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Module 3: Research Methods & practices

Sub-Modules

3.1: Introduction to research methods

3.2: Quantitative methods

3.3: Qualitative methods

3.4: Ethics and Open Science

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3.1: introduction to research methods

About

Non-experimental & experimental research design

Designing a mini research project

Combatting challenges, limitations, & bias in research

Designing a psychology-engaged theology project

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Designing a psychology-engaged theology project

About

materials:

Presentation

Reading List

Designing a psychology-engaged theology project: Why do you need theology anyway? Professor John Swinton

For class and personal study only; do not redistribute or edit without the author’s permission.

Reflexivity

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Designing the Project

Epistemology: What does it mean to know something

Theology and Psychology: Correlation or conversation?

Identifying Research Questions: What do you want to know?

Literature Review: Who knows what and what makes you so special!

Methodological Design: You use methods, methods don’t use you!

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Theology and Psychology:

Correlation or conversation?

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Theology and Psychology:Correlation or Conversation

Starting from the right place: The theological roots of psychology
What does psychology tell you about the human mind?
What does theology tell you about psychology?
Which epistemology is “correct?”

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Epistemology

What does it mean to know something

What does it mean to know something?

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Formulating a Research Question

Designing a Psychology-Engaged Theology Project

1. Identify Your Area of Interest

Broad Topic Selection: Start with a broad area within theology and psychology.
Narrow Focus: Focus on a specific aspect within your broad topic.
Examples: Impact of prayer on anxiety levels, the phenomenon of hearing voices

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2. Conduct a Literature Review

Review Existing Research: Understand what has already been studied.
Theological and Psychological Sources: Review both to get a comprehensive view.
Find the gaps

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3. Identify the Research Problem

Unresolved Issues: Identify specific problems or unanswered questions.

Practical Relevance: Ensure it addresses real-world issues.

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Question Type: Descriptive, explanatory, or exploratory.

4. Develop a Preliminary Research Question

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Types of Questions

Aim to describe characteristics or phenomena. They do not seek to explain why something happens but rather focus on what is happening.

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Climbing the Spiritual Ladder

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Explanatory Research Questions:

Seek to explain why or how something happens. They often explore causal relationships or underlying mechanisms.
Example : "Why do some individuals find greater psychological resilience through religious faith compared to non-religious coping strategies?“

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Exploratory Research Questions:

Aim to investigate an area where little information is available. They are often open-ended and seek to explore new insights or phenomena.

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Spirituality and Breast Cancer

Moving inwards, Moving outwards, moving upwards

5. Refine the Research Question

- Clarity: Ensure the question is clear and concise.
- Feasibility: Consider resources, time, and access.
- Scope: Appropriate for the length and depth of your study.

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6. Ensure Interdisciplinary Relevance

- Integration of Disciplines: Ensure relevance to both psychology and theology.
- Theoretical and Practical Balance: Contribute to both academic knowledge and practical outcomes.
Example: How does the concept of grace in Christian theology influence coping mechanisms for stress in believers?

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7. Test the Research Question

- Peer Review: Get feedback from colleagues or mentors.
- Pilot Study: Conduct a small pilot study to test feasibility and relevance.

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Methodological Design:

What you see is what you get

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You Use Methods, Methods Don’t Use You

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What is a Method?

A method is the particular tool or set of tools that a person chooses to use to get the data that he or she thinks is appropriate for any particular investigation.
The nature and appropriateness of the method is determined by a person’s methodological assumptions.
The choice of method is therefore a theological as well as a practical movement.

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Method and Methodology

A methodology relates to the particular worldview that you use to look at the world.
Methodologies contain the values, assumptions, methods, plausibility structures and theologies that we assume makes up our perspective on the world.
One’s methodological framework deeply impacts upon one’s choice of methods.
Methodologies, like worldviews, tend to be things we look through rather than look at.

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What drives our choices?

Methods are tools? Methods are lenses? Methods guide and determine what you see.

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What do you want to do? Why do you want to do it? Who do you want to do it for?

Three key questions in determining your method

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What do you want to do?

What exactly is it that you want to look at?
Why?
Who are you in the process?

Are you a scientist or a theologian?
Is it really worth looking at?

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Why do you want to do it?

Why are you interested in this situation?
What is your personal investment in the issue?
Could anyone else do this study?
Is it part of your spiritual formation, or is it just a practical task; a means to an academic end?

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Who do you want to do it for?

Are you doing it to gain your degree?
Are you doing it to please those who fund you?
Are you doing it to prove a point to an individual or an organisation? Are you doing it for Jesus?

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Choosing the Right Method

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Qualitative methods...

historical research

grounded theory

ethnography

phenomenology

case study

symbolic interaction

action research

ethnomethodology

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Context determines your choice of method

Hermeneutic phenomenology

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Ethnography

Personality drives your choice of method

Phenomenology V Grounded Theory

Historical Research V Interactions with real human beings!!

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Practicalities drives your choice of method

Focus groups V Ethnography

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Theology underpins your choice of method

In principle any of these approaches can be used as an aspect of theological reflection...but...each contains a quite specific methodology and, arguably, implicit theology.
Where do you think God is?
How do you think God can be known?
What is the role of the Holy Spirit in your data collection and analysis?
How important is Jesus for your research?

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Questions for Reflection

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Is Research for Jesus?

What kind of knowledge is theology?
Does QR cease to belong to the social sciences when it is done within a theological context?
Research as spiritual practice: Can qualitative research be worshipful?
Do we end doing something different? Theography?

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Combatting challenges, limitations, and bias in research

About

materials:

Presentation

Topic Summary

Quiz

Reading List

Introduction to history of Psychology:Quiz

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Challenges, limitations and bias in research

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

The replication crisis

What if almost half the things we thought we knew about human psychology turn out to be wrong? The field of academic psychology has been going through a “replication crisis” for the last decade or so. Many things we thought we knew about human psychology from past research turn out to not be supported by the data when we test for those effects again. One attempt to replicate 100 studies that were published in prominent psychology journals in 2008 found that 40 of the 100 original studies did not replicate. Other replication efforts in psychology have found similar results. This seems like pretty bad news. After all, research findings in psychology are used in ways that have a profound impact on people’s lives. What if roughly half of the things we’ve been basing those decisions on just aren’t true? How can you know what research to trust if a lot of it doesn’t hold up in subsequent testing?

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Context:

https://www.youtube.com/watch?v=Qia-McaUSY8 https://www.youtube.com/watch?v=QGWeVbYduOI From 21mins in…

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Why do results not replicate?

There are many reasons why a study might generate a result that doesn’t replicate. Here are a few common ones:

All research relying on statistical analyses has a chance of generating a “false positive” result - a result that seems to show a real effect, but is actually a fluke caused by the noise in that particular sample of data. This is, to some extent, an unavoidable aspect of the scientific process.
The “file-drawer problem” occurs when researchers test a hypothesis, and find nothing interesting (i.e. null results), so those results get shoved into a file drawer because journals aren’t interested in publishing them. This produces a selection bias on which results are published. A hypothesis may have been tested many times with null results, but because those results aren’t interesting enough to publish, they don’t make it into the literature. When a false positive result occurs on the same hypothesis, that result is likely to be published and appear in the literature as the only test of that hypothesis because the null results are stuck in file drawers so people aren’t aware of them.

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Why do results not replicate?

Studies with small sample sizes can also contribute to results failing to replicate. If a sample size is small, the study may not have enough statistical power to reliably detect real effects. This results in a larger share of the significant results these underpowered studies do report being false positives.
On top of that, researchers need to find interesting results in order to get published, and getting published is what it takes to win funding and have a successful career. This means that a lot of researchers are searching for statistically significant results (results where p < 0.05) and some of them are running large numbers of tests (or making minor variations in the tests they run) to find ones where the significance threshold was met. If you think about it, it’s pretty easy to figure out what the problem with this is: the more tests you conduct, the more likely you are to (eventually) get a false positive result. This practice is now known as “p-hacking,” and it has become among the most infamous of a number of questionable research practices (QRPs) that have come under scrutiny in the last decade.

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Why do results not replicate?

Another common QRP is called “HARKing,” which stands for Hypothesizing After the Results are Known. If you run a bunch of tests on your data and find a few results that meet the standard for statistical significance, you can then come up with a story to explain what those results mean, and present that story as the hypothesis that you were testing for in your study.

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How to address the issue

Make sure your study is described in sufficient detail that it can be easily reproduced
Submit raw data with paper and online
Pre-register your study
Registered reports.
https://www.youtube.com/watch?v=WQmJOrAupSU
A Student's Guide to Open Science : Using the Replication Crisis Reform Psychology
https://ebookcentral.proquest.com/lib/bham/detail.action?docID=30352988

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designing a mini research project

About

materials:

Presentation

Topic Summary

Reading List

Back

Designing a Mini Research Project

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

A typical research cycle...

1. Developing a research idea. 2. Formulating a testable hypothesis. 3. Reviewing relevant literature. 4. Conducting preliminary research. 5. Designing the project.

