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Research methods - PYB1
Section B
Q3
Answer all questions (20 marks)
1. Planning research: Aim, Hypotheses & VariablesPsychologists first aim to make predictions about a common sense explanation of behaviour. For example, “hmm, does eating fish really make someone smart like the advertisements say?”
Therefore the first thing a psychologist must come up with is an aim of her ‘what will be’ investigation:
1.1 AIM
“The researcher wanted to investigate whether eating fish could actually help improve a person’s intelligence”.
This is still quite a general statement so psychologists must make refine the aim and make it more testable, i.e. a hypothesis. Something that reads that can be tested in a laboratory experiment.
First, in order to make sure your hypothesis is EXACT is to know your Independent and Dependant variables. (See notes below hypotheses)
1.2 HYPOTHESES - There are two major types of hypothesis:
The experimental (or alternative) hypothesis. It is experimental because it predicts a RELATIONSHIP between the IV and the DV and it is alternative because it can be of two types:
Alternative Hypothesis
One – tailed Hypothesis
A statement that predicts there will be a difference (increase/decrease) in the DV, i.e. a specific direction
“Participants who eat fish at least three times a week will have higher IQ scores than participants who never eat fish”.
Two - tailed hypothesis
A statement that predicts there will be a difference between the IV and DV but it doesn’t say in which direction….
”Participants who eat fish three times a week will have different IQ scores than participants who do not eat fish”.
1.3 The second type of hypothesis is the Null hypothesis
A statement that predicts no difference between the IV and the DV that will be tested in the investigation.
“Participants who eat fish at least three times a week will not have different scores from participants who never eat fish at all”.
1.4 Correlational hypothesis
Are used to establish a relationship between two variables. The wording of a hypothesis for correlational studies differs from previous hypotheses:
‘There will be a correlation between the number of portions of fish eaten by participants and their IQ score’s. (2-tailed)
There will be a positive correlation between the number of portions of fish participants eat and their IQ scores. (1-tailed)
Can you think of another 1-Tailed hypothesis for this scenario?
1.5 Variables
A variable is any object, quality or event that can change or vary in some way. For example, aggression, intelligence, time, temperature, eye-colour, amount of alcohol or attraction etc…. All experiments have two variables:
Independent variable (IV): The variable that is manipulated – changed (cause)
Dependant variable (DV): The variable that is measured (effect)
What are the IV and DV in the experimental hypotheses above?
1.6 Extraneous Variables (EV)
‘Any variable other than the IV that could affect the outcome of the study (DV)’. These variables must be controlled or kept the same in both conditions….so that we can say for sure the IV was the only variable that affected the outcome.
1.7 Confounding variables
Any EV not controlled will develop into a confounding variable and these will affect the outcome. This will make it difficult to establish a cause effect relationship with certainty..
Confounding Variables can be classified as two types Subject variables: Anything to do with the individual participant, i.e. fatigue, tired, boredom, IQ, age, gender, lack of motivation etc
Situation variables: Anything to do with the situation, for example, too warm, cold, bright, dark, noise, distractions etc…
2. Populations and sampling techniques
2.1 Target Population
The group of people whose behaviour we are interested in measuring. A small group of people from that target population must be selected for investigation who will be representative of that population.
When the results of that investigation are analysed, it is possible to generalise from that small group and say these results apply to the target population.
A sample is a small group of people who are gathered to take part in the investigation. We must refer to these people as ‘participants’.
2.2 Sampling Bias
If the sample is not representative of the target population, its is said to be biased. For example, some characteristic of the sample was either over or under-represented.
For example, if a sample of 20 teachers consisted of 16 males and 4 females, the females would appear to be under-represented in that sample.
2.3 Six main methods of collecting participants – You must know at least 4. However, Self-selecting and cluster methods are also possible
Random sampling
Definition Every member of the identified target population has an equal chance of being selected or the sample. This could be achieved by putting all names in a hat, mixing it up and picking names out.Example For example putting all AS year 12 students studying psychology in the UK into a database, number each person then pick random numbers.
StrengthsThe sample is unbiased, so the participants should be representative of the target population and therefore we can generalise their results to the population.
Weakness It can be time consuming and costly particularly if participants refues to take part.The sample may not be representative of the original target population, for example a random sample of 20 students might end up exclusively female.
Opportunity sampling
DefinitionThe sample selected consists of people who are available and willing to take part. Or imply those available at the time. This is not a random sample, because the researcher has chosen them.Example University lectures will use their students as participants for a study.StrengthsIt is quick, convenient and often the most economical method of sampling.
