MRES902: Quantitative Research Methods

Q1. Research Design

A. What research design was proposed by the consultant? Justify your answer.

• Introduction: Briefly introduce the consultant’s proposed design.
• Descriptive Research: Discuss how descriptive research will gather data from various sources (surveys, interviews, secondary data) to explore consumer behavior (e.g., age, demographics).
• Correlational Analysis: Explain how correlational analysis will examine relationships between variables, such as age and car sales. Emphasize that correlation doesn’t imply causation.
• Mixed Methods: Describe how the integration of qualitative and quantitative data (structured surveys and interviews) will offer a comprehensive analysis of consumer behavior.

B. Limitations of the research design

• Causal Inference Limitations: Explain that since the study is correlational, it cannot determine causality, using the age-sales example.
• Sampling Bias: Discuss how the sample from Sydney showrooms may not represent the broader population, leading to potential biases in the results. Refer to related literature (e.g., Panzeri et al., 2025).

C. Suggestions for improvement

• Broaden the Sample: Propose expanding the geographic scope to include other regions in Australia to improve the sample’s representativeness (e.g., rural areas).
• Longitudinal Data: Suggest tracking customers over time to capture shifts in preferences and market trends (Vasileiou et al., 2018).

Q2. Sampling Method

A. Proposed Sampling Method

Convenience Sampling: Define convenience sampling, mentioning its practicality and cost-efficiency. Clarify how it is used in the consultant’s study (e.g., selecting showroom visitors based on availability).

B. Limitations of the sampling method

• Selection Bias: Explain how the sample may not reflect the target market, as it’s based on ease of access rather than random selection.
• Limited Generalizability: Discuss how the sample’s specific characteristics (e.g., showroom visitors) may not apply to broader or online consumers, impacting the external validity.
• Overrepresentation of Certain Groups: Mention how urban consumers may be overrepresented, skewing the data.
• Response Bias: Address how showroom visitors may already have strong preferences that could influence their responses.

C. Suggestions to improve the sampling method

• Stratified Random Sampling: Propose this method for improved representativeness by dividing the population into subgroups (e.g., age, income).
• Multiple Recruitment Channels: Recommend combining online surveys with showroom data to capture a wider range of consumers.
• Quota Sampling: Suggest ensuring proportional representation of key demographics, such as age and income groups, to avoid biases (Smith & Dawber, 2019).
• Mixed-Mode Data Collection: Introduce online, phone, and in-person methods to enhance diversity and reduce location bias.

Q3. Variables and Data Collection

A. Categorization of Variables

• Nominal Variables: Define gender and region as nominal variables, explaining that they represent categories without an inherent order.
• Ordinal Variables: Discuss possible ordinal variables like car model preference, based on perceived quality or brand loyalty.
• Interval Variables: Mention the absence of explicit interval variables in the study.
• Ratio Variables: Identify age, monthly income, and sales as ratio variables, which have meaningful zeros and equal intervals.

B. Dependent and Independent Variables

• Dependent Variable: Clarify that sales (in AUD) of related car models are the primary dependent variable, being influenced by factors like age.
• Independent Variables: List age, gender, income, region, and current car model as independent variables, affecting car sales.

C. Data Collection Methods

• Surveys: Classify surveys as a primary, quantitative method for collecting structured data from showroom visitors.
• Interviews: Label interviews with showroom managers as primary, qualitative data collection.
• Published Data: Categorize the use of secondary data (market trends) as quantitative.

Q4. Control Variables

A. Control Variables

• Income Level: Suggest income as a control variable, as it can affect purchasing decisions alongside age.
• Region: Propose region as a control variable, recognizing that consumer needs and access may differ in urban and rural areas.
• Brand Preference: Mention brand loyalty as a key control, as some customers may be more inclined toward specific car brands.
• Car Ownership Duration: Suggest the length of time owning a car as a factor influencing purchasing behavior.
• Marketing and Promotions: Recommend considering marketing efforts that may affect sales in different regions.

