Research Methods Flashcards
Empirical method
Using a procedure that means you are only measuring what can be directly observed (observable hard evidence)
Replicability
To be able to replicate research and get the same findings.
- this is done in a controlled and standardised approach (control of variables)
- also helps determine causation
Paradigm Shift
When an important change in the basic concepts and experimental practices of a scientific discipline occurs (e.g flat earth —> round earth)
Objectivity
The tendency to base judgments and interpretations on external data rather than on subjective factors, such as personal feelings, beliefs, and experiences.
Falsifiability
The principle that a proposition or theory could only be considered scientific if in principle it was possible to establish it as false.
Reliability
The overall consistency of a study ( can someone else repeat it and get similar results)
Validity
The ability of a test to measure what it was set out to measure
Standardisation
Where the procedures used in research are kept the same (e.g same order, same instructions)
Internal validity vs External Validity
Internal : The extent to which the effects of a study are due to the manipulation of the independent variable - with no influence of any extraneous or confounding variables. (Demand characteristics are a major threat for internal validity)
External : Howe generalisable the findings are
Paradigm
consists of the basic assumptions, ways of thinking, and methods of study that are commonly accepted by members of a discipline or group.
Hypothesis testing
uses sample data to evaluate the credibility of a hypothesis about a population.
Null hypothesis
Predicts no pattern or trend in results.
“There will be no difference/correlation…”
Experimental hypothesis
Predicts a significant difference or correlation in results between conditions.
Independent variable/ dependent variable/ control variable
IV -> change
DV -> measure
CV -> keep
Experimental designs
How participant’s are allocated in experimental condition:
- independent measures
- repeated measures
- matched pairs
Repeated measures + evaluation
Testing a participants under both conditions
Criticisms:
- may perform better on second condition due to practice effect = order effects
- may perform worse on second condition due to fatigue and boredom (order effects)
- cause demand characteristics
Pros:
- one participant does all conditions so fewer participants needed
- no problems with individual differences as same person for each condition, controlling participant variables.
Independent measures + evaluation
The participants in one condition are independent from participants in the other (only participate in one condition).
Criticisms:
- differences in conditions may be due to individual differences
- potentially more sample needed
Pros:
- no demand characteristics as they cannot compare knowledge from previous conditions
- no order effects as they do not know what the other condition is, as only sit one condition
Demand characteristics
Participants changing their behaviour/answers purposefully to either aid or hinder an experiment
Social desirability
Participants changing their answered as they wished to be liked by the experimenter
Types of sample definitions
- Random sample -> a sample selected at chance
- Opportunity sampling ->a sample selected by convenience
- Volunteer sampling -> self selected sample chosen by themselves via eg an ad.
- Systematic sampling-> involves taking every nth person from a list to create a sample
- Stratified random samples -> The composition of the sample reflects the proportions of people in certain subgroups (strata) within the target population or the wider population.
Experiment types/method + criticisms
Lab -> IV is manipulated by researcher in a controlled environment (true exp)
cons: low ecological validity as artificial , demand characteristics + experimenter effects
pros: replicable/reliable due to being well controlled, easy to establish cause and effect
Field -> IV is manipulated by researcher in a naturalistic setting (true exp)
cons: lack of control over extraneous variables so less replicable/reliable, harder to establish cause and effect, issues with getting informed consent
pros: high ecological validity so displays real human behavior, less chance of demand characteristics + experimenter effects
Natural -> IV is not directly manipulated in a natural environment (e.g piaget)
cons: lack of control over extraneous variables so less replicability/reliability, IV is not deliberately changed so we cannot claim the IV has caused any observed change
pros: high ecological validity so displays real human behavior (situational IV)
Quasi-experiment -> IV is already pre-existing (e.g age, gender) and has not been determined. IV cannot be changed unlike natural experiments .