WeaknessOpportunity sampling gives very unrepresentative samples and is often biased on part of the researcher who may choose subjects who will be ‘helpful’ and so again it would be difficult to generalise these findings to all people in the population.
Systematic sampling
DefinitionMeans the every nth member of the target population is selected. For example every fifth person on a register. ExampleFor example every 4th baby born in a maternity hospital in one week.
StrengthThis sampling method is relatively unbiased since and it is only those who are in the relevant position can be selected. Second it is faster than random sampling.
WeaknessIt is less quick and convenient than opportunity sampling, for example participants do not stand an equal chance of being selected if the starting point of the sample is not conducted at random.
Stratified sampling DefinitionIt involves dividing the target population into important sub-categories (strata/types) and then selecting people from that stratum (group) in the proportion that they occur in the target population. ExampleFor example, if a target population contained 75% women and 25% men, a sample of 100 people would contain 75 women and 25 men.StrengthsIt produces a representative sample of key groups in the target population.
WeaknessIt is time consuming, since pre-
research studies are required to establish the necessary proportions of each group.
METHODS OF INVESTIGATION I
3. The experimental method3.1 Laboratory Experiment
An environment where all variables are controlled (IV and DV)…. FOR EXAMPLE Bandura’s Bo Bo dolls :
1. Indicates cause and effect2. Easy to replicate3. Lacks ecological validity4. May involve demand characteristics
3.2 Field Experiment
The researcher manipulates the IV in the participant’s natural environment…. For example Pivilian train station study:
1. High ecological validity2. Avoids demand characteristics3. Difficult to replicate4. Ethical problems of consent, deception, invasion of privacy etc.
3.3 Quasi-experiment
The IV is changed by natural occurrence. The researcher just records the effect on the DV. For example study in Canada where TV was introduced and their behaviour was measured before and after it was introduced.
1. High ecological validity2. No demand characteristics
3. Difficult to infer cause and effect4. Ethical problems of consent, deception and invasion of privacy etc
4. Experimental Design
In an exam, once you have read the study provided, you may be asked to specify what ‘experimental design’ was used by the researcher. There are three types of experimental design and all it means is ‘How participants are allocated into the different conditions of an experiment. That is how participants are randomly allocated into the experimental or control condition. You could then be asked to give an advantage or disadvantage to the design used. The three are as follows:
4.1 Independent measures design
Different participants are randomly allocated to each condition. So each participant only takes part in one condition (experimental or control).
4.2 Repeated measures design
The same participants are used in both (or more) conditions. So each participant is used in all conditions. (They first do the experimental condition, then do the control condition)
4.3 Matched pairs design
This is similar to independent measures but this time the participants who are randomly allocated to each condition are matched on certain variables i.e. age, gender, background etc.
Strengths weaknesses
Independent groups design (unrelated)
No order effects (such as boredom, fatigue or practice)
Reduced demand characteristics
Can’t use same materials in both conditions
Some participant variables may be uncontrolled
Repeated measures design (related)
Can use same materials in both conditions
Participant variables kept constant Fewer participants needed
Order effects (such as boredom, fatigue or practice)
Demand characteristics
Matched pairs design Some participant variables kept constant
No order effects (such as boredom, fatigue or practice)
Reduced demand characteristics
Loss of one participant means loss of pair
Can’t use same materials in both conditions
Time consuming
5. Controls
5.1 Counterbalancing is used to prevent order effects (fatigue, boredom and practice) from distorting the results in the repeated measures design.
For example half the participants are given condition A then B and half are given condition B then A. This means the chances of boredom, fatigue or practice are halved because we can say with mire certainty that the results are due to the IV, not the practice etc…
5.2 Demand Characteristics may occur because the participant appears to be more helpful or a researcher who talks or behaves in a way that might influence the participants behaviour and so jeopardise the results. Demand characteristics can be reduced by using a single blind procedure. The participants do not know what condition they have been placed, i.e. they do not know if they are in the alcohol or no alcohol condition.
5.3 Investigators can also jeopardise the results by experimenter expectancy or observer bias. For example, experimenter expectancy is when the experimenter behaves in a way that pushes the participant to act in a certain way. The observer bias is when a researcher will ignore certain behaviours of the participants or interpret their behaviours in a certain way or even corrupt the results according to their expectations. Using a double blind procedure can reduce these problems. Neither participants nor experimenter know what conditions the participants are in or the hypothesis being tested.
6. QuestionnairesQuestionnaires are written methods of gaining data from subjects that do not necessarily require the presence of a researcher. For example, a researcher might be interested in finding out about people’s thoughts and feelings about happiness. There are three types of questionnaires
Open questions:
Can you tell me about how happy you feel right now?
