \end{longtable} \par \caption{\label{tab_8}Table 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{9} \par \begin{longtable}{P{0.03984375\textwidth}P{0.08632812499999999\textwidth}P{0.23906249999999998\textwidth}P{0.11953124999999999\textwidth}P{0.2125\textwidth}P{0.0796875\textwidth}P{0.073046875\textwidth}} \tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \\ Model\tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep \\ \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep t\tabcellsep Sig\\ 1\tabcellsep (Constant)\tabcellsep 4.369\tabcellsep .132\tabcellsep \tabcellsep 33.146\tabcellsep .000\\ \tabcellsep GDR\tabcellsep .819\tabcellsep .162\tabcellsep .325\tabcellsep 5.051\tabcellsep .000\end{longtable} \par \caption{\label{tab_9}Table 9 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{10} \par \begin{longtable}{P{0.021982758620689654\textwidth}P{0.06594827586206897\textwidth}P{0.18685344827586206\textwidth}P{0.10991379310344827\textwidth}P{0.10258620689655172\textwidth}P{0.16120689655172413\textwidth}P{0.20150862068965517\textwidth}} Model\tabcellsep \tabcellsep Beta In\tabcellsep t\tabcellsep Sig.\tabcellsep Partial\tabcellsep Collinearity\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Correlation\tabcellsep Statistics\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Tolerance\\ 1\tabcellsep DPT\tabcellsep -.047(a)\tabcellsep -.726\tabcellsep .469\tabcellsep -.049\tabcellsep .997\\ \tabcellsep MST\tabcellsep .122(a)\tabcellsep 1.587\tabcellsep .114\tabcellsep .108\tabcellsep .701\\ \tabcellsep PPR\tabcellsep -.110(a)\tabcellsep -1.671\tabcellsep .096\tabcellsep -.113\tabcellsep .942\\ \tabcellsep DSG\tabcellsep .055(a)\tabcellsep .845\tabcellsep .399\tabcellsep .058\tabcellsep .978\\ \tabcellsep QUA\tabcellsep .043(a)\tabcellsep .661\tabcellsep .510\tabcellsep .045\tabcellsep .978\\ \tabcellsep AGE\tabcellsep .083(a)\tabcellsep 1.164\tabcellsep .246\tabcellsep .079\tabcellsep .823\end{longtable} \par \caption{\label{tab_10}Table 10 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11} \par \begin{longtable}{P{0.003923076923076923\textwidth}P{0.18830769230769231\textwidth}P{0.6577692307692308\textwidth}} \tabcellsep \tabcellsep Job Satisfaction\\ \tabcellsep Hypothesized Model\tabcellsep JS = a+ ? 1GDR +? 2DPT +? 3MST +? 4PPR +? 5DSG +? 6QUA +? 7AGE +e\\ 1\tabcellsep The best fit\tabcellsep JS = a+? 1GDR +? 2MST +? 3PPR+e\\ \tabcellsep \tabcellsep JS = 3.751+.538+.205+-.161+.60471\\ \tabcellsep Excluded variables\tabcellsep DPT, DSG, QUA, \& AGE\\ \tabcellsep \tabcellsep Involvement \& Commitment\\ \tabcellsep Hypothesized Model\tabcellsep I\&C = a+ ? 1GDR +? 2DPT +? 3MST +? 4PPR +? 5DSG +? 6QUA +? 7AGE +e\\ 2\tabcellsep The best fit\tabcellsep I\&C = a + ? 1GDR+ ? 3PPR+e\\ \tabcellsep \tabcellsep I\&C = 3.406+.593+-.342+.85715\\ \tabcellsep Excluded variables\tabcellsep DPT, MST, DSG, QUA, \& AGE\\ \tabcellsep \tabcellsep Absenteeism \& Turnover\\ \tabcellsep Hypothesized Model\tabcellsep A\&T = a+ ? 1GDR +? 2DPT +? 3MST +? 4PPR +? 5DSG +? 6QUA +? 7AGE +e\\ 3\tabcellsep The best fit\tabcellsep A\&T= a+? 1GDR +e\\ \tabcellsep \tabcellsep A\&T= 4.369+.325+1.13397\\ \tabcellsep Excluded variables\tabcellsep DPT, MST, PPR, DSG, QUA, \& AGE\end{longtable} \par \caption{\label{tab_11}Table 11 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{12} \par \begin{longtable}{P{0.03966666666666667\textwidth}P{0.187\textwidth}P{0.21533333333333335\textwidth}P{0.09633333333333333\textwidth}P{0.16433333333333333\textwidth}P{0.14733333333333334\textwidth}} \tabcellsep Demographics\tabcellsep \multicolumn{2}{l}{Job Satisfaction Involvement \&}\tabcellsep Absenteeism \&\tabcellsep Roles of the\\ \tabcellsep \tabcellsep \tabcellsep Commitment\tabcellsep Turnover\tabcellsep Factors\\ 1\tabcellsep GDR\tabcellsep ?\tabcellsep ?\tabcellsep ?\tabcellsep 3\\ 2\tabcellsep DPT\tabcellsep -\tabcellsep -\tabcellsep -\tabcellsep 0\\ 3\tabcellsep MST\tabcellsep ?\tabcellsep -\tabcellsep -\tabcellsep 1\\ 4\tabcellsep PPR\tabcellsep ?\tabcellsep ?\tabcellsep -\tabcellsep 2\\ 5\tabcellsep DSG\tabcellsep -\tabcellsep -\tabcellsep -\tabcellsep 0\\ 6\tabcellsep QUA\tabcellsep -\tabcellsep -\tabcellsep -\tabcellsep 0\\ 7\tabcellsep AGE\tabcellsep -\tabcellsep -\tabcellsep -\tabcellsep 0\end{longtable} \par \caption{\label{tab_12}Table 12 :}\end{figure} \footnote{© 2012 Global Journals Inc. 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