Revision (content from lecture #4) Flashcards
Scientist- Practitioner model
Boulder conference 1949
- Emerging profession of clinical psychology
Trainer psychologists to be equally skilled in
- Research (scientist)
- Therapeutic practice (practitioner)
Why?
- Ensure the highest professional standards
- A psychologist requires scientific training to fully understand and evaluate published research and be able to apply it in practice
- Two way traffic between science and practice as well as feedback from practice to science
- Researcher delivers theories of practical interest and relevance to practitioner
- Practitioner assists in formulation of relevant issues to be studied and how they work in ‘real life’
- Practitioner works scientifically
- Scientist works practically
Psychological research
- A systematic way to understand human experiences of themselves and the world
- Discipline inquiry into human thinking, feeling and behaviour
- Research is a language
– Need to learn key terms
and concepts
– Starting today
– Understand and evaluate scientific research
Aims of psychological research
Measurement and description:
- How do we measure the phenomenon under study?
- Allow us to describe behaviour clearly and precisely
Understand and predict:
- Attempt to explain what is observed
- Refine explanations based on further observation
Application and control:
- Practical (real- world) implication
Theory of psychological research
System of interrelated ideas used to explain a set of observations
- Number of hypothesis can be generated
- Take time
- Eg. big 5
- Parsimonious (little, neat)
Research- psychological research
Objective, systematic process to establish facts and reach conclusions
Research Process (step 1)
Formulate hypothesis
Question: initial observation or question of interest
Define: define the problem/ interest
Review: Conduct a detailed literature review
Formulate: formulate hypothesis
Null hypothesis:
No differences or changes expected
Research hypothesis (HA or alternative hypothesis): A tentative and testable explanation of the relationship between two or more events or variables
Variables:
Factors that can vary between people (individual differences)
–> Eg. height, intelligence
Factors that can be manipulated or adjusted
–> Eg. dose of drug
Operationalised:
How will you define and measure a specific variable?
–> Need to justify based on literature
Testable hypothesis:
- Consumption of alcohol causes increased aggression
- It is predicted individuals who consume 10 grams of alcohol will express more verbal insults in a 10 minute period than individuals who do not consume alcohol
Research Process (Step 2)
STUDY DESIGN
Choose design:
- Experimental
- Descriptive (correlational)
Choose sample:
- What sample are you going to use?
Devise materials:
- Rating scales
- Vignettes
- Stimuli
Experimental:
Researcher controls the variable of interest
Descriptive (correlational):
- Naturalistic observation
- Case studies
- Surveys, questionnaires, interviews
Experimental designs:
A and B (causal, cause and effect relationship)
Testable hypothesis
It is predicted individuals who consume 10 grams of alcohol will express more verbal insults in a 10 minute period than individuals who do not consume alcohol
IV= consumption of alcohol
Two levels: 10 grams, 0 grams
DV= number of verbal insults in a 10 minute period
Sample:
Populations
All the people you want to study
Sample needs to be representative of the population of interest
- Leads to high generability
IE. how you results will translate to the other members of the population
Random sampling:
Gold standard
Every member of the population has an equal chance of being chosen for the study
Groups:
Experimental group
Eg. 10 grams of alcohol
Control group :
Eg. 0 grams of alcohol
Both groups need to be treated as equal as possible
Keep everything constant except for the IV (grams of alcohol)
Good example: Drug trials
Experimental group gets drug A/ Control group gets a sugar pill
Placebo effect- around ⅓ of people
Experimental designs
Descriptive (correlational) designs
Naturalistic observation (correlational)
Case study (correlational)
Survey (questionnaire/ interview)
Experimental designs:
Experimental designs:
A and B (causal, cause and effect relationship)
Testable hypothesis
It is predicted individuals who consume 10 grams of alcohol will express more verbal insults in a 10 minute period than individuals who do not consume alcohol
IV= consumption of alcohol
Two levels: 10 grams, 0 grams
DV= number of verbal insults in a 10 minute period
Experimental designs \+: ability to determine causation I.e variable A causes variable B to occur Ability to control variable -: Artificial External validity Can the results be generalised to other situations and people.
Descriptive (correlational) designs
Descriptive (correlational) designs
Cannot be used to imply causation
Correlation: the relationship between 2 variable
Relationship may be due to other variables
Correlational designs help us predict behaviour but does not indicate the cause of the relationship
Descriptive (correlational) designs
+:
Determines relationship between variables
Study variables that cannot be manipulated
Eg. individual differences
-:
Cannot establish causality (cause and effect)
Case study (correlational)
Case study (correlational) \+: In depth examination Provides a complete picture Economically feasible Clinical situations Best for new or rare conditions -: Observer bias Low generalisability Can’t assume it will apply to all people Time consumer
Survey (questionnaire/ interview)
Survey (questionnaire/ interview) \+: Lower costs Potentially larger samples High generalisability if using random sampling -: Questions need to be clear and easily understood Need to use structured questions Self report biases Social desirability bias Intentional deception Response set Reliance on memory Inaccurate responses Incomplete picture
Naturalistic observation (correlational)
Naturalistic observation (correlational) \+: Makes use of real life situations Observed behaviour is not manipulated Study factors unethical or unpractical to study any other way Eg. bullying, natural disasters -: Need to have systematic observation Requires trained unbiased observers to systematically observe Blind Eg. don’t know which children have ADHD Observer presence may influence the behaviour of participants Lack of control Time consuming
Step 3: Collect Data
Direct observation Questionnaire Interview Psych test Standardised Eg. IQ test Psych recording Examination of archival records
Step 4: Analyse data
Descriptive statistics (summarises, on average, whats going on→ eg. on average how many males are there) Summarise data Participant characteristics Mean age, number of males/ females Central tendency Mean, median, mode Variability SD How much do the scores vary from each other and from the mean? Range of scores Inferential statistics Enables us to infer or draw conclusion Are our results due to chance or what we are researching
Types of analysis
Quantitative (objective)
Qualitative (subjective)
Mixed methods
Step 5: Report Findings
Discuss what we found and hypothesis why?
Identify limitations
Identify future research