Closed questions
Do you feel happy right now? Yes ____ No ____
Likert attitude scale question:
On a scale of 1 – 50, where 25 represents your normal level of happiness and 50 is the most happy you could feel, estimate how happy you feel right now.
Strengths
Collates large amounts of data very quickly and conveniently
Highly replicable and easy to
Weaknesses
Self report data may be biased by the motivation levels of the participants responding
score into quantitative form Provides rich detailed data - new
insights into further research
Social desirability may affect the responses (ticking yes to all the questions to be viewed in a socially accepting manner)
Poor response rate
7. InterviewsThese essentially gather the same information as questionnaires but are conducted face to face. They may be used if the researcher has more time available and or wants to obtain more personalised views.
There are two main types of interviews
7.1 Structured Interviews
Used more in research (market research) and uses a standard set of fixed predetermined questions with certain ways of replying (yes/no). This means data from all the participants can be collated and summarised statistically as they were all asked the same questions.
7.2 Unstructured interviews
Used more in therapeutic situations (psychodynamic therapy) and will contain a topic area for discussion but will not have any fixed questions or ways of answering.
The strengths and weaknesses of unstructured interviews
Strengths Provides rich detailed data Extremely flexible Participants input is unrestricted
and so new ideas can be explored Clarification of issues can be
explored
Weaknesses Self report - biased Experimenter expectancy -
Demand characteristics Difficult to quantify and analyse Not replicable, generalisable
Now try to think of the strengths and weaknesses for structured interviews!!!
Strengths
Weaknesses
8. Correlations8.1 Correlations are used to see if there is a pattern or relationship between two sets of variables. It aims to find a cause and effect relationship BUT IT DOES NOT
ESTABLISH A CAUSE AND EFFECT RELATIONSHIP in the same way an experiment can…..
None of the variables have actually been manipulated in any way, but are simply recorded. Correlations investigate naturally occurring phenomena and situations that would be unethical to create in an experiment. For example, the relationship between TV and violence.
The hours of violent TV would be recorded as one variable and the number of violent acts would then be recorded as the second variable. A visual description of these measurements would be displayed on a scattergram.
A strong positive correlation is where the score for one variable increases, so does the other. For example hours of study, better the grade on a test.
A strong negative correlation, is where the score for one variable decreases, the other variable increases. The older the car, the cheaper its value for sale.
Copy a zero correlation: This is where there is no relationship between the two variables, as one increases the other neither decreases or increases. For example the colour of hair and intelligence.
8.2 Please note – you may be asked to either interpret the data on the graph or to actually draw a graph. Label axes clearly and be as accurate as possible.
For example, a scattergram to show the table to show the time taken to complete a card sorting task according to age.
Strengths
No manipulation of variables/behaviour is required
Strong correlations can suggest ideas for future experiments to determine a cause and effect relationship
Weaknesses
No cause and effect relationship can be inferred – relationship may be due to other variables
9. Observational studies
Participant observation
Non-participan
t observatio
n
Overt Covert
Definition The experimenter becomes part
The experimenter watches the
Overt means obvious – the participants
Covert means concealed – the researcher does
of the observation.
behaviours from afar.
know they are being watched
not tell the participants she is a researcher
AdvantagesHigh ecological validity if done in a natural setting
Can give an insight into future areas of study
Can provide richer data than experiments
High ecological validity if done in a natural setting
Can give an insight into future areas of study
Can provide richer data than experiments
Can give an insight into future areas of study
Can provide richer data than experiments
High ecological validity if done in a natural setting
Can give an insight into future areas of study
Can provide richer data than experiments
Disadvantages
No control of variables so difficult to determine clear cause and effect
Open to observer bias
Open to observer bias
Demand characteristic
s if participants know that they are
being watched
Ethical problems – invasion of privacy
10. Quantitative vs. Qualitative dataQuantitative data is in the form of numbers/charts, i.e. laboratory experiments, structured interviews etc produce this kind of information.
Qualitative data is in the form of written description i.e. case studies, unstructured interviews etc that produce lots of written information
Some argue human behaviour should be expressed in the form of numbers so that we can scientifically establish a cause effect relationship. However, qualitative research provides rich detailed data which is also helpful when making conclusions. Usually combinations of both methods are used.