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Possible research questions

Naturalistic Observation:

• What are the most common ways that people in a particular religious community doctrinally view and experientially relate with God?

Case Study:

• How did a particular spiritual exemplar (e.g., Mother Teresa or Billy Graham) develop the ways they doctrinally viewed and experientially related with God?

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Possible research questions (Surveys)

Questionnaire

• Are their gender differences in how men and women view and relate with God?

Interview

• How do people from different faith traditions view and relate with divine beings recognized in their belief system?

Diary Study:

• How much and why do Jewish (or Muslim, Christian, etc.) believers’ views and relationship with God change amid a major life transition (e.g., getting married, becoming a new parent, experiencing unemployment)?

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Possible research questions

Experimental or Quasi-Experimental:

• Does presenting religious believers with different types of stimuli (e.g., words/images that elicit positive vs. negative emotions) lead to fluctuations in the ways they report viewing and relating with God? • How much does a particular congregation-level intervention (e.g., a sermon/video series on God’s love in one congregation vs. a sermon/video series on God’s forgiveness) lead to changes in the ways those communities’ congregants view and relate with God?

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Non-experimental and experimental research design

About

materials:

Presentation

Topic Summary

Quiz

Reading List

Non-experimental methods

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

Topic learning outcomes

You should be able to...

Explain differences and similarities of different methods/methodologies used in psychology.
Explain which methodology suits which data/question.
Choose the appropriate method for a given question.
Critically evaluate research.
e.g. journal articles.

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Main types of research methods

A. Non-experimental methodologies.

Descriptive research
Naturalistic observation.

- Case study (e.g. Clinical psychology). - Survey (questionnaire, interview, diary study).

Correlational research.

B. Experimental methodologies. C. Quasi-Experiment methodologies.

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Before we start…!

Different types of data - or information - can be gathered. Generally, these can be split into 2 categories: Qualitative and Quantitative

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Non-experimental design

Non-experimental design.
‘What?’ questions.
Describing what is happening.
Test predictions.

Vs.

Experimental design.
‘Why?’ questions.
Explanation of what is going on.
Control of factors.

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Non-experimental design: observation

Descriptive research A. Naturalistic observation

Mainly categorisation.
As little disturbance as possible.
Observer stays in the background.

Examples:

Feeding behaviour of fussy children.
Cross-cultural eyebrow raising on greeting (Eibl-Eibesfeldt).

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Non-experimental: observation

Problem

• Reliability of categorisations.

Solution

• Comparing notes of more than one observer • Inter-observer reliability.

Problem

• Reactivity.

Solution

• Participant observations.

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Non-experimental design: case studies

B. Case studies

Observation of a single person
E.g. Clinical (e.g. neurotic patient)
E.g. Developmental (child with Autism).

Examples

Freud (1856-1939): oedipus, electra complexes (see CANVAS).
Festinger, Riecken, Schachter (1956):
UFO cult that believed the end of the world was at hand.

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Non-experimental design: case studies

Problems

• Generalisation. • Reproducibility. • Cause-effects.

Solution

• Similarities of two cases with one difference.

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Non-experimental design: surveys

C. Surveys

Questionnaire

- E.g., five-factor personality questionnaire.

Interview

- Structured/unstructured.

Diary study

- E.g., issues with eating.

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Non-experimental design: Surveys

Problems with surveys
Reactivity

- Participants know their answers will be analysed.

Questionnaire validity.

- Use a well-tested method.

Comparing answers.

- E.g., Neutral vs. Agree vs. Completely Agree

Memory of participants.

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Starbucks make the best coffee...

Strongly disagree.
Disagree.
Neutral.
Agree.
Strongly agree.

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Non-experimental design: interviews

Structured interviews

- Fixed set of questions asked in a fixed order. - Multiple choice or ratings.

Benefits

- Easily quantified. - Comparability across participants - All topics covered.

Costs

- Rigid structure. - Not adaptable to participant. - Surface information.

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Non-experimental design: interviews

Unstructured interviews

- Number of topics without fixed order or fixed questions.

Benefits

- More in-depth information. - Relevant to specific participant.

Cost

- Generalisability. - Analysis is time-consuming

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Descriptive Research

Advantages:

Sometimes the only possible way of study (practically and ethically)
Often inexpensive and flexible
Real-life studies = no manipulations.
Ecological validity.

Disadvantages:

Researcher bias.
Reactivity.
Lack of cause and effect conclusions.

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Main Types of Research Methods

A. Non-experimental methodologies.

Descriptive research
Naturalistic observation.

- Case study (e.g. Clinical psychology). - Survey (questionnaire, interview, diary study).

Correlational research.

B. Experimental methodologies. C. Quasi-Experiment methodologies.

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Correlational research

Purpose:

To determine the relation between two variables
Without manipulating any variables.

How?

Measuring two or more variables.
As they exist without interference.
E.g., genetic history, income etc...

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Correlational research

Looks at direction and strength of relationship among variables.

Variables must be quantifiable.
Statistical method: correlation analysis

Examples:

Watching violence on TV and aggressive behaviour.
Head size and memory.

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Correlational research

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Hypothesis: bigger head = better memory?

Correlational research

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Correlational research

Problems

Direction of relationship often unclear.
Confounding variables (and how to determine them)

Example

Relationship of shyness and daydreaming.
What leads to what?
Other factors (e.g., introversion) may play a role.

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Descriptive vs. correlational study

Correlational study can be more informative as:

Degree of relation is stated
Prediction is possible

What can a relational study tell us?

Can identify and quantify relationships.
However, is never proof of causality.

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Experimental methods I

Dr Jon Catling

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Main types of research methods

A. Non-experimental methodologies.

Descriptive research
Naturalistic observation.

- Case study (e.g. Clinical psychology). - Survey (questionnaire, interview, diary study).

Correlational research.

B. Experimental methodologies. C. Quasi-Experiment methodologies.

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What is an experiment?

Manipulation of one or more variables.

e.g., coffee intake.

Determine the effect of this manipulation on another variable.

e.g., driving when tired.

To test of cause-effect relationship between variables.

Test of causality.
e.g., does coffee improve your driving when tired?

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Hypotheses

Experimental / alternative hypothesis.
‘Learning with background music does lead to lower marks.’
Treatment leads to an effect.

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Hypotheses

Null hypothesis.
‘Learning with background music does NOT leads to lower marks.’
Treatment does not lead to an effect.

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Dependent and independent variables

Learning with background music leads to lower marks.

Cause-and-effect relationship

Variable 2

Variable 1

Dependent variable

Independent variable

Manipulating the independent variable changes the value of the dependent variable.

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Nuisance variable

An additional factor that affects thedependent variable.

Independent variable

background music

mark

place

Nuisance variables

Dependent variable

time of testing

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How to deal with nuisance variables

Turn it into a control variable.

Potential independent variable (e.g., nuisance variable) that is held constant.
e.g., test participants at the beginning or end of term.

Note, if the nuisance variable varies across conditions, it becomes a confounding variable.

Any effect of varying the independent variable may be due to the confounding variable.
e.g., the amount of revision may vary depending on whether they are listening to music or not.

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How to deal with nuisance variables

If nuisance variable varies across all levels of the independent variable...

Hold the variable constant for all participants.
e.g., fix how much revision participants are allowed to do.

If nuisance variable varies across participants...

e.g., gender.
Randomly assign participants to treatment groups.

Counterbalancing.

e.g., no music condition followed by music condition.
Vice versa for other half of participants.

Include a control group.

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Groups of participants

Experimental group

Group receiving the important level of the independent variable.
e.g., students listening to music as they study.

Control group

Group that serves as the untreated comparison group.
Group receives comparison level of the independent variable.
e.g., students not listening to music as they study.

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Non-Experimental and Experimental Research Design:Quiz

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3.2: quantitative methods

About

The art of survey research

Experimental research: Using statistics

Variables, scales, and visually representing data

Learning the language of psychology - statistical probability and inference

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Variables, scales, and visually representing data

About

materials:

Presentation

Topic Summary

Quiz

Reading List

Variables, Scales, and Visual Methods of Representing Data:Quiz

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Variables, scales and visual methods for representing data

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

Descriptive Statistics I

Variables and scales.
Visual methods of representing data.

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Leaning outcomes

Variables and scales

You should be able to…

- Describe different scales. - Recognise the type of scale of a variable.Central tendency

You should be able to…

- Calculate and describe measurements of central tendency. - Explain which measurement is appropriate for which scale.

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Variables and scales

Variable = something that varies.

Hypotheses are phrased in general terms.

For a study, we have to decide exactly what we want to measure.

Learning with background music leads to lower marks.

Cause-and-effect relationship

Variable 2

Variable 1

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Variables and scales

Operational definition of variables

A specific statement about how a variable will be measured to represent the concept under study.

Examples

Background music

- Participants study for four hours a day seated at a table. - Listening to The Beatles via headphones at a volume X…

Mark

- A mark out of the range A-F. - A=100% correct, F=0% correct.