B. Justification for Control Variables

• Increase Validity: Emphasize that using control variables helps isolate the impact of age on car sales, providing more accurate insights.
• Reduce Confounding Effects: Explain that without control variables, the study might incorrectly attribute changes in sales to age alone. For instance, higher income or brand loyalty may drive sales among younger consumers, not just age (Surucu & Maslakci, 2020).

References

Include all the references in proper citation style, ensuring that they support each point made in the text. Use APA or Harvard style for consistency.

Question 1

A: What research design was proposed by the consultant? Justify your answer.

The consultant will utilize a descriptive and correlational research design with a mixed-methods approach:

• Descriptive Research – The consultant will collect demographic and sales data through surveys, interviews, and secondary sources to outline consumer behavior (Sarstedt & Mooi, 2015).
• Correlational Analysis – Researcher will be examining the statistical relationship between age and car sales to identify purchasing trends.
• Mixed Methods – The consultant will integrate quantitative data (e.g., structured surveys, sales figures) with qualitative insights (e.g., showroom manager interviews) for a comprehensive analysis.

B: Explain the limitations of this research design with supporting literature

While the proposed research design has merit in terms of understanding the market, several limitations could affect the validity and generalizability of the results:

• Limited Causal Inferences: Since the consultant’s study is correlational, it is important to recognize that correlation does not imply causation. For instance, the consultant is looking for a relationship between age and car sales, but even if a correlation exists, it cannot conclusively prove that age causes the variation in sales.
• Sampling Bias: The reliance on surveying individuals visiting specific car showrooms in Sydney introduces the possibility of sampling bias (Panzeri et al., 2025). The individuals in the sample may not represent the broader population of potential car buyers in Australia.

C: Provide suggestions on how it can be improved in this scenario.

To enhance the research design, several improvements could be made to increase the reliability, validity, and generalizability of the findings:

• Expanding the Sample Size and Geographical Scope: The consultant could expand the sample by including participants from different regions in Australia (e.g., rural and urban areas), not just Sydney. This would help mitigate sampling bias and provide a more representative sample of the target market. (Vasileiou et al., 2018)
• Using Longitudinal Data: To capture dynamic trends, the consultant could collect longitudinal data by surveying the same individuals over a longer period, perhaps across several months or years. This would allow for the tracking of changes in customer preferences and sales, providing deeper insights into how attitudes toward car models evolve.

Question 2

A. What sampling method has been proposed?

The consultant has proposed a convenience sampling method, selecting participants based on accessibility and willingness to participate (Golzar et al., 2022). This approach involves gathering data from showroom visitors and available consumer reports, making it cost-effective and time-efficient.

B. What are the limitations of this method?

Despite its practicality, convenience sampling methods have several limitations:

• Selection Bias: As buyers are chosen based on convenience rather than random selection, the sample may not accurately represent the broader target population.
• Limited Generalizability: Findings may not apply to consumers in different regions or those who primarily shop online, which reduces external validity (Smith, 2018).
• Overrepresentation of Certain Groups: Showroom visitors may disproportionately represent specific demographics, such as urban consumers, while rural or online buyers are underrepresented.
• Potential Response Bias: Consumers visiting showrooms may already exhibit strong brand preferences, influencing the results and skewing purchasing trends.

C. Explain how can consultant improve her sampling method? Justify your discussion with supporting literature.

C. Explain how can consultant improve her sampling method? Justify your discussion with supporting literature.

The consultant proposed collecting data on several variables, which can be categorized based on their measurement scales:

• Nominal Scale: These variables represent categories without a meaningful order. In this study, gender (male/female/other) and region (rural/urban) are nominal variables (Mishra et al., 2018).
• Ordinal Scale: These variables have a meaningful order but do not have equal intervals. The consultant has not explicitly listed an ordinal variable, but car model currently in use may be considered ordinal if categorized based on perceived quality or brand preference.
• Interval Scale: These variables have equal intervals but no true zero. The provided dataset does not include an explicit interval variable.
• Ratio Scale: These variables have equal intervals and a meaningful zero point. Age in years, monthly income in AUD, number of years using the current car model, and sales (in AUD) of related car models all belong to the ratio scale.

b. What are the dependent and independent variables being proposed?

