Resources are the main agents for the employees in terms of carrying out the business activities. Provision of quality resources to the personnel results enhances the productivity level. On the other hand, it is the responsibility of the employees to make effective use of the organizational resources. This is in terms of enhancing their professionalism (Silverman, 2016). This assignment attempts to shed light on analysing the data collected on the impact of job resources on job demands through the consideration of different analysis techniques.
Resources and demand are interrelated between each other. Provision of quality resources to the employees results in the exposure of better performance. However, they need to be trained about proper utilization of the organizational resources. This is in terms of achieving sustainable development. Moreover, this approach is important in terms of preventing the instances of inadequate resources at the time of executing the business activities. It is an adverse situation, when the demand from the clients is high, but the inadequate resources compels the managers to encounter turnovers from employees, clients and the customers (Hair & Lukas, 2014). Therefore, proper research is needed for establishing the links between job demand and job resources.
Analysis of the collected data is important in terms of gaining an insight into the expected outcomes regarding the role of job resources on job demands. Here, mention can be made of descriptive analysis, which transforms the raw data into central tendency, distribution and variability. Along with this, tabulation is also used for completing the process of data analysis. This method involves the tabular representation of the data, representing the number of responses. According to Smith, (2015), tallying is one of an important activities for projecting the accurate number of responses for the number of job resources and demands. The tallying method is appropriate in terms of developing accurate, reliable and valid results for the impact of job resources on the increasing the demands for employment. Within this, mention can be made of the frequency table, which represents the different ways in which the respondents answered the questions. This tabulation analysis method can be contradicted with cross tabulation method, which excavates the answers for the typical research questions. These questions exposes the relationship between the variables, job demand and job resources (Bernard, Wutich & Ryan, 2016). The results regarding job resources would be presented in rows and the results regarding the job demands would be projected in the form of columns.
One of the other methods of analysing the data is preparation of contingency table. Here, data matrix is used for displaying the frequency related to the combination of responses. This matrix is useful for gaining an insight into the time when the job demands are high and through which resources. Within this, the percentile of the respondents attains statistical representation. The next stage in this aspect is elaboration and refinement for the statistical representation. In this, the results derived in the cross tabulation is analysed for deducing relevant results regarding the impact of job resources on job demands (Gibbs, 2018). In the technique of elaboration analysis, categorizing the samples helps in the achieving a deeper understanding of the results.
Incorporation of moderator variable in this context alters the relationship between the two variable. This incorporation adds hypothetical parameter to the correlation between job demand and job resource. Herein, job demand-resource model can be brought into the discussion. This model presents a strain in the responses regarding the conditions of resources and the demands. As per the arguments of Fusch and Ness, (2015), the imbalances between the demands and the resources contradicts the conglomeration between demands and resources in the workplace. This model was introduced for counter-arguing the models on employee wellbeing.
According to the assumptions of the model, job demands and job resources are important components for risk factors and stress. Job demands includes psychological, social and organizational business aspects, which needs specific efforts and skills. Typical example of this is the emotions, which destroys the balance between the personal and professional life. Resources occupy an important position in this model. This is because physical, social and organizational resources help the employees in fulfilling the identified goals and objectives (Bandara et al., 2015). If the company emerges successful in meeting the targets, the professionalism of the employees is enhanced.
Within the workplace, employees belong to different socio-cultural backgrounds. This means that their opinions regarding the workplace activities would be different. Therefore, in terms of data analysis, appropriate techniques need to be applied. In this context, cross tabulation method would be applicable, as there would be many possible responses regarding increasing the job demands through the resources. In the process of answering the question, every possible response is categorized as the explanatory variable. Moreover, cross tabulation is appropriate due to the presence of two categorical variables, job demand and job resources. Countering this, correlation can be established between job demands and job resources (Yang et al., 2018). This is in terms of excavating the impact, which resources have on increasing the demands for employment levels.
Segregation of two rating scale into four helps in providing a deeper understanding about the responses. However, it aggravates the complexities in terms of deriving the results. These aspects relates with the characteristics of quadrant analysis. In this, focus is placed on the graphical representation of the performance exposed in the survey on the role of resources in upgrading the standards and quality of their performance.
As per the role of resources in the workplace, data transformation is the most appropriate. This is in terms of the objectives, which helps in increasing the revenue and the profit margin. Gehman et al. (2017) opines that radical transformation in the raw data helps in gaining an insight into the current trends related to the significance of resources on enhancing the productivity. Analysis of this transformed data might destroy the originality of the data. Moreover, it might be the case that this transformation would not lead to the desired and expected outcome.