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Variables and scales

Problems with defining variablesSubjectivity vs. Objectivity

How do we measure...

- Mood (sadness, happiness, depression…). - Social behaviour such as shyness. - Intelligence (IQ tests?).Testability

How do we measure...

- The activation of words in the mind. - Level of anxiety.

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Variables and scales

Measurement

A way to describe real life factors by numbers.

Types of measurement = scales

1. Nominal scales
2. Ordinal scales
3. Interval scales
4. Ratio scales

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Variables and scales

Scales differ in relationship between: The properties of the numbers e.g., 2 vs. 4 vs. The properties of what is being measured e.g., does a score of 4 = twice 2

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Variables and scales

1. Nominal scale
Numbers are merely labels.
No relationship between size of number and attribute measured.
Examples

- Bus 19, 242, 3, 9... - Numbers of buildings on the university map.

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Variables and scales

2. Ordinal scales

Order of size of numbers = Order of size of attribute measured.

Data are put in order.

- But only relative ranking - Distances between scores vary.

Examples

- IQ scores. - Commonwealth games medals.

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Variables and scales

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Variables and scales

Example

Celsius

- Difference between 10˚ & 20˚ is the same as difference between 20˚ & 30˚ - But: 20˚ is not twice as warm as 10˚. - Zero is not a meaningful point. - e.g., -10 ˚ exists.

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Variables and scales

4. Ratio scales

Interval scale and number zero denotes absolute absence.

Examples

- The time for participants to press a button (RTs). - Degrees Kelvin (since 0° K is a meaningful zero point). - Absolute zero.

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Variables and scales

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Graphs

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Types of graph

1) Bar graphs

- Histograms.

2) Stem-and-leaf plots
3) Box plots
4) Scatterplots

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Bar graphs

Bar graphs: ordinal data

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Bar graphs

Bar graphs: nominal data.

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Bar graphs

Bar graphs: horizontal

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Bar graphs

Bar graphs: stacked

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Bar graphs

Bar graphs: histograms

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Box plots

Also known as box-and-whisker diagram.They summarise...

The lower quartile (Q1) and upper quartile (Q3).
Median.
Minimum.
Maximum.
Outliers.

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Box plots

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Box plots

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Scatterplots

Shows the relationship between variables.

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the art of survey research

About

materials:

Presentation

Topic Summary

Quiz

Reading List

The Art and Science of Survey Research: Quiz

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The Art and Science of Survey Design

Dr Shoko Watanabe

For class and personal study only; do not redistribute or edit without the author’s permission.

What do you mean by “survey”?

Terminology Clarification: “Survey” - Method

Asking people about themselves
Helps study relationships between/among variables
Non-Experimental (does not manipulate any variables)
Examples: World Values Survey (WVS); General Social Survey (GSS); British Social Attitudes Survey (BSA)

“Survey” = Questionnaire?

A set of written questions used to collect information

- Surveys contain questionnaires - Experimental studies often contain questionnaires

Basically, self-reported data

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Bad News…

Poorly designed questions/surveys waste your money and time (and participants’ time)Why are you using a survey?

You can’t infer causality from survey research
Why not do an experiment with behavioral measures?
Why not do a semi-structured interview?
Issues with open-ended (free response) items
Issues with Likert-type scales
Issues with multiple-choice items
Issues with self-reported data

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So, you decided to do a survey…

FasterCheaperMore participantsNumerical data ☺

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Survey Research Process

Survey programming draft in Word

Ethics approval

Build, pre-test, revise/debug, repeat

Pre-registration (e.g., OSF)

Soft-launch (Pilot)

Survey distribution (data collection)

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Survey Research: Designing

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Create survey programming draft in Word

Research Question, Method/Design, Hypotheses
Operationalize Variables
Existing measures (save citations)

Draft/

Create own items (justify why)
Survey Flow: General order
Survey Draft: Specific items
Instructions, consent, debriefing

Review/ Reflect

Revise

Share/ Feedback

Exact question wording, format, variable names

Survey Research: Designing

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Create survey programming draft in Word

Draft/ Revise

Review/ Reflect

Share/ Feedback

Survey Research: Designing

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Create survey programming draft in Word

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Survey Research: Designing

Create survey programming draft in Word

Cost: A little time investment
Benefits:
Easily revise/track changes with collaborators
Quickly build final survey on Qualtrics (mostly copy/paste)
RA can build the survey for you
Help you draft: Pre-registration, methods section
Readily refer to survey items without opening survey
Remember your decisions when other researchers ask
Open science practices (online supplementary materials)

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Disseminating/Evaluating Research

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Survey Research: Building Survey

Build, pre-test, and revise/debug your survey

Build survey according to the programming draft
Label variables! Check scale coding!
Preview/Pre-test
See what participants will see!

Duration estimate & feedback
Revise/Debug
Too long? Too wordy? Fatigue?
All features working properly?
Pilot, pilot, pilot!

Cut the item if you can’t say “100% YES!” to the following:

Does this item address my main research question?
Do I know how to analyze this

type of data?

Is it worth the extra time and money?

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Survey Research Process

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Ethics approval

Survey programming draft in Word

Build, pre-test, revise/debug, repeat

Pre-registration (e.g., OSF)

Soft-launch (Pilot)

Survey distribution (data collection)

Draft the methods section while collecting data

Why do you think the following examples are "bad" survey items?

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Why do you think the following examples are "bad" survey items?

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Which question should be first?

Importance  Frequency?
Frequency  Importance?

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How would you order these questions?

Open-ended  Close-ended?
Close-ended  Open-ended?

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Preventing Participant Fatigue

Duration: 10-15 minutes is ideal
Number of questions: Ask as few questions as you need to get the reliable/valid information you need.
Sometimes, 1 item is OK (e.g., “What is your age?”)
Maybe use a subscale of a longer scale? (e.g., RCOPE)
Short & Simple is best!
People don’t like to read
1 question with 50 response options is not short!
Longer/complex questions overwhelm people
Every second/click adds time and money!

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More Suggestions

Include attention check items!
Include a timer on each page!
Include anti-bot/fraud protection features!
https://www.qualtrics.com/support/survey-platform/survey- module/survey-checker/fraud-detection/
Consistency throughout survey (e.g., font size)
Use page-breaks to minimize scrolling
(If applicable) Translation & back-translation

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What are the pros/cons of these options?

Include a variety of question formats?
Include the “back” button?
Force-response or request response?
Include filler questions?
Include reverse-coded questions?
Demographics at the end?
How much information about the survey should I give?
Why does Shoko usually avoid: fill-in-blanks, free-response, dropdowns, slider scales, and multiple-answer formats?

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Shoko & Sarah’s Feud Research

At University, Shoko and Sarah were assigned to work together on a research project for a psychology course. They decided to compare stress or burnout levels among college students by field of study (i.e., major). When generating the research hypothesis, however, they got into an argument because Shoko insisted that music majors probably don’t get stressed because their homework is just practicing what they’re already good at. Sarah disagreed and said that music majors are incredibly burnt out because unlike other majors, they often must deal with performance anxiety, perfectionism, career-related concerns, and lack of respect. To settle this dispute, Shoko and Sarah decided to carry out this research together by conducting a survey among fellow college students.

Shoko thinks music majors just go to gigs and have fun…

Sarah’s hypothesis: Music majors are more stressed/burnt out than non-music majors.

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Quick Illustration

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Q8: To examine the research question, Shoko and Sarah need to know about each participant’s major (field of study). Create 1 questionnaire item (including response options, if applicable) that Shoko and Sarah can include in their survey to assess this.

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…list 200 more majors?

What are the costs and benefits if you used this item?

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Quick Illustration

Q9: In addition to majors, Shoko and Sarah also need to assess each participant’s perceived stress/ burnout level.

Gold et al.(1989) developed college student version of Maslach Burnout Inventory (1986) Also, see here: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=perceived+academic+stress+scale&btnG=

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experimental research: Using statistics

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Experimental Research:using statistics - Quiz

Experimental Research:using statistics- Quiz

Experimental Research:using statistics - Quiz

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Experimental research 2: using statistics

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

More than one independent variable

If you have more than one independent variable (IV), include them in one experiment.
Change a nuisance variable into an IV.
Testing in the beginning compared to end of semester.
Better than several experiments testing IVs independently as:
More efficient.
Better control of nuisance variables.
Results often more representative of behaviour.

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More than one independent variable

Example question
Does a special diet and/or exercise reduce cholesterol?
Independent variables (IVs)
Diet (special diet vs. normal diet).
Exercise (exercise vs. no exercise).

Dependent variable (DV)
Level of cholesterol in blood.

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More than one independent variable

a) Testing IVs separately.
Results
Experiment 1
Special diet reduces cholesterol levels
Experiment 2
Exercise reduces cholesterol levels

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More than one independent variable

b) Testing IVs in one experiment

Special diet reduces cholesterol levels.
Exercise reduces cholesterol levels.
Diet and exercise interact and lead to even bigger decrease.