cons: confounding variables as cannot randomly allocate participants into conditions, IV is not deliberately changed so we cannot claim the IV has caused any observed change./ small sample size therefore not generalisable(individual differences)
pros: controlled conditions so is reliable/replicable (based on the individual)
Extraneous variables
Any variable that you’re not investigating that may affect the dv
Quantitative data vs qualitative data + evaluate
Quan-> numerical data
Pros:
-scientifically objective, easily replicated as the data obtained does not need a lot of interpretation of results so more reliable (easier to identify patterns and trends)
-can use it to reject or accept nul hypothesis
Cons:
-require large samples to get useful data
-poor knowledge of stats can lead to misinterpretation of a=data
-low construct validity = simplifies complexity
Qual -> descriptive data
Pros:
- in depth/detailed therefore more information about a single case (high validity)
- can lead to possible investigations of cause and effect and relationships
Cons:
-time consuming
-expensive
-less generalisable
-no statistical tests or information (easier to understand with numbers> pages of writing)
-samples do not have a large data set affecting reliability of data as it can be subjective in nature
Observer effect
Subjects altering their behaviour when they are aware that an observer is present
Co-variables
Something that changes in relation to another variable
Overt observation( disclosed) vs Covert observation (undisclosed)
Participants are aware their behaviour is being observed and what they are being observed for = overt
Participants are unaware of the presence of the researcher and are unaware their behaviour is being observed = covert
Participant observation
Researcher observes participants while participating in their activities (e.g milligram/ bickman)
- group is usually aware of researchers presence
Non participant observation
Researcher observes participants without participating in their activities (can be either covert or overt)
- e.g OFSTED inspections at school
Primary and secondary data + evaluation
Primary = first hand info collected by researcher
Pro:
-Reliable data as it has been collected by themselves (trust it)
Con:
-Expensive and time-consuming
Secondary = pre-existing data
Pro:
-Saves time and money as data is already pre-existing
-Psychologist may have access to data they would not have been able to collect otherwise
Con:
-Untrustworthy
Experimenter bias vs investigator effect
bias -> The researcher consciously influences the results in order to portray a certain outcome
effect -> researcher unintentionally or unconsciously influences the outcome of any research they are conducting as they know the aims
Observer bias (detection bias)
Happens when the researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study (usually occurs when observer is aware of research, aims and hypothesis)
6 ethical issues in psychology
- withdrawal
- debrief
- informed consent
- protection of participants
- deception
- confidentiality
I don’t pick colourful wellies daily
Types of external validity
Ecological validity
Population validity: is it generalisable to population?
Temporal validity : refers to the validity of the findings in relation to the progression of time.
Types of internal validity
Construct validity: Ability of a measuring tool to actually measure the psychological concept being studied
Questioning bias
To phrase a question to favour one view over others
Correlational study (inc neg and pos)
An analysis of the relationship between two variables .
Nothing is manipulated, two co variables are simply measured to look for an association
neg-> as one variable increases the other decreases \
pos-> as one variable increases so does the other variable /
Self report evaluations
=Methods of gathering data where participants provide information about the cells without interference from the experimenter
Pros:
-For research it is inexpensive and can reach many more test subjects that could be analysed by observation or other methods. It can be performed relatively quickly so a research can obtain results in days or weeks rather than observing a population of the course of a longer timeframe. Self reports can be made in private and can be anonymized protect sensitive information of the haps promote truthful responses.
Cons:
-Honesty: subject may make them more socially acceptable answer rather than being truthful (social desirability)
-Introspective ability: the subject may not be able to assess themselves accurately
-Rating scales: rating something yes or no can be too restrictive, but numerical skills also can be inexact and subject individual inclination to give an extremely middle response to all questions
-Response bias questions are subject to all of the biases of what the previous responses were, Whether they relate to recent or significant experience and other factors.
-Sampling bias: the people who complete the questionnaire are the sort of people who will complete a questionnaire. Are they representative of the population wish to study?
Unstructured interview vs structured interview
Unstructured interviews are the most flexible type of interview, with room for spontaneity. In contrast to a structured interview, where the questions are the order in which they are presented are not set.
Structured interviews are when the interviewer has a set of prepared closed questionsIn the form of an interview schedule, which he/she reads out exactly as worded. Interview scheduled to have a standardised format which means the same questions asked to each interviewee in the same order.
Strength/weakness of correlations
PRO
-High ecological validity as nothing is set up and manipulated(more ethical)
-Can lead to further research (causal rel) via experiment
CON
-Cannot establish cause an effect as we can only make assumptions on the relationship between variables. (may be due to other factors that one variable changes etc)
External validity
The extent to which the results of a study can be generalised to an increase of the situations, people, stimuli, and times. (Bickman high external validity)
Where is the independent variable placed on a graph
X axis (horizontal)
Nominal Data (levels of measurement)
Frequencies in categories (no mathematical value)
Ordinal Data (levels of measurement)
= Subjective/judgement scores (ie on a rating scale) which don’t have equal intervals/ difference between each of them is unknown, just the position they hold within the group
Interval/Ratio Data (levels of measurement )
= Fixed (agreed/universal) units of measurement, with equal intervals
what is a ‘true experiment’
Experiments where the IV is under direct control of the researcher who manipulates it and records effect on DV.
