MRES902 Quantitative Research Methods

Questions:

Question 1: Research Design

1. Identify and justify the research design presented in this study.

2. Explain the limitations of this proposed research design, using some relevant academic literature.

Question 2: Sampling

1. Identify and explain the sampling method proposed by the consultant.

2. Discuss the limitations of this sampling approach.

3. Discuss how the consultant could further improve the sampling strategy to increase the validity. Use some relevant literature to support your claims.

Question 3: Variables and Measurements

1. Categorise the variables collected in this case study into nominal, ordinal, interval, and ratio scales.

2. Identify the independent and dependent variables presented in this case.

3. Identify and categorise the data collection methods used in the study.

Question 4: Control Variables and Statistical Reasoning

1. List out the proper control variables that should be included when examining the association between remote workdays and productivity.

2. Critically discuss why the inclusion of these control variables would enhance the validity and credibility of the findings of this study.

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Rubric for AT1 Practical Portfolio

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MRES902 Quantitative Research Methods

AT1 Practical Portfolio – Sample Solution

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Question 1: Research Design

1.1 Identification and Justification of Research Design

The research design employed in this study is a cross-sectional survey design situated within a quantitative research paradigm. A cross-sectional design collects data from a population at a single point in time, allowing the researcher to examine relationships between variables without manipulating them (Creswell & Creswell, 2018). This design is appropriate for the present study for several reasons.

First, the primary objective of the study is to examine associations between hybrid work arrangements and employee outcomes such as well-being, job satisfaction, and productivity. Cross-sectional designs are well-suited to investigating such associations in organisational settings where longitudinal data collection is not feasible within project timelines (Bryman, 2016). Second, the use of a standardised survey instrument to collect data on multiple variables simultaneously is consistent with the deductive, positivist approach that underpins quantitative research (Saunders et al., 2019).

Furthermore, the study incorporates elements of a mixed-methods design by supplementing the primary quantitative survey with brief qualitative interviews with line managers and secondary data review (2024 Australian Workplace Wellbeing Report). However, the dominant paradigm remains quantitative, as the core analytical focus is on measuring and statistically examining relationships between variables (Johnson & Onwuegbuzie, 2004).

1.2 Limitations of the Research Design

Despite its suitability, the cross-sectional design presents several notable limitations.

The most significant limitation is the inability to establish causality. Because data are collected at one point in time, it is impossible to determine whether hybrid work causes changes in productivity or well-being, or whether pre-existing individual characteristics drive both the choice to work remotely and productivity outcomes (Shadish et al., 2002). This is a fundamental weakness of cross-sectional designs identified extensively in the literature (Bryman, 2016).

Second, the study is subject to common method bias, as both independent and dependent variables are self-reported by the same respondents at the same time, which can artificially inflate observed correlations (Podsakoff et al., 2003). Third, by surveying only employees at the Sydney office of a single organisation, the findings may lack external validity and generalisability to other Australian workplaces or sectors (Sekaran & Bougie, 2016).

Question 2: Sampling

2.1 Identification of Sampling Method

The sampling method proposed by the consultant is convenience sampling, a non-probability sampling technique in which participants are selected based on their availability and accessibility (Etikan et al., 2016). In this study, employees from the organisation's Sydney office are surveyed simply because they are the most accessible population to the consultant. No random selection process is described, and the sample is constrained to a single geographic location and organisation.

2.2 Limitations of the Sampling Approach

Convenience sampling, while practical and cost-effective, carries several methodological limitations.

Most critically, convenience samples are not representative of the broader population of Australian employees in hybrid work environments. The restriction of sampling to a single Sydney office means that employees in other states, industries, or organisational cultures are entirely excluded, severely limiting the external validity of the findings (Bryman, 2016). Additionally, employees who are more engaged or satisfied with hybrid work may be more willing to participate, introducing self-selection bias that skews results toward more favourable outcomes (Saunders et al., 2019).

Furthermore, the small and homogenous sample (a single organisation, single city) reduces statistical power and increases the risk that findings reflect idiosyncratic organisational factors rather than generalisable patterns (Sekaran & Bougie, 2016).

2.3 Strategies to Improve Sampling

To improve the validity and representativeness of the sampling strategy, the consultant should consider the following evidence-based approaches.

Stratified random sampling would be the most appropriate improvement. This technique divides the population into distinct subgroups (strata) — such as job level, department, gender, and employment status — and then randomly selects participants from each stratum proportionally (Creswell & Creswell, 2018). This ensures all key subgroups are represented and enables more meaningful subgroup comparisons, significantly enhancing internal and external validity.

Additionally, expanding the sample beyond a single Sydney office to include employees from multiple Australian states and a range of industries would substantially improve generalisability (Bryman, 2016). A multi-organisational or multi-site design, though more resource-intensive, would better capture the diversity of hybrid work experiences across the Australian workforce (Felstead & Henseke, 2017).