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More than one independent variable

Interaction between diet and exercise.

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More than one Independent Variable

If no interaction between diet and exercise

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More than one dependent variable

Dependent variables (DVs) are measures of the behaviour in question.
e.g., essay marks.
Measuring more than one DV is usually more informative.
For example, speed vs. accuracy.
e.g., time taken to write essay vs. essay mark.
Note: these variables might not measure the same thing.
e.g., speed-accuracy trade-off.

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More than one dependent variable

Example
Task: identify as non-word/word.
Hypothesis: ‘when reading a word, similar words are activated’
Therefore:
‘cird’

- Difficult to identify as non-word as it is similar to real word

‘tgik’

- Easy to identify as non-word, not similar to real word.

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More than one dependent variable

Procedure:

Words (and non-words) appear on a screen.
Participants press ‘yes’ or ‘no’ button
Response depends on whether it is an English word nor not.
They do this as fast as possible.

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More than one dependent variable

Independent variable

English words

- e.g., ball, lent…

Non-words (pseudo-words) resembling real words.

- e.g., cird, semp, stook…

Pseudo-words not resembling real English words

- e.g., tgik, ptil, krsi…

One factor with three levels.

Dependent variables

Response errors.
Reaction times (RTs).
Two factors.

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More than one dependent variable

Results

Errors

- More if pseudo-words resemble real words (cird). - Than if they do NOT (tgik).

Reaction times (RTs)

- Slower to reject pseudo-words that resemble real words (cird). - Than those that do NOT (tgik).

DVs measuring similar processes

- No speed-accuracy trade-off.

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Pros and cons

Advantages of experiments

Relative strong test of causality (e.g., what leads to what).
Possibility of a variety of manipulative controls.

Disadvantages of experiments

Unnatural setting and tasks.
Reactivity (also in non-experimental research).
Some phenomena cannot be studied under controlled conditions (e.g., social interactions).
Ethical limitations (e.g., lying to participants, electric shocks etc…).

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Within vs. between subject designs.
Quasi-experiments.
Sampling types.
Cutting-edge experimental methodologies.

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Main types of research methods

A. Non-experimental methodologies.

Descriptive research

- Naturalistic observation. - Case study (e.g., Clinical psychology). - Survey (questionnaire, interview, diary study).

Correlational research.

B. Experimental methodologies. C. Quasi-Experiment methodologies.

Cutting-edge experimental methodologies.

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Learning outcomes

At the end of this lecture you should be able to…

Explain differences and use of:

- Between-subjects designs and… - within-subjects experimental designs.

Explain what quasi-experiments are.
List advantages and disadvantages of quasi-experiments.
Describe different sampling techniques.
Describe different types of modern experimental methodologies.

- Advantages/disadvantages.

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Within- vs. between-subjects design

Within-subjects design

AKA repeated measure designs.
All participants receive all levels of the independent variable.

Examples

All participants exposed to different doses of a drug.
Participants study with background music in for one essay, silence for another.
Between-subjects design
Different groups of participants receive different levels of an independent variable.

Examples

Experimental and control group when testing the effectiveness of a drug.
Group A study with background music; Group B study in silence.

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Within- vs. between-subjects design

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Between-subjects design

Advantages

No order effects.
Some experiments can only be between-subjects.
Naïve participants (no learning).

Disadvantages

Lots of participants.
Characteristics might differ between groups.

- e.g., how well they study generally, with or without music.To counteract…

Participants randomly assigned to groups.

OrParticipant characteristics matched in each group.

However,

- Cannot think about all nuisance variables. - Variables can be related and cannot be matched.

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Within-subjects design

Advantages

Fewer participants required.
Reduced individual differences.

- e.g., confounding variables from each participant (essay writing ability) - Participants are used as ‘their own controls’.Disadvantages

Carryover effects.
Effect of one carries over to next session.

- e.g., ‘benefits’ from silent study affect following ‘with music’ behaviour.Solution

Order of conditions randomised or counterbalanced.

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Counterbalancing

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Counterbalancing

Compromise: Latin square design

Each condition occurs equally often in each session of the experiment.

Problem…

Be careful as carryover effects still possible.

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Quasi-experiments

One (or more) of independent variables are selected.

i.e., not manipulated.

Example

Effect of education on memory skills.

- Quasi-independent variable: education. - University degree: yes/no. - Dependent variable: scores in memory test.

Participants not randomly assigned to the levels of the independent variable (education).

- E.g., whether they have a degree or not.

Leads to more confounding variables that cannot be removed.

- E.g., people with better memories more likely to go to university?

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Quasi-experiments

Advantage of quasi-experiments

Examination of variables that would be unethical to manipulate.

Disadvantage of quasi-experiments

No strong conclusions about cause and effect possible.

Improvements that can be made.

Matching participants.
If treatment study: tests before and after treatment.

- Participant their own control.

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Sampling types

Random sample

Everybody has equal chance of being selected usually practically difficult!

Stratified sample

Random selection of each subgroup of the population
Advantage: key groups in sample.

Quota sample

Representative sample that meets quotas/targets.
e.g. 50% females (14% left-handed).
50% males (16% left-handed).

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Psycho-physiological measurements

Aim

Testing the effect of psychological variables on physiological processes.

Examples

Muscle activity.
Eye movements or eye blink rate.
Brain imaging / neurophysiological measures.

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Psycho-physiological measurements

Brain imaging / activations

Localisation and timing of brain functions

Examples

Electroencephalography (EEG).

- Event-related brain potentials (ERP).

Functional magnetic resonance imaging (fMRI).

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Electroencephalography

EEG

Electrodes placed on the scalp.
Detect and measure patterns of electrical activity emanating from the brain.

- Event-related potentials (ERPs). Example experiment

Comparing normal and abnormal developing children.

- Differences in activity in the brain.

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Electroencephalography

Advantages

Excellent temporal resolution.
Relatively inexpensive.

Disadvantages

Poor spatial resolution.
Artifacts from, for example, eye movements.
Surface activity.

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fMRI

Functional Magnetic Resonance Imaging

2-D and 3-D views of the brain.
Measures amount of blood oxygen --> activity.

Advantages

Excellent spatial resolution (2-3mm).
Accesses all areas of the brain.

Disadvantages

Poor temporal resolution (5 seconds after a stimulus).
Expensive.
Claustrophobia inside the scanner.
Participants must not move.

Example

Localization of visual and auditory functions.

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Learning the language of Psychology - Statistical Probability and Inference

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Learning the Languageof Psychology Research

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

Learning outcomes

Statistical use of probability.
Null-hypothesis and alternative hypothesis.
The logic of hypothesis testing alpha-levels and critical values.
Type I and type II errors.
Directional and non-directional hypotheses.
Statistical inference from samples.

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Probability

What is probability?

- A measure of how likely it is that some event will occur.

Example...
Dealing a card.

- Probability of 1 in 4 of getting a particular suit (e.g., a heart). - p = 1/4 = 0.25

Or, on average, on 25% of all cases you will get a heart.

p can vary from 0 (never) to 1 (always).

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Statistical use of probability

Case study analysis.

What is the probability that one score belongs to a particular distribution?

Distribution of control behaviour (e.g., reading).

- What is the probability that the score of stroke patient matches that of controls? - Or does brain damage impair their performance? - E.g., they do not belong to the control distribution.

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Statistical use of probability

Standardised reading test

Normal distribution for age-matched population of healthy people:

- Mean = 50. - Standard deviation = 10.

Patient’s score: 28.

Did the brain damage caused a decrease in reading ability for this patient?

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Competing hypotheses

Null hypothesis: H0

The patient’s reading ability does not differ from that of healthy people.

Experiment/Alternative hypothesis: H1

The patient’s reading ability is lower than that of healthy people.
Directional hypothesis.

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Testing the two hypotheses

Assume the null hypothesis (H0) true.

E.g., there is no difference...
The scores from the patient belongs to the distribution of control scores.
Just due to individual differences.

Under this assumption...

Calculate how probable it is to get the score as extreme as or more extreme than what we obtained.

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Testing the two hypotheses

If this probability is very small, reject the null hypothesis (H0).

Thus accepting the alternative hypothesis (H1).
Reading ability of patient worse/better than healthy controls.
Difference is not just due to individual differences.

If this probability is not very small, retain the H0.

Reading ability of patient no different to healthy controls.

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Testing the two hypotheses

Assuming H0...

How probable is it to get the score of 28?

From the analysis

Percentage of people with a score 28 or lower is o.0139.

In other words…

If you randomly sample one person from the population of healthy people…
1.39% of the time you get a score 28 or lower.

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Testing the two hypotheses

Conventional threshold for rejecting the H0...

Is 5% or p=0.05.
Also known as α (alpha).

If p<0.05, we reject H0.

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Testing the two hypotheses

Patient with brain damage

Probability of getting a lower score, p=0.0139.
Directional hypothesis.
Which is less than 0.05.

Reject the null hypothesis.

Reject that the ability of patient is not different to healthy controls.
Patient’s score did not come from the same population as scores for healthy people.
Difference not just due to individual variance.