Thus only lab and field experiments are ‘true’ as they involve manipulation of IV by researcher.
Situational iv
Something in the environment that causes a change (e.g in a natural experiment researching the behaviour of football fans with hooliganism)
Hypothesis
A clear, concise, testable statement that states the relationship between IV and Dv to be investigated (written in future tense)
2 types:
- null
- experimental
Types of experimental hypothesis:
One tailed hypothesis/directional:
This states the specific direction the researcher expects the results to move in (e.g higher, lower, more, less and for correlations it would be positive or negative) (evidence/ previous research to support)
= the girls will do significantly better in the maths test scores compared to the boys
Two tailed hypothesis/ non-directional:
This states that a difference will be found between the conditions of the iV but does not state the direction of results. (When you have no evidence to support/ no previous research)
Operationalised
Turning abstract concepts into measurable, clear and specific observations
Order effects
When the order in which the participants are exposed to the different conditions affects the results (e.g fatigue/practice) = in repeated measures
Matched pairs + evaluation
Participants are put in pairs on a characteristic that the researcher has a reason to believe may affect the results. HAPPENS BEFORE EXP. Then one member of each pair is placed into each condition (e.g two people of the same age).
cons:
- if one half of the pair withdraws, the whole pair is lost
- matching pairs if very time consuming
- even though participants in different groups are matched on their similarities, some individual differences and participant variables may still occur.
pros:
- no order effects (different people are in each condition)
- demand characteristics are less likely
- paired individuals treated as if the same person so they can be compared and individual differences can be controlled.
standard deviation + percentages
How spread out numbers are in relation to the mean (different from range as any anomalies are included and gives us percentages of average)
- cannot be negative
-low sd = data is clustered around the mean
- high sd = data is more spread out
-1 +1 : 68.26%
-2 +2 : 95.44%
-3 +3 : 99.74%
Examples of measure of dispersion
Range - big number - small number
Standard deviation - how spread out the data is, in relation to the mean
Measures of central tendency + evaluation
Mean - add up data then divide by how many there are
PRO: most accurate as includes all data
CON: distorted by extreme values
CON: not appropriate with nominal data
Median - the middle number once put in chronological order
PRO: not affected by extreme values
CON: not as accurate as mean
CON: not appropriate with nominal data
Mode - most common value
PRO: useful for nominal data
CON: not useful if you have more than one mode
Pros + cons of range vs standard deviation
Range:
Pros: easy to calculate
Cons: distorted by extreme values
Standard deviation:
Pros: more accurate as all values are taken into account
Cons: may hide some characteristics of data (e.g extreme values) // harder to calculate and more time consuming
Graphs
Histograms ->demonstrate the ‘frequency density’ of continuous (interval/ratio) data.
- represents the frequency of each score (bell shape curve if less extreme = symmetrical, tails never touch x axis)
Scatter graphs ->used when doing a correlational (relationship) analysis .
- In a scatter graph is doesn’t matter which variable goes on which axis
- the scatter of the dots medicates the degree of correlation between the co-variables (using correlation coefficient—-> -1. -0.5. 0. 0.5. 1.
Frequency Polygon ->demonstrate the ‘frequency density’ of continuous data.
Bar charts ->demonstrate the difference in the frequencies of non-continuous (category/nominal/ordinal) data
Pie Chart ->demonstrate the frequencies of each category as a proportion (%) of the whole.
Line graphs -? Represents continuous data (iv = X axis , dv= Y axis)
Distribution of histograms
Normal distribution = symmetrical bell
Positive skew = most scores on left, elongated tail on right
Negative skew = most scores on right, elongated tail on left
Stratified sampling evaluation
PRO:
As selection occurs from sub-groups within a population and involves random sampling, selection is unbiased and therefore representative, making it easier to generalise findings.
CONS:
- Time consuming dividing population into stratums
- Detailed knowledge of population characteristics are required for stratified samples which may not be available.
Random sampling evaluations
PRO:
- More likely to be representative and not biases to a particular type of person as each individual has the same chance of being selected, therefore easier to generalise findings to wider target population, meaning it has a high population validity.
CONS:
- time consuming
- chance of representation decreases as the sample size increases (therefore may not be representative to wider target population)
Systematic sampling evaluations
PRO:
- unbiased and representative sample as results are representative of the population unless certain characteristics are repeated every nth person which is highly unlikely
CON:
- unbiased selection can only be ensure as long as the researcher has not intentionally biased the sampling system to only include certain types of people