Question 3: Variables and Measurements

3.1 Variable Categorisation by Measurement Scale

The variables in this study can be categorised according to Stevens' (1946) typology of measurement scales as follows:

Note: Job Satisfaction and Work-Life Balance are treated as ordinal because Likert scale ratings, while often treated as interval in practice, technically represent ordered categories without guaranteed equal intervals (Norman, 2010).

3.2 Independent and Dependent Variables

Independent Variable (IV): The primary independent variable is the number of remote workdays per week, which represents the extent of hybrid work adoption. In the second phase, number of work hours per week serves as an additional independent variable.

Dependent Variables (DV): The key dependent variables are employee productivity (measured via the internal performance rating scale), job satisfaction, and perceived work-life balance. These are the outcomes the study aims to explain.

3.3 Data Collection Methods

Three data collection methods are used in this study, each serving a distinct purpose:

• Primary Quantitative Survey: A structured questionnaire administered to employees collects data on demographic variables and outcome measures (well-being, job satisfaction, productivity). This is the dominant method, consistent with the quantitative research design.
• Qualitative Interviews: Brief semi-structured interviews with line managers provide contextual, interpretive data to complement the survey findings. This introduces a qualitative element into the study design.
• Secondary Data Review: Analysis of the 2024 Australian Workplace Wellbeing Report provides external benchmarking data and supports triangulation of findings, enhancing the credibility and contextual validity of the study (Creswell & Creswell, 2018).

Question 4: Control Variables and Statistical Reasoning

4.1 Control Variables

When examining the association between the number of remote workdays and employee productivity, the following variables should be controlled for to isolate the effect of the independent variable:

4.2 Why Control Variables Enhance Validity and Credibility

The inclusion of control variables is essential to minimising the threat of confounding — a situation in which a third variable correlates with both the independent and dependent variables, producing a spurious association (Field, 2018). Failure to control for confounding variables is one of the most common threats to the internal validity of quantitative research (Shadish et al., 2002).

For example, without controlling for job level, an observed positive relationship between remote workdays and productivity may simply reflect the fact that senior employees both have more autonomy to work remotely and have inherently higher performance expectations — making it appear that remote work drives productivity when it does not (Bloom et al., 2015). Similarly, failing to control for age or digital literacy could mask the fact that younger, more digitally proficient employees both prefer and perform better in remote environments, confounding the remote work–productivity relationship (Wang et al., 2021).

By statistically controlling for these variables — through techniques such as multiple regression analysis — the consultant can partial out their effects and obtain a cleaner, more credible estimate of the independent contribution of remote workdays to productivity (Tabachnick & Fidell, 2019). This approach not only strengthens internal validity but also enhances the credibility of evidence-based recommendations to senior management by reducing the likelihood that policy decisions are made on the basis of spurious findings (Saunders et al., 2019).

In summary, the systematic inclusion of theoretically justified control variables reflects best practice in quantitative organisational research and is essential for producing findings that are both statistically defensible and practically meaningful (Sekaran & Bougie, 2016).

References

Alon, T., Doepke, M., Olmstead-Rumsey, J., & Tertilt, M. (2020). The impact of COVID-19 on gender equality. National Bureau of Economic Research. https://doi.org/10.3386/w26947

Bloom, N., Liang, J., Roberts, J., & Ying, Z. J. (2015). Does working from home work? Evidence from a Chinese experiment. The Quarterly Journal of Economics, 130(1), 165–218. https://doi.org/10.1093/qje/qju032

Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11

Felstead, A., & Henseke, G. (2017). Assessing the growth of remote working and its consequences for effort, well‐being and work‐life balance. New Technology, Work and Employment, 32(3), 195–212. https://doi.org/10.1111/ntwe.12097

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

Golden, T. D. (2012). Altering the effects of work and family conflict on exhaustion: Telework during traditional and nontraditional work hours. Journal of Business and Psychology, 27(3), 255–269. https://doi.org/10.1007/s10869-011-9247-0

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. https://doi.org/10.3102/0013189X033007014

Lund, S., Madgavkar, A., Manyika, J., & Smit, S. (2020). What's next for remote work: An analysis of 2,000 tasks, 800 jobs, and nine countries. McKinsey Global Institute.

Norman, G. (2010). Likert scales, levels of measurement and the 'laws' of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education.

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680. https://doi.org/10.1126/science.103.2684.677

Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson Education.

Toscano, F., & Zappalà, S. (2020). Social isolation and stress as predictors of productivity perception and remote work satisfaction during the COVID-19 pandemic: The role of concern about the virus in a moderated double mediation. Sustainability, 12(23), 9804. https://doi.org/10.3390/su12239804

Wang, B., Liu, Y., Qian, J., & Parker, S. K. (2021). Achieving effective remote working during the COVID-19 pandemic: A work design perspective. Applied Psychology, 70(1), 16–59. https://doi.org/10.1111/apps.12290