Quantitative and Qualitative Methods

Quantitative and Qualitative Methods

Introduction

• Quantitative and qualitative methods are essential tools in geographical techniques.

• This lecture introduces both methodologies, exploring their uses, differences, advantages, and disadvantages.

Social Science Methods

• Future sessions will cover:

  • Questionnaires and surveys.

  • Focus groups.

  • Interviews.

  • Reflexivity, positionality, and triangulation.

  • Embracing uncertainty in geographical data.

  • Statistics.

Human Geography

• Human geography aims to understand the world through a ‘geographical imagination’.

• It creates and claims knowledge, raising questions about how we know what we know and how that knowledge is produced.

• Human geography is a social science with interdisciplinary boundaries, relating to objectivity, evidence, and perception.

Method vs. Methodology

• Methodology: A framework for interpreting and evaluating a research problem systematically.

• Methods: Tools and techniques used to conduct research.

• Research methodology involves not only research methods but also the logic behind those methods, explaining why particular methods are used and others are not.

Research Design Stages

• The nine main stages of research design:

  1. Defining a topic.

  2. Developing a researchable question.

  3. Designing research and choosing methods.

  4. Researching ethically and safely.

  5. Reviewing the literature.

  6. Doing fieldwork/gathering data.

  7. Analyzing data/findings.

  8. Writing up.

  9. Dissemination.

• Research design states what data is required, what methods will be used, and how it will answer your research question.

Types of Research Design:

• Exploratory:

  • Aims: Formulate problems, develop hypotheses, establish priorities, eliminate impractical ideas, clarify concepts.

  • Uses: Literature research, qualitative surveys, case study analysis, interviews, ethnographies, focus groups.

• Descriptive:

  • Aims: Make specific predictions, describe cohort characteristics, estimate proportions.

  • Uses: Longitudinal/large scale research, panels and sample surveys.

• Experimental:

  • Aims: Provide evidence on causal relationships, rule out other explanations.

  • Uses: Laboratory and field experiments.

Realist vs. Anti-Realist Approaches

• Realist Approach:

  • The world is tested, measured, mapped, and counted.

  • Aims for the establishment of universal theories and laws.

• Anti-Realist Approach:

  • The world is knowable only because we invest meaning in it.

  • Focuses on the study of contexts.

Examples:

• Realism: Easier with physical facts (e.g., number of planets).

• Anti-Realism: Easier with subjective concepts (e.g., beauty, humor, cultural values).

Quantitative Approaches

• Knowledge is science.

• Focus on formulating and testing hypotheses.

• Seeks universal laws of behavior.

• Results should be verifiable/replicable.

• Uses surveys, statistics, facts, and numbers to predict human behavior.

Qualitative vs. Quantitative Analysis

Examples in Social Sciences

• Unemployed people are more likely to be depressed (relation between unemployment and depression).

• Low-income and less educated people are more likely to vote for Trump (relation between income, education, and voting).

Why Go Quantitative?

• To obtain scientific explanations about human behavior.

• To predict what people do.

• Important for governments.

• To improve people’s lives.

• Results are easily accessible for practitioners.

• Funding opportunities.

Presentation of Results

• Consider the source, where it is published, and who commissioned/wrote the research.

• Statistics and numbers are not immune from poor research practices.

• Example: MMR-Autism study by Andrew Wakefield (1998).

Limitations of Quantitative Methods

• Presumption of objectivity.

• Figures taken out of context.

• Research can never be fully objective.

• Victims of the ‘god trick’ (Haraway 1988).

Statistics and Objectivity

• Official statistics should be viewed as social, political, and economic constructions based on the interests of those who commissioned the research (May 2011: 73).

Significant Limitation

• Does not explain the ‘why’ of phenomena and relations between variables.

• Example: Women nowadays are more likely to get married in their 30s.

• People from low-income backgrounds are more likely to develop mental health issues.

Example: Marriage Trends

• Marriage in Britain during the 1970s:

  • By age 20: $28\%$

  • By age 25: $77\%$

  • By age 30: $91\%$

• In 2019, women married before 30 are a significant minority, which amount to 1 in 3 women having fallen from more than 9 in 10 in 1976.

• Understanding changes requires exploring changes in education, employment, pre-marriage cohabitation, feminisms, ‘sexual revolution’, abortion, and divorce reform.

