GCSE Psychology (AQA) 2024: Research Methods Part 2

• some questions decided before but follow up questions emerge at certain points

• "goes with the flow"

strengths:
-produce a lot of info
-insight gained into thoughts and feelings
weaknesses:
-data can be difficult to analyse
-people may feel uncomfortable talking face to face

prepared list of questions which can be answered in writing, over the phone, internet ect

• more likely in an interview

• no fixed range of possible answers

• respondents are free to reply in any way they wish

• produces qualitative data

• more likely in questionnaires

• fixed range of possible answers

• use rating scales, yes/no ect

• produces quantitative data

strengths:
-can gather info from many people quickly
-questionnaires produce data that is easier to analyse than interviews (closed questions)-easier to make comparisons
weaknesses:
-social desirability bias (might not answer truthfully and give the answers they think is more appropriate/puts them in a better light---low validity
-may be unclear
-maybe be leading questions---lacks validity

a question that prompts or encourages the desired answer

a researcher watches or listens to participants and records data

researcher records behaviour where it would normally occur

researcher manipulates aspects of the environment

participants aren't aware behaviour is being recorded

told that they're being recorded in advance

researcher is part of the group being recorded

researcher remains separate from the group that is being recorded

• systematic method of collecting data

| - target behavior broken into separate observable changes

• observational studies should be carried out by more than one researcher because bias can be a problem

• a single researcher may overlook important details or only record data that fits expectations

• the group or pair should produce the same records of behavior

• they would watch at the same time, have the same categories of behaviour and correlate data

strengths:
-greater validity as it's based of what people actually do (opposed to what they say they do which they could lie about)
-covert and natural observations show real life behaviour so it has high validity
weaknesses:
-ethical issues as researchers cant gain consent if they're observing in a public place which invades their privacy
-observer bias:observers expectations affect validity as they may only record data that fits their expectations or may look over details

shows how things are linked together (associations)

• analysis of qualitative (numerical) data

| - continuous data

• a special graph used to plot correlation data

• one co-variable is on the x-axis and the other is on the y- axis

• a dot is placed where they meet

as one co-variable increases the other increases

as one co-variable increases the other decreases

no relationship between co-variables

strengths:
-allows psychologists to carry out investigations on things that cant be experimented on
-high level of ecological validity as nothing is set up or manipulated
weaknesses:
-don't show cause and effect
-no control over EVs so wrong conclusions may be drawn

in-depth investigation of an individual, group, event or institution

• collect information about people's experiences in words

• data in a case study may be describing past events or interviews (qualitative)

• some case studies may involve experimental testing that produces quantitative data (eg. intelligence tests which produce a numerical score) to see what the person can and cant do

• often carried out over a long period of time so we can see how behaviour changes

• may also collect retrospective case history

strengths:
-triangulation:more than one method to collect data on the same topic
-naturally occurring
-high ecological validity
-rich and detailed qualitative data
weaknesses:
-lack of replication (every case is relatively unique)
-cant generalise findings (as it deals with only one person/group/event so you can never sure whether conclusions drawn from this case can be applied elsewhere)
-time consuming
-subjectivity bias causes low validity (findings are based on the psychologists opinion)

• if it can be repeated its reliable

| - measure of consistency

• tend to be more reliable

• lab experiments are controlled and easy to repeat exactly

• interviews/questionnaires: same person should answer the same questions in the same way- closed questions are likely to be more reliable

• observations:one or two observers (interobserver reliability) should produce the same observations if repeated

• less reliable

| - case studies and unstructured interviews are difficult to repeat in the same way

relates to whether a result is a true reflection of "real world" behavior

• the sample may not represent the target population

• high representativeness=stratified

• low representativeness=opportunity

• repeated measures:order effects challenge validity (can be overcome by counterbalancing)

• independent groups:participant variables challenge validity (can be overcome by random allocation)

• matched pairs:overcome both problems (though it isn't perfect)

• lab experiments: task, setting and participant awareness challenges validity=reduces naturalness and has high control

• field experiments:artificial tasks and lack of control of extraneous variables challenge validity=more natural

• methods for producing numerical data (eg questionnaires) lack validity as they reduce behavior to a score=therefore we get little to no insight into other aspects of their behavior

• case studies have greater validity as they give deeper insight into behavior, thoughts and the participant's point of view

• difficult to analyse=reduces validity, findings are fairly subjective and the researcher's own expectations influence the analysis

• quantities (numbers)

| - can measure thoughts/feelings

strengths:
-easy to analyse an draw conclusions
-straightforward to make comparisons
-statistics are open to less interpretation=less chance of bias
weaknesses:
-lacks depth and detail
-lacks validity
-doesn't reflect real world complexity

• data in words

| - can be turned into numbers by counting themes

strengths:
-more depth and detail
-better insight (participant is free to fully express thought)
-more validity
weaknesses:
-difficult to analyse and summarise (a lot of material)
-difficult to draw conclusions
-conclusions may be based on the researcher's own opinions=open to bias

• data that has been obtained first hand

| - collected data matches the aim of the study (collected for the purpose of the research)

strengths:
-suits the aims of the research=more useful
-authentic
weaknesses:
-takes time and effort to collect
-costly
-much easier and quicker to use that is already validated

• second hand data

• from other studies or government statistics

• already exists

strengths:
-easy and convenient to use
-saves expenses
-saves time
-little effort
weaknesses:
-may not fit what the researcher is investigating 
-secondary data may be out-of-date, not quite complete or of poor quality=may waste time

express numbers in a way that shows the overall pattern

• spread of data

| - arranges data in order and subtracts the lowest from the highest score

strengths:
easy to calculate
weaknesses:
-can be distorted by extreme scores

• mathematical average

| - add up all the scores and divide by the number of scores

strengths:
-uses all the data (most sensitive measure)
weaknesses:
-can be distorted by extreme values

• data put in order from lowest to highest

| - middle value

strengths:
-not affected by extreme scores
weaknesses:
-less sensitive than the mean to variation in values

most common score

strengths:
-very easy to calculate
weaknesses:
-can be unrepresentative

-frequency: number or times it occurs-frequency tables are a systematic way to organize data in rows and columns

• histogram: continuous categories, no spaces between bars

• bar chart: bars can be in any order

• normal distribution: symmetrical spread forms a bell shape with mean, median and made at peak

• any number written with a decimal

• position represents value

• eg. 0.2

• reduce to simplest form

| - eg. 1/2

• way to express fractions

| - 8:2=4.1

• fractions out of 100

| - eg. 58%

add all scores and dividing by the number of scores

mathematical shorthand to represent very large or very small numbers

numbers expressed to the required degree of accuracy eg.2 significant figure:

• 32,462=32,00

• 0.003256=0.0033

rough calculation