Accept the alternative/experimental hypothesis.

Patient’s reading score was significantly lower than healthy controls.

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Type I error

We could be incorrect in rejecting the null hypothesis.Type I error.

Rejecting the Ho when we should not.
Deciding the score is significantly higher/lower when it is not.

When the alpha-level is 0.05...

Our inference is wrong 5% of the time.
We find significance wrongly 5 times out of 100.

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Type I error

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Type II error

Why don’t we try to reduce the Type I error by reducing the alpha level (e.g. 0.01)?Because we will increase the risk of a Type II error.

Failing to reject null-hypothesis when we should.
E.g., finding a score to be not significantly different from the population when it is.

Decreasing the likelihood of Type I error increases the likelihood of Type II error and vice versa.

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Direction of hypotheses

Related to what you predict about the data.

Directional (one-tailed) alternative hypothesis.

- “The patient’s score will be lower than the scores of healthy controls.” - “Grades on your second essay will be better than your first.”

Non-directional (two-tailed) alternative hypothesis.

- “The patient’s score is different from the scores of healthy controls.” - “Grades on your second essay will differ to those on your first.”

Based upon prior research.

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Directional hypotheses

Alternative hypothesis (H1)

- “The patient’s score is lower than the scores of healthy controls.” - “The patient’s score is higher than the scores of healthy controls.”

Null hypothesis (H0)

- “The patient’s score does not differ from the scores of healthy controls.”

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Non-directional hypotheses

Alternative hypothesis (H1)

- “The patient’s score is different from the scores of healthy controls.”

Null hypothesis (H0)

- “The patient’s score does not differ from the scores of healthy controls.”

H0 does not differ whether the hypothesis is directional or not.

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Statistical inference from sample

Using probability theory to make inferences about a population from sample data.

- Not just one participant….

Data from a 20-participant sample.
What you can take from the sample meanand standard deviation…
…And generalise it to the general population.

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Statistical inference

Example

The average weight of 18-year-olds in Britain
We can only estimate the real mean by taking a sample.

Inference

Sample

Population

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Statistical inference

How sure can we be that the...

- Estimated mean/standard deviation (from sample) - The population mean/SD

We cannot be 100% sure, but we can state the probability of our inference being wrong.
Or correct.

Population

Sample

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What we have learnt?

The use of probability in statistics.
What is statistical significance.

- E.g., alpha levels.

Type I and Type II errors.

- What they are and how they interact.

Null and alternate hypotheses.

- One-tailed and two-tailed.

The logic of statistical inference.

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Learning the language of psychology: Quiz

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3.3: quaLITATIVE methods

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Qualitative methods

Thinking theologically about qualitative methods

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Thinking theologically about qualitative methods

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Dr Amy Daughton

For class and personal study only; do not redistribute or edit without the author’s permission.

Some frequent methods

Participation/ Observation

Interviews

Action research

Focus groups

Creative arts

Co-creation

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A common task?

Qualitative approaches are often geared toward creating texts
from interviews or focus groups turn experience and expertise into text through questions and story-telling
Even for artistic methods there’s often a stage of commentary or explanation
This may reflect Boisen’s insight that human persons themselves represent a kind of ‘living human document’

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Psychology methods also include tools like scales and ways of measuring attitudes. All these approaches might be ways of reading each human ‘document’ and comparing documents too. What is common to these approaches is that they are person-centred.

A critical question: Does this account of using qual methods retain the theological character of the enquiry?

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The challenge of being theological

“…The discipline [of practical theology] is still largely reliant on social science methods of data collection and analysis and so uncovering the theological character of empirical research, making a thesis ‘theological all the way through’, can be a challenge.” Sexton, 2019, p. 44

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“we are talking about the ongoing collaborative activity of theologians, learnt largely by apprenticeship but constantly evolving and developing, its boundaries stretched by each generation of practitioners. And we are talking about the standards of good practice, and the exemplars who embody those standards, that shape our notion of what it means to pursue this practice well.” Jim Fodor, Mike Higton, 2015

So can we think about using qual methods as a theological practice?

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An increasing concern in the discipline:

“For the theologian employing qualitative research there is a call also to develop spiritual practices which bear witness to the fundamental theological reason for an integrated theology. This fundamental reality is, I suggest, bound up in the doctrine of the Trinity as God‐with‐us, and in our theology of, and faith in, the Holy Spirit in particular.” Watkins, 2022, p.16

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Example 1: Michael Paterson

Often we see:
give an account of the actual experience;
engage insights from the social sciences;
mine the theological tradition for what it may profitably yield;
and identify a pastoral response.

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Paterson suggests: theology is not a tool to be applied.

“reflection [is] a sustained, disciplined, spiritual practice which consists of looking, looking and looking again, until what is seen provokes wonder, insight and response in the beholder. I am talking about reflection as an active form of contemplative inquiry or Visio Divina … constantly surprised by grace.”

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“…irrespective of the mode used, what praxis is increasingly teaching me, is that at the heart of reflective practice lies a dialogue between Soul, Role and Context which runs deeper than any preferred mode. By soul, I mean a person’s motivational impulse; what takes them tick; what gets them out of bed in the morning; the fire in their belly that provides meaning and purpose; the inner drive to contribute to the common good.” Michael Paterson, Practical Theology 12.1 (2019)

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Example 2: Catherine Sexton

Worked with older apostolic Religious sisters
How did they understand their apostolic ministry when they were less active?
Interviews Transcripts
Voice-Centred Relational Method of reading
Narrative portraits of the individuals
Thematic coding

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‘conversion’ of the methods

Swinton and Mowat: ‘conversion’ of social science methods ‘whereby such methods are used to reveal and mediate the Gospel’.
Women themselves prompted that conversion, as living gospels

Interviews as ‘holy listening’
Reading transcripts as lectio divina:

reading, meditation, prayer, contemplation

Research as vocation and a kind of ministry

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‘in place of my early morning Lectio ‘practice’ in the same physical location and with the same attitude of reverence and to coming to something of God. I approached the texts, therefore, not in expectation of fresh sources of revelation, but in expectation of discerning the actions and presence of God’. Sexton, 2019, p. 49

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Example 3: Cas Wepener

One project included a focus group to learn more about death rituals in an African Initiated Church
The gathering was structured (by the research participants) using liturgical symbols and actions:
A drum that would start worship was sounded, then the researcher was invited in to the Church where the group met
All participants and researcher wore garments to do with their church roles
Incense was burned during the discussion
Concluded with singing and the Lord’s Prayer

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The researcher’s role and identity

The research was enabled by the researcher’s own religious (ordained role) – this was a key question of the congregation’s own leader, seeking that the researcher would have a ‘genuine sympathy’
Longer-term research also involved:
Exchanges of gifts
Sharing meals
Praying
Wepener called all of this part of developing a spirituality of liminality – crossing thresholds

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Example 4: Marie-Claire Klassen

Also used Lectio Divina as a way to handle data
Read resonant scripture next to the accounts of participating women
Specifically, the Magnificat:

“This practice further opened me to listening to the movements of the Spirit in the reflections these women shared with me. The act of praying invited listening to their words with a posture of spiritual discernment rather than academic analysis… By listening in this spiritual register, I also opened myself up to be changed by them—to slowly let praying with their insights impact my own posture towards Mary” Klassen, 2024, p. 19

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Still, some caution

“Merton repeatedly warns that contemplation is not gained through technique. There are certain practices that can help us along the way, lectio divina being a classic one, but these practices are more akin to path we can follow than a skill to be mastered. He recommends letting go of the desire and expectation for ‘results.’ Instead, show up consistently expecting nothing. Contemplation is a gift to be received rather than a method to be perfected” pp.10-11 Klassen notes that her approach “lacks the communal dimension that historically shaped lectio divina. I engaged in this practice alone rather than with others, in part because it emerged organically…” p. 21

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‘Being theological’ may mean seeking insight into God in/through:

Your role as a researcher
Your professional role
Your and your participants’ contexts
The encounter/s with participants:
both formal and informal moments

The data those encounters produce
How you handle and ‘read’ the data
The insights of theological traditions at each of these stages.

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Consider your approach to research practice:

how might your own personal convictions or formal religious role be present in the research?
Collaborative activity, or apprenticeship – who can be a guide or a partner here? And who can help triangulate?
Standards of good practice, e.g. lectio has rules and a process. Where are the exemplars of those standards?

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qualitative methods

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Qualitative research methods: Quiz

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Qualitative Research Methods

Dr Carissa Sharp

For class and personal study only; do not redistribute or edit without the author’s permission.

Ethics

ALL research with human subjects needs to go through an ethics approval process

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Benefits of Qualitative research

In-depth understanding

Flexibility

Contextualisation

Rich data

Engagement

Multiple Perspectives

Realistic Setting

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The elements of a methodological paradigm

Martin Stringer, in his paper “Discussion document on Methodology”(1999), outlines five parts to any methodology:
Theoretical Frame: meaning the “basic assumptions andpresuppositions which are made by the researchers as they approach their work”
Pragmatics: the practical considerations of research;
The Collection of Data: determined by the theoretical frame:sociological, ethnographic etc.;
The Analysis of the Data: how the data is processed
Dissemination: the presentation of the data.