Example: Mental Health

• Recent research shows a strong socioeconomic gradient in mental health.

• People of lower socioeconomic status are more likely to develop and experience mental health problems.

• Children and adults in the lowest 20% income bracket in the UK are 2-3 times more likely to develop mental health problems than those in the highest.

• There is a strong correlation between mental health and employment, with gender differences.

• Understanding requires broader reasons ‘why’ including social class identities, gender inequalities, austerity politics, and the effects of the Covid-19 pandemic.

Are Numbers Enough?

• Can we study human beings, values, and relations through natural sciences approaches?

Studying People

• The cause-effect approach is not sufficient; need to consider beliefs, thoughts, desires, intentionality, fears.

• Use fewer participants but go more in-depth.

• Questions are open and not already defined.

Exercise

• In the village of Chao-chao, only $15\%$ of people are educated. After opening free schools, a study reveals that only $17\%$ are educated after 10 years.

• What are we missing?

Other Strategies

• Understanding requires interpretation, which differs from strategies used by natural scientists to test scientific hypotheses (Graham 2005: 18).

Numbers and Beyond

• Looking at universal laws we lose specificity.

• Interpretation is a form of knowledge.

• Focus shifts from quantity to quality.

• From abstract to deeper meanings.

Qualitative Methods

• Participant observation and ethnography (full immersion).

• Interviews and focus groups.

• Focusing on people’s lives and stories.

• Exploring the ‘how’ and ‘why’.

• Cultural and social contexts.

Culturally Situated ‘Knowledge’

• Knowledge is culturally situated.

• Example: differing interpretations of cultural practices.

Qualitative Intuitions

• Feminist perspective: women’s position in society is not a natural phenomenon, but a social, political, and economic product (May 2011: 19).

Feminisms and Lived Experience

• Feminisms argue that lived experience matters.

• Standpoint theory (Dorothy Smith 1980s):

  • We all have a standpoint based on our social location.

  • All views are partial; objectivity is a myth.

  • Including standpoints of marginalized groups provides more objective knowledge.

Is Qualitative Reliable?

• The researcher is like a traveler telling about a foreign land (Neuman 2011: 105-106).

• We cannot assume to inhabit another’s lived experience.

Why Is It Relevant?

• An in-depth understanding of people’s experience, feelings, and condition.

• We get to know the ‘why’ and ‘how’.

• ‘Empathetic understanding’ (Neuman 2011).

• Insiders’ point of view and stories.

Advantages

• Allows observation/exploration of complexities and contradictions.

• Allows us to give voice to others.

• Increasingly research ‘with them’ rather than ‘on them’.

Limitations

• Works with smaller samples.

• Qualitative research is accused of not being objective or scientific.

• It cannot be replicated/tested, but it can still be rigorous and reliable.

Qualitative vs. Quantitative

• Respond to different research questions (broad vs. narrow).

• Soft data vs. hard data.

• Exploring vs. predicting.

• No hypothesis vs. hypothesis.

• Open questions vs. well-defined questions.

• People’s voices vs. researcher’s pre-determination.

Exercise – Which Methods

1. ‘Being white and male increases the chances of voting for Labour’

2. ‘This research explores British people’s perception of homelessness’

3. ‘This paper investigates young people’s experience and understanding of social media’

4. ‘Poverty (or coming from area X) increases the chances of smoking’

Role of the Internet

• Quantitative research?

• Qualitative research?

Which One Is Right?

• Both!

Mixed-Method Approaches (MMA)

• Combines quantitative and qualitative data collection and analysis in one study.

• Provides more in-depth findings.

Triangulation

• Denzin’s concept of ‘triangulation’ is important for rigor, reliability, and validity in qualitative research.

• Methodological triangulation can be within-method or across-method.

  • Within-method: Triangulating data from multiple data collection methods in a qualitative case study.

  • Across-method: Triangulating data from a combination of quantitative and qualitative techniques.

Which Approach?

• If the aim is to investigate the relationship between deprivation and health across the UK, quantitative modeling is more suitable.

• If the intention is to understand the meanings of a particular place for different ethnic groups, qualitative methods are more appropriate (Graham 2005: 30).

Key Takeaways

• Quantitative and qualitative methods answer different sets of questions.

• Different assumptions on social life.

• Different strategies, data, and results.

• They are both valid!