Dependent Variable: The primary dependent variable in this study is the sales (in AUD) of related car models, as the consultant aims to analyze factors influencing car sales (Mishra & Min, 2020).

Independent Variables: The consultant is primarily focusing on age as an independent variable to study its impact on car sales. Additionally, gender, monthly income, region, and car model currently in use could also serve as independent variables in the broader analysis.

c. What data collection methods were proposed by the consultant? Clearly label and categorise these methods in your answer.

The consultant has proposed the following data collection methods:

Surveys - She will collect data from people visiting car showrooms.

• Type: Primary data collection.
• Method Category: Quantitative (structured survey).

Interviews with showroom managers - She will gather insights from showroom managers.

• Type: Primary data collection.
• Method Category: Qualitative (semi-structured interviews).

Use of Published Data - She will analyze market trends using secondary data.

• Type: Secondary data collection.
• Method Category: Quantitative (statistical analysis).

Question 4

a. The consultant thinks that age is an important variable that affects sales of related car models. What control variables can the consultant use?

To strengthen her analysis, the consultant should consider control variables that might influence car sales alongside age. Possible control variables include:

• Income level – Higher income may enable customers to afford new cars regardless of age.
• Brand preference – Some customers may have a strong loyalty to specific brands (Paswan et al., 2020).
• Previous car ownership duration – Customers who have owned their car for a long time may be more likely to buy a new model.
• Marketing and promotions – Sales could be influenced by how well a brand is marketed in different areas.

b. Why should the consultant use those control variables to make her research findings more convincing? Justify your answer.

Using control variables is essential for increasing the validity and reliability of the research findings (Surucu & Maslakci, 2020). Without control variables, the consultant may falsely attribute changes in sales solely to age, while other factors such as income, brand loyalty, or location might be more influential.

For example, if younger customers are shown to purchase more cars, it might not be due to age alone but rather their income level or preference for trendy models. By controlling for income and other factors, the consultant can isolate the true impact of age on sales. This will help produce more accurate, unbiased, and generalizable findings, making the recommendations more credible for the car company's decision-making.

References

• Golzar, J., Noor, S., & Tajik, O. (2022). Convenience Sampling. International Journal of Education & Language Studies, 1(2), 72–77. https://doi.org/10.22034/ijels.2022.162981
• Mishra, D. P., & Min, J. (2020). Analyzing the Relationship between Dependent and Independent Variables in Marketing: A Comparison of Multiple Regression with Path Analysis. SSRN Electronic Journal, 6(3). https://doi.org/10.2139/ssrn.2259524
• Panzeri, Dr. S., Magri, Mr. C., & Carraro, Dr. L. (2025). Google Scholar. Google.com. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Sampling+Bias&btnG=
• Paswan, A. K., Spears, N., & Ganesh, G. (2020). The effects of obtaining one’s preferred service brand on consumer satisfaction and brand loyalty. Journal of Services Marketing, 21(2), 75–87. https://doi.org/10.1108/08876040710737840
• Sarstedt, M., & Mooi, E. (2015). A Concise Guide to Market Research. In Springer Texts in Business and Economics. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-53965-7
• Smith, B. (2018). Generalizability in qualitative research: misunderstandings, opportunities and recommendations for the sport and exercise sciences. Qualitative Research in Sport, Exercise and Health, 10(1), 137–149. https://doi.org/10.1080/2159676x.2017.1393221
• Smith, P., & Dawber, J. (2019). Random probability vs quota sampling Random probability vs quota sampling. https://eprints.soton.ac.uk/435300/1/WP5_Random_probability_vs_quota_sampling.pdf
• Surucu, L., & Maslakci, A. (2020). Validity and Reliability in Quantitative Research. Business & Management Studies: An International Journal, 8(3), 2694–2726. http://dx.doi.org/10.15295/bmij.v8i3.1540
• Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and Justifying Sample Size Sufficiency in interview-based studies: Systematic Analysis of Qualitative Health Research over a 15-year Period. BMC Medical Research Methodology, 18(1), 1–18. https://doi.org/10.1186/s12874-018-0594-7