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What kind of research paradigm/data collection method to choose?

What are you trying to find out?

Can you rely on self-report?

Is it enough to hear people’s own opinions/experiences?

What kind of research activity do you want to do/are you comfortable with?

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Why use different approaches?

Science and Religion: Exploring the Spectrum research project 4x distinct “strands” of research addressing the top-level question: “What social and cultural factors have driven, and are currently driving, the narrative in the public domain that there is a necessary clash between religious belief and acceptance of evolutionary science?” This is an example of how researchers can triangulate across methods to get a fuller picture than any one method could provide on its own.

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Types of Qualitative Research

(with “human subjects”)

Observation/Ethnography
Case Studies
Interviews
Focus groups

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Ethnography

- Martin Stringer states ethnography is:

“the careful listening and observation of a specific social context over an extended period of time. Its ‘permissible data’ is everything and anything that occurs within this space (as defined by locality and time). Its aim is to make sense of that space, so far as it is possible, from the point of view of those who occupy that space” (Stringer 1999:6).

- Often includes “participant observation”- Trying to get the “whole picture”; “thick description” (Geertz, 1973)- Data can include:

Space
Smells
Details

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Case Study

Focus on ONE particular individual (or organization, or…)
Why that person (or organization, or…)?
In-depth, particular
How does this relate to other people’s experience?

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Interviews

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Focus Groups

A small group of people (6-12-ish)

- From a particular population - Who share a particular characteristic - Or chosen for being different from each other

Breadth of experiences/viewpoints
Group dynamic affects the conversation

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Practicalities/Pragmatics

Beforehand:

where do you need to go?
who do you need to talk to? Are there gatekeepers you need to be in touch with?
do you need to book travel/rooms for focus groups/etc.?

During:

What kind of information/data are you going to gather?
How are you going to record your data?

After:

How are you going to keep your data safe?
Does your data need to be transcribed, etc.?

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Data

What kind of data will you be gathering?
How might that differ by type of method?

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Analysis

So, you have your data…

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Steps (common to most types of analysis):

“Prepare and organize your data”
“Review and explore your data”
“Develop a data coding system”
“Assign codes to the data”
“Identify recurring themes”

(Bhandari, 2023) Also: think about what kind of data management software might make your life easier (e.g., NVivo)

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Types of analysis

Content analysis: “To describe and categorize common words, phrases, and ideas in qualitative data.”
Textual analysis: “To examine the content, structure, and design of texts.”
Discourse analysis: “To study communication and how language is used to achieve effects in specific contexts.”
Thematic analysis: “To identify and interpret patterns and themes in qualitative data.”

(Bhandari, 2023)

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Thematic analysis

Purpose: to identify common themes in the data – what comes up often?Different types:

Inductive: determine the themes from the data itself
Deductive: come to the data with pre-determined themes (e.g., that you’ve gotten from previous research/literature review/etc)

(information on thematic analysis: Caulfield, 2023)

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Thematic Analysis (cont.)

6 steps:

Familiarization with the data
Coding
Generating themes (broader than codes)
Reviewing themes (have we missed anything, is this an accurate representation of the data?)
Defining and naming themes
Writing up

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Write-up/Dissemination

“Show your work” – be VERY clear about your:- Methods

Why did you choose this method? Why is it the best one to use to answer your research question?
What did you do? (to the detail where someone could “replicate” it), e.g.,:
If you did semi-structured interviews, for example, list the questions asked
Give the demographics of the people you talked to

- Analysis

Why did you choose this method?
Step-by-step choices, decisions – err on the side of too much detail first!
How did you do it? Did you use software, etc.?

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Things to watch out for:

- Researcher bias/subjectivity- Impact of the research/questioning on the participants- Generalizability- Labor-intensive: make sure you have a good idea of how long it will take you

Preparation
Ethical approval
Gathering the data
Analysing the data
Writing up the data

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Challenges in Qualitative Methods

Qualitative research faces unique hurdles.

Selfhood and Continuity: Narratives may change over time or contexts (MacIntyre on narrative selfhood; Goffman on presented selves), complicating consistency.
Selfhood and Coherence: Internal inconsistencies in accounts pose methodological issues - treat as data or resolve?
Selfhood and Discourse: Subjective views are socially shaped (Saukko), with opinions borrowed from sources, requiring critical unpacking.

Other pitfalls: Researcher bias, impact on participants (e.g., emotional distress), labour intensity (transcription, ethics). Mitigate with reflexivity, ethical approval, and clear timelines.

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Design a research project

Topic: Examine the role of religion in shaping gender norms and expectations in Theravada Buddhism, in Sri Lanka.
Ethnography, Case Study, Interviews, or Focus groups
What type of research will you do, and what will it look like (e.g., how many people will you talk to)?
Who will you interact with?
What do you have to “watch out for” in this particular research project?
What is your timeline?
What sort of data will you get, and how will it help you address the topic?
How will you analyze the data?

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References

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved October 31, 2023, from https://www.scribbr.com/methodology/qualitative-research/ Caulfield, J. (2023, June 22). How to Do Thematic Analysis | Step-by-Step Guide & Examples. Scribbr. Retrieved October 31, 2023, from https://www.scribbr.com/methodology/thematic-analysis/ Geertz, C., (1973). Thick description: Toward an interpretive theory of culture. Turning points in qualitative research: Tying knots in a handkerchief, 3, pp.143-168. Stringer, M. (1999). Discussion document on Methodology.

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3.4: ethics and open science

About

Research ethics: principles & practices

Ethics in psychological research

Successes and challenges of "open science" practices in psychology

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Research ethics: principles & practices

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Dr Amy Daughton

For class and personal study only; do not redistribute or edit without the author’s permission.

What do we mean by ‘ethics’?

See Maureen Junker-Kenny’s chapter in Ethics for Graduate Researchers. She maps out three ways we could mean ‘ethics’

Vision of the human person and her capabilities
Distinctive theories of ethics
Methods adopted in professional settings

1 might be ‘upstream’ - the sources of our ethical commitments 3 is ‘downstream’ - where principles come to bear in practices

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Upstream (source of the river) Ethical theory Foundational ideas or visions

Think of a river...

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Midstream: debate norms, rules, models and ways of reasoning. Often where we agree certain regulatory frameworks

Downstream (where the river flows) Ethics in practice Concrete ways of acting

Photo by Anders Ipsen on Unsplash

Why is ethics important to your research?

Decisions about ethical elements can:

Influence the kind of investigation you will do
Shape your own behaviour as a partner in research
Even impact on the clarity of interpretation

And for fieldwork specifically:

Render data more or less accurate
Harm or help research participants – and you

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UK ethics review processes

There are some central bodies for particular disciplines

e.g. HFEA, “at-arms-length bodies”

Institutionally specific

e.g. Universities, NHS Trusts

Some peer-led development

e.g. Association of Research Ethics Committees

This is likely going to be your position. You will need to seek ethical review and clearance from your institution, and possibly from any institution you are working with, like a school, or a hospice.This is important even if your college doesn’t have a formal system for this, as many journals require it. Explore this with your institution at an early stage!

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Common Qs in ethics review processes

You must engage with your institution’s ethical review process and any required by an organisation you are working with. Such reviews will usually start with a checklist so they ask you the relevant questions – after all, psychology-engaged research probably isn’t using human tissue, or nuclear material! What follows are some common elements that might be scrutinised in an ethics review process, where you are seeking data from human participants

Identifying and recruiting participants
Informed consent
Opportunity to withdraw
Role of incentives
Confidentiality vs anonymity
Data storage, security and deletion
Kind of data being sought

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Identifying and recruiting participants

Your relationship to the participants and how you contact them are both important areas here:

For example: are you emailing people whose email you already have because of your professional role? Is that a permissible use of their contact details?
Would your relationship to those potential participants influence their decision?
Might a gatekeeper do the emailing instead?
When working with larger populations, such as through large scale anonymous surveys, how will you advertise the survey? On what platforms? Is that an appropriate way to reach relevant participants?
Any ethics review process will likely want to see examples of the advertising, whether it’s an introductory email or a social media post

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Informed consent

It’s important to establish how participants will be informed about all the aspects of the research, and how you will record that they consented having been informed. The common way to do this is:

A formal (but appropriate for the audience) participant information sheet

A consent form that lists the key features that participants sign directly

These documents should speak directly to what the participants will be asked to do, how they can withdraw, how their data will be stored, handled and deleted. That means that they can consent in an informed way.

For an online survey this information is often given at the story; completing the survey demonstrates consent.

Some psychology research involves “deception” – i.e. not revealing what you are investigating so as not to skew the results. Ethical scrutiny should consider this to ensure this is being handled appropriately.

