2.18 fashion management

FMAN3003 RESEARCH PROJECT

Week 19- Introduction to Quantitative research

Salma Tallaa

Research

project

pyramid

Research Methodology & Methods

Research methodology refers to the overall framework of the research process, guiding the entire study. Studies can be Quantitative, Qualitative, or a mixture of both.

•Philosophical Approach: The underlying philosophy or paradigm (positivism, interpretivism, pragmatism, realism). •Research Design: The overall plan or structure of the study. •Data Collection and Analysis: How data will be gathered and interpreted. •Ethical Considerations: Addressing moral and ethical aspects in research.

Research method is a specific technique or tool used to collect and analyse data within the chosen methodology. Examples: Survey, Interview, Experiments

Components:

Methodology: The overarching plan and guiding principles. Method: The specific technique or tool used within that plan.

Research Paradigms and Approaches

Research paradigms are sets of assumptions that guide research, shaping how researchers view the world and make methodological choices.

• Objective reality. • Quantifiable data. • Deductive reasoning. Example: Research on the impact of fast fashion on environmental sustainability.

• Subjective reality. • Emphasis on meanings. • Inductive reasoning. Example: Research on how Gen Z people feel about fast fashion.

InterpretivismPositivism

Quantitative Research

Numerical data. Surveys, experiments

Qualitative Research

Non-numerical data. Interviews, case studies.

Paradigm

Approach

Quantitative research

• An empirical method for understanding phenomena by collecting and analysing

numerical data.

• Involves making measurements and analysing these mathematically and/or

statistically.

Quantitative Methodology and methods

• Methods underpinned by Positivism are Deductive: data is collected to prove/disprove a theory or hypothesis.

• Methods used – Experiments, observation, Survey.

• Data collection types- Questionnaires, polls, cohort, direct measurable, observation.

Quantitative methodology is underpinned by positivism and aim to find one truth using measurable outcomes.

Quantitative Data Examples

• I updated my phone 6 times in a quarter.

• My teenager grew by 3 inches last year.

• 83 people downloaded the latest mobile application.

• My aunt lost 18 pounds last year.

• 150 respondents were of the opinion that the new product feature will fail to be successful.

• There will be 30% increase in revenue with the inclusion of a new product.

• 500 people attended the seminar.

• 54% people prefer shopping online instead of going to the mall.

• She has 10 holidays in this year.

• Product X costs $1000.

Survey research

Survey research is a quantitative and qualitative method. It has 2 important characteristics that tend to identify it:

1. The variables of interest are measured using a self report 2. Considerable attention is paid to and importance placed upon the sampling process

Survey researchers ask their participants (who are called respondents in survey research) to report directly on their own thoughts, feelings, and behaviours. Hence the term self report – they are reporting on themselves or their own thoughts or perceptions.

Survey research be can questionnaires, interviews, and

sometimes even focus groups.

Advantages of surveys

• Real world data “straight from the horse’s mouth”

• Can collect quantitative and qualitative data.

• Cost and time

Disadvantages of surveys

• Low response rates.

• Contact with hard-to-reach population

• Depth and detail of data

Types of Survey using Questionnaires

Survey

Interview-

questionnaire

Self-completion

Survey

Closed: Yes / No

/ Prefer not to

say

Open:

Elaborative

(Narrative)

Attutide: Likert

scaling

Survey

• Postal

• Telephone

• Text/E-mail

• Face to Face

• Internet: Survey

Monkey

A variety of methods can be used to produce quantitative data.

Closed questions in self- completion questionnaires are the most likely source for project researcher engaged with small-scale social research.

Online self- completion questionnaires benefits:

• Save time

• Save money

• Allow wide geographic coverage.

• Environmentally friendly

Tools

Before doing your questionnaire

1. Identify your research aims and the goal of your questionnaire. ...

2. Define your target respondents (Sample) ...

3. How the information will be used?

4. Develop questions. ... (Develop or Design?)

5. Choose your question type. ...

6. Design question sequence and overall layout. ...

7. Run a pilot.

Sampling methods

Non-Probability Sampling: Quota Sampling: Researchers select participants based on pre-defined quotas, such as age, gender, or socioeconomic status. It's not random and may not represent the entire population.

Purposive Sampling (or Judgmental Sampling): Researchers select participants based on their knowledge of the population and the research objectives. It's subjective and used when specific characteristics are sought after.

Snowball Sampling: Initial participants recruit additional participants from their acquaintances or social network. It's used when the population is hard to reach or define, often in qualitative research.

Probability Sampling:

Random Sampling: Every member of the population has an equal chance of being selected. It ensures representativeness and reduces bias. Stratified Sampling: The population is divided into subgroups (strata) based on certain characteristics, and random samples are taken from each stratum to ensure representation from all groups.

How to achieve good response rates?