Segregation of the data into two categories below and above the medium adds hypothetical approach to the process of data analysis. This reflects the bimodal characteristics, where median split is the most appropriate one. Along with this, Johnston, (2017) is of the view that inappropriate collapsing of the variables in a continuity contradicts the categorical collaboration of the information into the values, which are untransformed. Therefore, if job demand and job resources are clubbed into the same category, then it aggravates the complexities in terms of deducing the results related to the impact of the resources on the employment levels.
Scoring the responses, regarding the role of resources in the increasing the demand for jobs, helps in gaining an insight into the base number. This insight is assistance in terms of calculating the rank orders in which the data can be placed after the transformation. However, Elo et al. (2014) argues that multiplication of the frequency by the ranking score enhances the awareness about the choices, which the samples have. Consideration of these choices results in the introduction of new scale.
Along with this, mention can be made of the computer programs, which helps in analysing the collected data. As a matter of specification, these methods include spreadsheets for graphical representation of the data in Microsoft Excel. Apart from this, statistical software also helps in carrying out the analysis process. This is done in SPSS, SAS, MINITAB among others. In this case, SPSS seems appropriate in terms of projecting the results regarding the role of resources on increasing the employment levels.
Mention can also be made of computer graphics, which adds variability into the shapes of frequency distributions (Brannen, 2017). Consideration of interquartile range is assistance in terms of measuring the variability within the responses. In this context, motivation is a related content, however, it is an outlier in terms of the variables, job resources and demands.
Interpretation is the stage after analysing the results. These interpretations assists in taking important decisions related to the management of labours. From the perspective of management, using qualitative data analysis is appropriate. Typical examples are content analysis, narrative analysis, discourse analysis, framework analysis and grounded theory. Content analysis classifies the verbal and behavioural data regarding the impact, which resources have on the demands for jobs. Typical components of this technique are classification, summarization and tabulation (Bryman & Bell, 2014). In this process, the content or the data is analysed in two levels: descriptive and interpretative. In descriptive, the research revolves around exploring the basic concepts related to the data. In interpretive, attempts are made to interpret relevant results from the analysed data.
Narrative analysis is one of the other techniques through which the collected data is analysed. In this techniques, narratives are transcribed and presented in the form of experiences. As a matter of specification, this technique involves revision of the responses in a presentable form to the readers. However, Walliman, (2017) is of the view that originality needs to be maintained within the experiences narrated by the samples. This technique would be followed by the researcher to narrate the experiences of the managers regarding the role of resources on increasing the employment demands.
Mention can also be made of discourse analysis, which helps in analysing the interviews and written texts on different subject matters. Main focus of this type of analysis is on excavating the role of language in improving the quality of lifestyle for the people. Within this, there are three conditions:
This technique, if followed, might add hypothetical parameters to the responses of the managers regarding the impact, which organizational resources have on the rise and fall of the employment demands (Bryman, 2017). However, exposing rational approach in terms of analysing the responses of the managers, would lead to the efficient and effective interpretations. Graphical representations can be effective in terms of analysing the narration produced by the managers. On the contrary, if the researcher bears in mind the aims and objectives, then the results would be according to the context, that is, the impact of resources on increase of the employment demands.
According to the context of the research, framework analysis can be an appropriate one. This is in terms of the integrated process, which produces reliability, validity and accuracy within the process of data analysis. At the initial stage, the researcher needs to gain familiarity with the basic concepts related to the subject matter of the research (Banks, 2018). As a sequential step, a thematic framework needs to be identified. Relevant themes would assist the readers to achieve an understanding about the role of the resources on the employment demands. The researcher needs to consider a-priori and emergent issues, which are crucial for deducing relevant results. The next step is coding, which would enhance the visual skills of the readers. Synthesizing the numerical and textual codes helps in deducing relevant results. Mapping the responses of the managers is assistance in terms of clarifying the basic concepts relates to the linkage between job resources and the job demands. Moreover, Banks, (2018) argues that mapping the responses in 3D representation would enhance the thought processes of the readers regarding the job demands, which may or may be due to the organizational resources.