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Informed consent

The previous slide is a good way to get started at planning to acquire and record informed consent.
You may need to consider particular needs of your population though!
For example:
Children – parental and child consent is part of the picture here, and seeking it in a child-appropriate way. See Elizabeth Nixon, Ethics of Oral Interviews with Children in Ethics for Graduate Researchers
Vulnerable people, such as people with some forms of disability – seeking consent in a way appropriate to your participants’ ways of communicating, and seeking consent from relevant guardians too
There are a lot of projects that have conducted work with populations with vulnerabilities that offer examples for research design:
e.g Susan Price, Hearing the silent speak - an exploration into the silent spirituality of severely disabled children. (Anglia Ruskin University Doctoral Thesis, 2023)
e.g. Keith Dow, forthcoming article, Journal of Pychology and Theology, 2026

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Opportunity to withdraw

Participants need to know how to stop participating if they want to. Often that can be done by closing the survey window, or simply asking to stop an interview. BUT: it’s important here to consider if a participant might need support – such as if you’ve been discussing a difficult matter. How will you provide that? After the data has been recorded can be more complicated.

Are you sharing the participant’s data with them (e.g. an interview transcript) as a form of member-checking? That may be a point for withdrawal
To enable withdrawal be sure you can track whose data is whose in order to withdraw it (often by giving participants a number or code name – c.f. confidentiality and anonymity)
You will still need to be able to go ahead and analyse the data with confidence, so it’s common to set a limit on the withdrawal window, such as two weeks after seeing the transcript.

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Role of incentives

Incentives are more common where there’s a need to encourage participation

For example, where an incentive could help advertise, or otherwise enable participation

Incentives shouldn’t create a situation where a participant becomes reluctant to withdraw when they otherwise would wish to

Often incentives are still given even if there’s a later withdrawal

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Confidentiality vs anonymity

Participants are only anonymous if you the researcher also don’t know who they are – such as with an online survey
If the researcher knows who they are, common concerns will be about how you will keep participants’ data confidential. For example:

Using a pseudonym, or a participant number
Keeping the ‘key’ for participants and their pseudonyms securely and separately from the rest of the data
Not using identifiable details to describe the participants (such as their job role, gender, location or so on)
Redacting data to ensure identifiable details of participants or third parties aren’t quoted when writing up
If using focus groups, is everyone committed to the confidentiality of the space? – consenting to follow these rules might need to be part of the consent form

Exceptions to confidentiality are important too – for example if it’s a matter of a participant’s safety
Participants should know all of this before research starts and consent to it – including their own responsibility for others’ confidential information too

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Data storage, security and deletion

You will need to check the governance of data for your country and the country of any research being conducted.
For example, in the UK data protection is shaped by the UK General Data Protection Regulation and the Data Protection Act 2018
Some useful principles from those frameworks would be:

Ask for and record only the data you need for your research
Keep the data secure at each stage of the project
Keep identifying data separately from other data
Don’t keep the data for longer than needed

A data management plan will help you to map all of this out.
You also need to include all of these arrangements in the info given to the participant, so they can consent in an informed way.

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Data continued

Consequently there are some practical things to consider:

Where will the data be at each stage of the research and how is each location secured?
(e.g. recording device -> encrypted/password protected file storage -> university servers)
Sometimes there can be requirements that data not be hosted outside its origin country – beware clouds!
How will any physical files be kept secure?
When will the data be deleted?
The deletion of data no longer in use is a key principle in good data handling
Are you asking only the questions you need for your research?
(i.e. If demographics aren’t relevant, don’t record them)

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Implications of the data being sought

Some regulatory frameworks treat some kinds of data differently – such as if it relates to sensitive personal information such as health.
Check your local requirements! And those of any country you are researching in.
Researchers also have responsibility to attend to what it’s like for the participant to share and discuss data if the subject is difficult.
As a researcher you are not always best placed to pastor the participant or offer therapeutic support
Sometimes this involves ensuring that sources of support are available, via helplines, information or an appropriate supportive person to speak with
Such concerns are partly why many ethics review processes will ask for the research tool itself, such as the list of survey questions
Such concerns also intersect with many of the other elements named above

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Researcher Safety and Insight

Researcher safety is an important intersecting concern. For example:
If the subject matter is difficult, how is the researcher being supported to process and manage their responses?
If the research involves one to one work, where is that happening? Are good lone-worker protocols in place?
For international research there may also be considerations about risk and travel,
For more sensitive projects there may even be risks associated with handling the data itself – see for example, Gladys Ganiel’s Research Ethics in Divided and Violent Societies in Ethics for Graduate Researchers, Russell, Junker-Kenny, Hogan (eds.) Elsevier 2012
Researchers also need to think about how they themselves shape the research, and plan for appropriate reflexive and triangulating scrutiny when analysing the data along the way – research journals are one standard tool

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Final practical (re-)emphases

Ultimately the researcher needs to take responsibility for planning all of this out in detail, and ensuring that their plan is appropriately scrutinised.
Research should never start before the relevant scrutiny has been conducted and the plan has been cleared.
Pay attention to other forms of authorisation you may need, such as diocesan or organisational confirmation.
Some larger institutions have their own processes that you will have to go through – such as hospital ethics board.
Your first step must be to find out what’s required where you are, and to discuss a process of ethical scrutiny with your institution.
Many journals in psychology and religion areas will require a statement of ethical clearance in order to publish your work.

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Looking back upstream as theologians

Much of the information shared here is about common practical, and sometimes regulatory, requirements for how to go about research
As theologians, you may also be concerned with the ‘upstream’ – the source/s of your own ethical commitment
These can be helpful to hold in view when going back and forth with a committee on a point of detail!

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Looking back upstream as theologians

How are you enabling the participant to raise their voice?
How can you set up a research encounter that is open to research as revelatory?
How are you helping yourself listen well?
Where does your own power lie in all of this? How can you best hold that power?
What is your responsibility as theologian, as well as ethical researcher?

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Ethics in PsychologicaL research

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Quiz

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research ethics: Quiz

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Research Ethics

Dr Jon Catling

For class and personal study only; do not redistribute or edit without the author’s permission.

Ethics

Origins of ethical rules in psychology.
Ethical guidelines.
Ethics in research with animals.
Official guidelines.

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Learning outcomes

At the end of the lecture you should be able to…
Describe ethical guidelines in psychology.
Describe where these guidelines originated.
Identify ethical issues relevant to a given study.

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What do we mean by ethics??

What do we mean by ethics?
Where do we get our ethics from?

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An ethical dilemma

Hypothesis
Negative attitudes reduce a patient’s chance of recovery from cancer.

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NOT an ethical way to test this hypothesis?

Run positive thinking workshops for patients and see if the illness is reduced.
1 and 4.
See if depression correlates with reduced chances of survival.
Play negative music to cancer sufferers.

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Which is an ethical dilemma?

The severity of cancer suffered by participants?
1, 3 and 4.
Should we encourage negative thoughts in patients?
Should we do anything that may increase likelihood of death?

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The origins

Ethical rules are widely accepted values about everyday interpersonal relations:

It is wrong to hurt others needlessly.
It is good to help others.
It is usually wrong to make others do things contrary to their wishes and best interests.
It is usually wrong to lie to others.
We should respect others’ privacy.
Under most circumstances we should not break promises to keep others’ secrets.
We should afford special protection to those who are relatively powerless or especially vulnerable to harm.

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Interpersonal values  ethical guidelines

- It is wrong to hurt others needlessly.

We should minimise harm to participants (physical and mental).

- Milgram’s obedience study.

Word learning.
Gave ‘electric shocks’ to learner.
What type of harm is this?

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Interpersonal values  ethical guidelines

- It is good to help others.

We should maximise the benefits of research to participants.
And society in general.

- It is usually wrong to make others do things contrary to their wishes and best interests.

Participants should be fully informed about the research.
Invited to take part.
Informed consent to participate must be voluntary.
Participants must be free to withdraw, also during an experiment.
No explanation.

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Interpersonal values  ethical guidelines

- It is usually wrong to lie to others.

Deception in research is generally unacceptable.
It may be tolerated under limited circumstances.

- Problem: participant reactivity.

Not naive to the purpose of the experiment.
e.g., drug study, altruism study.

- Solution: debriefing (explaining purpose post-hoc).- Only necessary deception.

So as not to bias the experiment.

-If deception, then explain in the debriefing.

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Interpersonal values  ethical guidelines

- We should respect others’ privacy.

We should not intrude into the private lives of participants without their permission.

- Under most circumstances we should not break our promises to keep others’ secrets.

With certain exceptions...

We should guarantee information will be kept anonymous or confidential.
Unless they agree to make it public.

Participants have to be told who has access to their information.
Recordings are only allowed with agreement of those being recorded.

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Interpersonal values  ethical guidelines

We should afford special protection to those who are relatively powerless or especially vulnerable to harm.

Vulnerable populations

Children, prisoners, seriously ill patients, those with comprised cognitive abilities.
They should be treated with special care.

Not only the participants…

Parents or guardians have to give their permission.