• Target appropriate people for the survey

• Follow up non-responses: reminder

• Make the topic of interest to respondents

• Show that the participation will make a difference: response impact

• Establish the legitimacy of the survey

• Keep things simple and easy

The success of a self-completion questionnaire depends on three things:

• Response rate (how many are returned) • Completion rate (how fully completed) • Validity of responses (how honest and accurate)

Question types

Before writing your questions, consider how you want to use the responses. This will help you to identify the most appropriate question formats to use

Open-ended questions

These types of questions allow respondents to answer in their own words. Open-ended questions are suitable for exploratory phases when there is not enough information to develop appropriate categories.

Examples of open-ended questions include:

• “Why did you leave your last paid job?”

• “How did that make you feel?

Closed-ended questions

These types of questions provide respondents with a range of the most likely answers to select from.

There are two types of closed-ended questions.

• Limited choice questions, allow respondents to choose only one response category. An example of this would be the question “Are you currently employed?”, with the response options “yes” and “no”.

• Multiple choice questions allow respondents to choose more than one response category. An example of this would be the question “Where do you do your food shopping?” with the response options of “Supermarket”, “Local market”, “Online”, and “Other”.

Likert scales

Likert scales are widely used to measure attitudes and opinions that are more complex than a simple “yes” or “no” response. A minimum of a 5-point Likert scale is recommended as one of the most reliable ways to measure degrees of opinions, perceptions, and behaviours. A question such as “How likely or not likely are you to recommend this course to a colleague?” may use a likelihood scale with the following response options:

• “Very unlikely” • “Unlikely” • “Neither likely nor not likely” • “Likely” • “Very likely”

Other scale examples include:

• Agreement with response options like “Strongly agree”, “Agree”, “Neither agree nor disagree”, “Disagree”, and “Strongly disagree” • Satisfaction with response options like “Highly satisfied”, “Satisfied”, “Neither satisfied nor dissatisfied”, “Dissatisfied”, and “Highly dissatisfied” • Frequency with response options like “Always”, “Often”, “Sometimes”, “Rarely”, and “Never” • Quality with response options like “Excellent”, “Often”, “Good”, “Fair”, “Poor”, and “Very poor”

General considerations of designing response categories

• avoid any overlap between categories

• avoid repetitive response categories

• find a balance in the number of categories

• include a “Don’t Know” or “Unsure” option at the end of all your response categories, where appropriate

• put response categories that are more socially desirable than others at the end of the list — this can help to reduce bias

Before distributing your questionnaire

Remember!

• A good questionnaire needs to be easy to answer and respectful

• Start your questionnaire with a clear introduction showing why this data will be collected and for what purpose.

• Only include the relevant questions. Grouping the questions into logical categories can make it easier to navigate through.

• Make it look attractive, e.g. use colour and images.

• Consider the timing of your questionnaire.

Pilot

• Test on small number of sample

• Change questions

• Rephrase questions •

Opportunity to alter (change) hypothesis

• Re-define methodology

pilot testing involves quantitatively testing a survey with a small sample of respondents — this type of testing is used to identify any potential issues with the format, filtering, and length of a survey

Activity: Designing a Survey for Course Feedback Develop a course evaluation survey for this course

1. Attendees will divide up into teams. 2. Teams will each select a topic area (one per team) for which

they will develop survey questions. They can be either open- ended, closed-ended questions or Likert scale. In total, the five questions should be designed to provide insight into different dimensions of your topic so that, in total, they provide useful feedback to the instructor.

Each team will select one topic area from the following options: (a) Instructor’s knowledge of subject matter. (b) Instructor’s teaching ability and organization. (c) Course content. (d) Course materials (binder, website, textbook, etc). (e) Classroom facilities

consider factors such as

clarity of language,

appropriateness of

response options, and

overall survey flow while

designing the questions.

Microsoft Forms Google Forms

Reporting quantitative results in your thesis

The results section is a completely objective report that:

➢ Tells about key outcomes/findings of the research study

➢ Presents the data and findings, ordered/analyzed in ways justified in the Methods section

➢ Uses past tense (usually)

➢ Describes the findings in a simple way with the help of data

➢ Presents the data using a clear text narrative, supported by tables, graphs and charts

What is the difference between results and discussion?

• Core of the research

• Presents only the statistical findings

• Is important in answering the research hypothesis or research questions

• Whilst meaningful relationships, variances, and tendencies are highlighted, their

interpretations and implications are not discussed here

• Stating the cause for particular results

• Discusses the meaning of the results

• States clearly what their significance is

• Links the findings to prior research (i.e., your literature review), as well as your

research objectives and research questions

Results Discussion

vs

Presentation of data

Present the research findings considering your

research questions

Include statistical analysis that has been

performed for analyzing the results

While presenting the outcomes you can mark

the key trends, the relationship between

data, etc.

Presentation of data

Tables and figures are often used to present details, while the narrative sections tend to be used to present general findings

Numerical data can usually be presented more effectively in tables or graphs than in the text

The order of presentation should be either chronological to correspond with the methods or from the most to the least important

Why use table and figures?

Help the reader understand the emerging trends and relationships in your findings

Refer to all charts, illustrations, and tables in your writing but avoid recurrence

Illustrations and tables are used to present multifaceted data

Provide descriptive labels and captions to all illustrations used so the reader can figure out what each of them is referring to

What to include in the results chapter?

As a general guide, your results chapter will typically include:

• Demographic data about your sample

• Reliability tests (if you used measurement scales)

• Descriptive statistics

• Inferential statistics (if your research objectives and questions require these)

• Hypothesis tests (if your research objectives and questions require these)

First step

• Identify which results will be presented in this section

Second step

• Start each paragraph by writing about the most important results and concluding the section with the least important results

The dissertation findings chapter should provide the context for understanding the results

Key considerations

Negative results should be added in the findings section because they validate the results and provide high neutrality levels

The length of the section is directly related to the total word count of your dissertation paper

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work

One way to arrange the section is to present a result and then explain it; This can be done for all the results while the section is concluded with an overall synopsis

When writing your results chapter, consider the main variables of your study. Quantitative research involves at least one independent and dependent variable. In the results section, you should define the variables of your study clearly

Consider whether the variables in your study are continuous or categorical. In simple words, you need to determine the relationship between both the dependent and independent variables

Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS

Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions, but also that you didn’t include superfluous information

what to avoid in the thesis results chapter

Avoid using interpretive and subjective phrases and terms such as “implies, “suggests”, “confirms”, “reveals”, “validates” etc. These terms are more suitable for the discussion chapter, where you will be expected to provide your interpretation of the results in detail

Do not present the same data in both a Table and Figure - this is considered redundant and a waste of space and energy. Decide which format best shows the result and go with it

Do not report raw data values when they can be summarized as means, percentages, etc.

Only provide a brief explanation of findings in relation to the key themes, hypothesis, and research questions

Make sure you are not presenting results from other research studies in your findings

Observe whether or not your hypothesis is tested or research questions are answered

Illustrations and tables are used to present data, and they are labelled to help the readers understand what they relate to

Use software such as Excel, STATA, SPSS to analyse results and generate essential trends

Questions?

• Artino, Jr., A. R., & Gehlbach, H. (2012). AM Last Page: Avoiding Four Visual-Design Pitfalls in Survey Development. Academic Medicine, 87(10), 1452. Retrieved from

https://www.researchgate.net/profile/Hunter_Gehlbach/publication/231210670_AM_Last_Page_Avoiding_Four_Visual-

Design_Pitfalls_in_Survey_Development/links/5a835de6aca272d6501eb6a3/AM-Last-Page-Avoiding-Four-Visual-Design-Pitfalls-

in-Survey-Development.pdf • Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th

ed.). Hoboken, New Jersey: John Wiley & Sons, Inc. • Gehlbach, H., & Artino Jr., A. R. (2018). The survey checklist (manifesto). Academic Medicine, 93(3), 360-366. Retrieved from

https://journals.lww.com/academicmedicine/fulltext/2018/03000/The_Survey_Checklist__Manifesto_.18.aspx#pdf-link

• Gehlbach, H., & Brinkworth, M. E. (2011). Measure twice, cut down error: A process for enhancing the validity of survey scales. Review of General Psychology, 15(4), 380-387. Retrieved from

https://dash.harvard.edu/bitstream/handle/1/8138346/Gehlbach%20-%20Measure%20twice%208-31-

11.pdf?sequence=1&isAllowed=y • Krosnick, J. A., & Presser, S. (2010). Question and questionnaire design. In P. V. Marsden, & J. D. Wright (Eds.), Handbook of

Survey Research. Bingley, England: Emerald Group Publishing.

• Nielsen, T., Makransky, G., Vang, M. L., & Danmeyer, J. (2017). How specific is specific self-efficacy? A construct validity study using Raschmeasurement models. Studies in Educational Evaluation, 53, 87-97.

• Saris, W. E., Revilla, M., Krosnick, J. A., Schaeffer, E. M., & Shaeffer, E. M. (2010). Comparing questions with agree/disagree response options to questions with item-specific response options. Survey Research Methods, 4, 61-79.

• Schwarz, N. (1999). Self-reports: how the questions shape the answers. American Psychology, 54, 93-105.

• Swain, S. D., Weathers, D., & Niedrich, R. W. (2008). Assessing three sources of misreponse to reversed Likert items. Journal of Marketing Research, 45, 116-131.

• Weng, L. -J. (2004). Impact of the number of response categories and anchor labels on coefficient alpha and test-retest reliability. Educational and Psychological Measurement, 64, 956-972. Retrieved from

https://journals.sagepub.com/doi/pdf/10.1177/0013164404268674 • Wright, J. D. (1975). Does acquiescence bias the 'Index of Political Efficacy?'. The Public Opinion Quarterly, 39(2), 219-226.