11. Pilot study
A pilot study is a small practice scale study conducted before the actual main study/experiment. Its aim is to check and make sure everything involved in the study has been appropriately dealt with, including control of all extraneous variables. For example it will discover any ambiguous questions, anything that might be offensive, too difficult to understand, check how long it takes to do the experiment etc…
12. Case StudyA case study is a detailed in-depth study of an individual/small group or organisation. Case studies have many roles. The information gathered will then be used to find new themes/ideas/patterns and these will then be used to develop new theories. It can be used to support/refute previous theories. Or it could be used to decide which therapy is appropriate for an individual. Little Hans – is a famous case study.
Strengths
Produces rich detailed information May produce new insights and
ideas Is one person centred
Weaknesses
Difficult to generalise Retrospective data may be
unreliable Too subjective
13. Representing dataThis section introduces descriptive statistics and graphs to display and summarise the results of an investigation. This will show the reader any patterns or differences that may be present.
When data is in numerical form the researcher must convert that raw data into a statistical result which will summarise the results. The summary of or descriptive statistics commonly used are called measures of central tendency and measures of dispersion.
13.1 Measures of central tendency
These provide a single value to describe a set of raw scores:
Measure of central tendency
Advantages Disadvantages
Mean (add all the scores then divide by the number of scores)
It is a sensitive statistic – using all the data
If one score is extremely high or low it may distort the mean value
Median (put all scores in order from lowest to highest, then identify the middle value.
Is not distorted by extreme values
Is an actual score
Can be distorted by small samples and is less sensitive
Mode (score which occurs most often)
Not influenced by extreme scores
Does not use all scores
13.2 Measures of dispersion
Sometimes the mean, median and mode may be the same for both conditions, which would suggest there are no differences between those two sets of scores. However looking at the raw data one could easily see that in the first condition the values were very similar to each other and in the second condition, the values were very spread out. In order to summarise these sets of data, we need a statistic that displays this difference in spread or dispersion.
Measures of dispersion Advantage DisadvantageRange (The largest score minus the lowest score +1)
Simple to calculate Distorted by extremely high or low scores
Standard Deviation (the difference between each score in a condition and the mean value for each condition)
A sensitive statistic - uses all the data available
Time consuming
Ethical guidelinesIn Britain the BPS, British Psychological Society (1993) published the ‘Ethical principles for Conducting research with Human participants’ which guides psychologists to consider the implications of their research for the participants concerned.
Make notes on the following ethical guidelines:Informed consentPsychologists must get full consent from their participants before starting their experiment. This can be verbal or written. If under 16yrs o age consent must be gained from a guardian.
DeceptionParticipants must not be deceived about the nature of the experiment. However sometimes it is necessary so that genuine behaviour can be observed. So long as the experimenter can argue the ends justify the means.
DebriefingParticipants must be told the full purpose of the experiment at the end and have time to ask any questions at the end of the experiment.
Withdrawal from the investigationParticipants should be told from the very start they have the right to leave at any time if they do not feel comfortable with the experimental conditions.
ConfidentialityParticipants should be told that their results will be kept in the strictest of confidence and can be destroyed at their
Protection from harmAll participants should leave the experiment in the exact same physical and mental state in which they entered
convenience. the experiment.
Correlations
Correlations are used to see if there is a pattern or relationship between two sets of variables. It aims to find a cause and effect relationship BUT IT DOES NOT ESTABLISH A CAUSE AND EFFECT RELATIONSHIP in the same way an experiment can…..
None of the variables have actually been manipulated in any way, but are simply recorded. Correlations investigate naturally occurring phenomena and situations that would be unethical to create in an experiment. For example, the relationship between TV and violence.
The hours of violent TV would be recorded as one variable and the number of violent acts would then be recorded as the second variable. A visual description of these measurements would be displayed on a scattergram.
Copy a strong positive correlation: As the score for one variable increases, so does the other.
Copy a strong negative correlation: As the score for one variable decreases, the other variable increases.
Copy a zero correlation: This is where there is no relationship between the two variables, as one increases the other neither decreases or increases.
Besides displaying the information on a scattergram a correlation coefficient could be calculated to find out the extent of the relationship between the two variables. Correlation coefficients may be any figure between +1 and -1. The closer the coefficient is to +1 or -1, the more perfect the relationship is. The closer it is to 0, the weaker it is…….
For a correlation coefficient of +1 every rise in variable A is reflected in a rise in variable BFor a correlation coefficient of -1 every rise in variable A is reflected in a fall in variable BFor a correlation coefficient of 0, there is no relationship between variable A and variable B
Copy diagram from board…..In the example earlier, there may be a strong positive correlation between TV and violent acts, giving a coefficient of 0.95, but this does not mean watching TV makes people violent. The hypothesis needs to worded in such a way as to not indicate a cause and effect relationship.
Two tailed alternative (correlational hypothesis)
There will be a correlation between the hours of TV watched and the number of violent acts committed by children aged 6-10 years.
One tailed alternative (correlational hypothesis)
There will be a positive correlation between the hours of TV watched and the number of violent acts committed by children aged 6-10 years.
Strengths
No manipulation of variables/behaviour is required
Strong correlations can suggest ideas for future experiments to
Weaknesses
No cause and effect relationship can be inferred – relationship may be due to other variables
determine a cause and effect relationship
1. Draw a scattergraph to display the data from study C of the life events and stress study. (3 marks)
Label a title for the scattergramLabel both AxesApproximately give the correct scale and location of the data points on the scale
2. Identify the type of correlation shown in your scattergram and state what it shows about the relationship found between stress and illness. (2 marks)
3. State one strength and one limitation of the correlational method. (2 marks)
4. State a hypothesis for this study (2 marks)
Representing data
This section introduces descriptive statistics and graphs to display and summarise the results of an investigation. This will show the reader any patterns or differences that may be present.
When data is in numerical form the researcher must convert that raw data into a statistical result which will summarise the results. The summary of or descriptive statistics commonly used are called measures of central tendency and measures of dispersion.
Measures of central tendency
These provide a single value to describe a set of raw scores:
Measure of central tendency
Advantages Disadvantages
Mean (add all the scores then divide by the number of scores)
It is a sensitive statistic – using all the data
If one score is extremely high or low it may distort the mean value
Median (put all scores in order from lowest to highest, then identify the middle value.
Is not distorted by extreme valuesIs an actual score
Can be distorted by small samples and is less sensitive
Mode (score which occurs Not influenced by extreme Does not use all scores
most often) scores
Activity
Using the data from the reaction times and alcohol study, calculate the mean, median and mode for both conditions. Draw a table to show these averages with an accurate and detailed title below. Use the table below to help the layout your table…..(3 marks)
Condition AMemory rehearsal
Condition BMental image
Mean
Median
Mode
14.67
15
16
9.13
9.5
10
Graphical displays – see handout
Activity
Draw a bar chart to display the data from the alcohol and reaction times study. Use phrases from the stimulus material to make sure you label the axes and give an accurate title:
A bar chart to show the mean …….
Past exam questions
1. What might be the psychologist’s interpretation of the data shown in the bar chart (2 marks)
2. State one limitation of using the mean as a measure of central tendency. (1 mark)
Measures of dispersion
Sometimes the mean, median and mode may be the same for both conditions, which would suggest there are no differences between those two sets of scores. However looking at the raw data one could easily see that in the first condition the values were very similar to each other and in the second condition, the values were very spread out. In order to summarise these sets of data, we need a statistic that displays this difference in spread or dispersion.
Measures of dispersion Advantage DisadvantageRange (The largest score minus the lowest score +1)
Simple to calculate Distorted by extremely high or low scores
Standard Deviation (the difference between each score in a condition and the mean value for each condition)
A sensitive statistic - uses all the data available
Time consuming
To calculate the standard deviation follow these 7 steps:
a) Draw a table with three columns and place all the scores in rank order in the first column (as below)
b) Calculate the meanc) Subtract the mean from each score (d)d) Square each deviation (d²)e) Find the sum of (Σ) the squared deviations d²f) Divide this result by N – 1 (for the population estimate)g) You have now found the variance, the standard deviation is found by finding the
square root.
So to work out the standard deviation of the reaction times in milliseconds for the with alcohol condition:
a) Place scores in rank order and put onto a table as below:
b) Calculate the mean:Mean = 16 + 17 + 18 + 18 + 19 + 20 + 20 + 21 + 23 + 47
10
Mean = 219 10
Mean = 21.9
Reaction time in milliseconds (group 1)
d d²
16 -5.9 34.8117 -4.9 24.0118 -3.9 15.2118 -3.9 15.2119 -2.9 8.4120 -1.9 3.6120 -1.9 3.6123 1.1 1.2147 25.1 630.01Mean = 21.9 Σd² = 736.09
c) In the d (deviation) column, subtract the mean from each score: For example, (16-21.9) = -5.9. Do this for all scores.
d) Square all the values from the d column and put your answers in the d² (deviation squared) column. For example, -5.9² = 34.81
e) Find the sum of all the values for the squared deviations - Σd² (i.e. add up all the answers) = 736.09
f) Now follow the formula:
S = √Σd² N-1
S = √736.09 10-1
S = √736.09 9
g) S = √81.79
S = 9.04
Activity
Now using the data from the reaction times and alcohol study, calculate the range and standard deviation for the without alcohol condition (group 2).