Apart from this, one of the other techniques of analysing the data is grounded theory. The primary step in this theory is analytic induction, which examines the stereotypical case studies and public opinions related to the subject matter of the research. After this, comparative study is developed to assess the effectiveness, appropriateness and feasibility of the public opinions. Upon discovery of negative results, other case studies are selected for conducting the thematic analysis (Bryman, 2017). Here, the researcher is provided with two options: alteration of the case studies to fit the selected case study or the opinions of the population is changed for omitting any kind of relevance to the emergent issues.
The selection of the case study is a continuous process, until relevancy is developed within the research. Here, the main focus is on the development of a statement, which bears resemblance with the public opinions regarding the subject matter. However, this theory is applicable only for the analytical problems, which can be resolved through general statements.
After reviewing all of the data analysis techniques, it can be said that framework analysis is the most appropriate solution. This is because of the integrated structure, which would help the researcher to interpret relevant results from the collected data (Walliman, 2017). Familiarization with the basic concepts of the research can be achieved through the literary opinions related to the impact of job resources on job demands. From these opinions, relevant themes can be developed, which would broaden the scope and arena of the readers’ thoughts. Coding the themes, through the means of mapping would enhance the awareness about the increase in job demands through the resources. Statistical data in this context would add relevancy into the propositions and assumptions.
Viewing it from other perspective, framework analysis can be considered as similar to that of narrative analysis. This is because the narrations of the responses are transformed into graphical representation, before presenting the final results to the readers (Bryman & Bell, 2014). Emerging capable in maintaining the originality in the responses of the managers and context of the research would reflect the ethical approach of the researcher. This is in terms of averting the instances of pressurizing the responses.
Conclusion
Data analysis is a crucial activity for deducing relevant results related to the subject matter of the research. There are different ways of analysing a data, such as narrative analysis, framework analysis, grounded theory among others. Proper evaluation helps the researcher in selecting the appropriate method for analysing the collected responses. Proper results are discovered through transformation of the raw data into statistical and graphical representations. However, maintaining the originality in the context and the responses of the samples, reflects the ethical approach of the researcher. On the contrary, altering the responses of the samples for deducing the results, might lead to hypothetical results. These results would, further, aggravate the complexities in terms of interpreting results regarding the impact, which resources have on the increase in the job demands.
References
Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., & Beekhuyzen, J. (2015). Achieving rigor in literature reviews: Insights from qualitative data analysis and tool-support. Communications of the Association for Information Systems, 37, 154-204.
Banks, M. (2018). Using visual data in qualitative research(Vol. 5). Sage.
Bernard, H. R., Wutich, A., & Ryan, G. W. (2016). Analyzing qualitative data: Systematic approaches. SAGE publications.
Brannen, J. (2017). Mixing methods: Qualitative and quantitative research. Routledge.
Bryman, A. (2017). Quantitative and qualitative research: further reflections on their integration. In Mixing methods: Qualitative and quantitative research (pp. 57-78). Routledge.
Bryman, A., & Bell, E. (2014). Research methodology: Business and management contexts. Oxford University Press Southern Africa.
Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative content analysis: A focus on trustworthiness. SAGE open, 4(1), 2158244014522633.
Fusch, P. I., & Ness, L. R. (2015). Are we there yet? Data saturation in qualitative research. The qualitative report, 20(9), 1408-1416.
Gehman, J., Glaser, V. L., Eisenhardt, K. M., Gioia, D., Langley, A., & Corley, K. G. (2017). Finding theory–method fit: A comparison of three qualitative approaches to theory building. Journal of Management Inquiry, 1056492617706029.
Gibbs, G. R. (2018). Analyzing qualitative data (Vol. 6). Sage.
Hair Jr, J. F., & Lukas, B. (2014). Marketing research (Vol. 2). McGraw-Hill Education Australia.
Johnston, M. P. (2017). Secondary data analysis: A method of which the time has come. Qualitative and Quantitative Methods in Libraries, 3(3), 619-626.
Quinlan, C., Babin, B., Carr, J., & Griffin, M. (2019). Business research methods. South Western Cengage.
Silverman, D. (Ed.). (2016). Qualitative research. Sage.
Smith, J. A. (Ed.). (2015). Qualitative psychology: A practical guide to research methods. Sage.
Walliman, N. (2017). Research methods: The basics. Routledge.
Yang, Y., Pankow, J., Swan, H., Willett, J., Mitchell, S. G., Rudes, D. S., & Knight, K. (2018). Preparing for analysis: a practical guide for a critical step for procedural rigor in large-scale multisite qualitative research studies. Quality & Quantity, 52(2), 815-828.
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