Make sure that the participant understands the study.

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Do researchers ignore these rules?

Yes, but not many:

Nazi scientists.
Atomic tests in the US.
Scientists in the past…

Sometimes might we need to flout these rules?

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INFRINGING OR VIOLATING?

There is a difference between ‘compromising or infringing’ a principle and ‘violating’ it - former is ethically justifiable through a valid appeal to another principal:

e.g. it is acceptable to cut into a patient (and thereby cause some harm) when removing an otherwise more harmful tumour (the principle of not harming an individual is here compromised or infringed with an appeal to the principle of helping people - a little harm for much greater benefit and prevention of further harm...).
Violations, however, mean that the research is unethical, and as mentioned before there are a number of examples of such throughout history.

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Official guidelines

Official guidelines to ensure ethical solutions
British Psychological Society
‘Code of ethics and conduct’
http://www.bps.org.uk/sites/default/files/documents/code_of_ethics_and_conduct.pdf
If you need to apply...
Ethics applications are checked by committees.
A university committee made up of experienced researchers.

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BPS Guidelines

Observational research

Studies based on observation must respect the privacy and psychological well-being of the individuals studied

Protection of participants

Investigators have a primary responsibility to protect participants from physical and mental harm during the investigation

Confidentiality

Except in circumstances specified by law, information obtained about a participant during an investigation is confidential unless otherwise agreed in advance

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BPS Guidelines (Cont.)

Withdrawal from investigation

At the outset, investigators should make plain to participants their right to withdrawal from research at any time, irrespective of payment etc.

Debriefing

The investigator should provide the participants with any necessary information to complete their understanding of the nature of the study

Deception

Withholding information or misleading participants is unacceptable

Consent

Whenever possible, the investigators should inform all participants of the objectives of the investigation

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Ethical audit exercise: The Urination onset study (Middlemist et al., 1967)

Research indicates that social stressors inhibit relaxation of the external urethral sphincter, which would delay the onset of urination
Sixty Lavatory users were randomly assigned to one of three levels of interpersonal distance and their urination times were recorded
In a three-urinal lavatory, a confederate stood immediately adjacent to a subject, one urinal along, or was absent.
To measure the subjects’ urination, an observer used a periscopic prism imbedded in a stack of books lying on the floor of the toilet stall. An 11inch space between the floor and the wall of the toilet provided a view, through the periscope, of the user’s lower torso and made possible direct visual sightings of the stream of urine. The observer, however, was unable to see the subject’s face
Close interpersonal distances increased the delay of onset and decreased the persistence of urination

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DIRECTED READING

Beauchamp, T. L. & Childress, J. F. (1989) Principles of Biomedical Ethics Oxford: Ox. Uni. Press British Psychological Society (current) Ethical Guidelines BPS Jones, J. H. (1981) Bad Blood London: Collier MacMillan Publishers Kimmel, A. J. (1996) Ethical Issues in Behavioral Research London: Blackwell Publishers Lederer, S. E. (1995) Subjected to Science: Human experimentation. London: J.Hopkins Uni. Press Pappworth, M. H. (1967) Human Guinea Pigs London: Sage Sieber, J. E. (1992) Planning Ethically Responsible Research London: Sage

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Successes and challenges of "open science" practices in psychology

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materials:

Topic Introduction & Tasks

Video on replication crisis (external)

Video on open science (external)

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About Submodule 3.1

This submodule introduces research methods and guides theologians in designing psychology-engaged theology projects, featuring non-experimental and experimental approaches to build foundational research skills.

Qualitative Methods

This topic provides a practical overview of qualitative research methods, guiding theologians on how to effectively gather and interpret rich, non-numerical data. It explores core techniques like interviews, focus groups, and thematic analysis.

About Submodule 3.4

This submodule equips theologians with the critical skills to undertake ethical psychologically-engaged research. It explores the fundamental ethical principles governing research with human subjects, examines historical and contemporary ethical failures, and introduces the principles and challenges of Open Science practices.

Designing a psychology-engaged theology project

This topic focuses on designing a psychology-engaged theology project, guiding theologians through integrating psychological methods with theological inquiry to create impactful research.

Ethics in psychological research

It explores historical failures and contemporary challenges to help ensure that psychologically-informed theological research conducted with human subjects is pursued in an ethically sound and responsible manner.

Successes and Challenges of "Open Science" in Psychology

This module examines the replication crisis in psychology, spotlighting open science practices like pre-registration, data transparency, and study replication to bolster research integrity.

About Submodule 3.3

This submodule on Qualitative Methods equips theologians with the essential skills to explore human experience and interpret rich, non-numerical data using methods like interviews and thematic analysis for psychology-engaged theology studies.

Ethics: Principles & Practices

This module introduces foundational ethical principles and practices intended to guide responsible theological research involving human participants.

Experimental research: using statistics

This topic explores advanced experimental research designs and their statistical implications, guiding theologians in using multiple variables, within/between-subjects approaches, quasi-experiments, and sampling methods to investigate causal relationships in psychology-engaged theology.

Combatting challenges, limitations, and bias in research

This topic addresses combatting challenges, limitations, and bias in research, equipping theologians with strategies to enhance the rigor and reliability of psychology-engaged theology studies.

Quantitative Methods

This submodule on Quantitative Methods equips theologians with essential skills in statistical analysis and survey research to explore the relationship between variables in psychology-engaged theology studies.

Learning the language of Psychology

This topic introduces the language of statistical inference and probability. It covers the logic of hypothesis testing, the function of the Null Hypothesis, and the meaning of statistical significance to enable the critical evaluation of quantitative claims in psychology-engaged theology.

Non-experimental & experimental research design

This topic explores non-experimental and experimental research design, offering theologians practical insights into structuring studies for psychology-engaged theology projects.

Designing a mini research project

This topic covers designing a mini research project, providing theologians with a step-by-step approach to plan and execute small-scale studies in psychology-engaged theology.

Experimental research: using statistics

This module introduces core psychological research tools such as variables, measurement scales, and data visualisation. It covers operationalising ideas for testable hypotheses, selecting appropriate scales from nominal to ratio, and crafting effective graphs to reveal patterns and insights.

The Art of Survey Research

This topic explores survey methodology. It covers the crucial steps of drafting questionnaire items, managing survey flow, preventing common response biases, and addressing the practical limitations of self-reported data to ensure high-quality data collection in psychology-engaged theological studies.

Thinking Theologically About Qualitative Methods

This module explores how to ensure research remains theological while relying on the tools of social science. It explores common qualitative methods, such as interviews and action research, and guides the researcher in maintaining theological integrity from data collection through interpretation.

Module 3

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Module 3: Research Methods & practices

Sub-Modules

3.1: Introduction to research methods

3.2: Quantitative methods

3.3: Qualitative methods

3.4: Ethics and Open Science

3.1: introduction to research methods

Designing a psychology-engaged theology project

materials:

Combatting challenges, limitations, and bias in research

materials:

Introduction to history of Psychology:Quiz

Introduction to history of Psychology:Quiz

Introduction to history of Psychology:Quiz

Introduction to history of Psychology:Quiz

Introduction to history of Psychology:Quiz

Introduction to history of Psychology:Quiz

designing a mini research project

materials:

Non-experimental and experimental research design

materials:

Non-Experimental and Experimental Research Design:Quiz

Non-Experimental and Experimental Research Design:Quiz

Non-Experimental and Experimental Research Design:Quiz

Non-Experimental and Experimental Research Design:Quiz

Non-Experimental and Experimental Research Design:Quiz

Non-Experimental and Experimental Research Design:Quiz

3.2: quantitative methods

Variables, scales, and visually representing data

materials:

Variables, Scales, and Visual Methods of Representing Data:Quiz

Variables, Scales, and Visual Methods of Representing Data:Quiz

Variables, Scales, and Visual Methods of Representing Data:Quiz

Variables, Scales, and Visual Methods of Representing Data:Quiz

Variables, Scales, and Visual Methods of Representing Data:Quiz

Variables, Scales, and Visual Methods of Representing Data:Quiz

the art of survey research

materials:

The Art and Science of Survey Research: Quiz

The Art and Science of Survey Research: Quiz

The Art and Science of Survey Research: Quiz

The Art and Science of Survey Research: Quiz

The Art and Science of Survey Research: Quiz

experimental research: Using statistics

materials:

Experimental Research:using statistics - Quiz

Experimental Research:using statistics - Quiz

Experimental Research:using statistics - Quiz

Experimental Research:using statistics - Quiz

Experimental Research:using statistics- Quiz

Experimental Research:using statistics - Quiz

Learning the language of Psychology - Statistical Probability and Inference

materials:

Learning the language of psychology: Quiz

Learning the language of psychology: Quiz

Learning the language of psychology: Quiz

Learning the language of psychology: Quiz

Learning the language of psychology: Quiz

Learning the language of psychology: Quiz

Learning the language of psychology: Quiz

3.3: quaLITATIVE methods

Thinking theologically about qualitative methods

materials: