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Research Methods

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Research Methods Revision Guide

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Experimental Research Methods, including: Laboratory Experiment – features, and strengths and weaknesses; Field Experiment – features, and strengths and weaknesses; Natural Experiment – features, and strengths and weaknesses;

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Non-Experimental Research Methods, including: Correlational Study – features, and strengths and weaknesses; Observational Study – features, and strengths and weaknesses; Questionnaire – features, and strengths and weaknesses; Interview (3 types) – features, and strengths and weaknesses; Case Study – features, and strengths and weaknesses.

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3 Writing aims for experiments;

4 Writing operationalized hypotheses for experiments, including: directional and non-directional hypotheses;

5 Be able to identify independent and dependent variables;

6 Know how to control/eliminate extraneous variables and their control;

7 Demand characteristics and investigator effects;

8 Identify and evaluate experimental design (repeated measures, independent groups, matched pairs);

9 Sampling techniques, including random, opportunity and volunteer;

10 Design of observations, including the use of behavioural categories;

11 Design of questionnaires, including the question wording;

12 Design of interviews, including the question wording;

13 Reliability – what it is and how it’s tested;

14 Validity – what it is and how it’s tested;

15 The BPS Code of Ethics and the main ethical issues in Psychology & How ethical issues are dealt with by researchers;

16 Pilot studies – features and why researchers carry them out.

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17 Presentation and interpretation of quantitative data including, graphs (bar chart, histograms, scattergram) and tables;

18 Analysis of quantitative data - measures of central tendency (mean, mode, median) and measures of dispersion (range, SD);

19 Analysis and interpretation of correlational data, including correlation co-efficient;

20 Analysis of qualitative data – The process/problems of content analysis.

Mr J. Robinson

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1. Experimental Research Methods

Laboratory Experiment A laboratory experiment is a controlled situation in which the researcher manipulates one variable (IV) to measure its effect on another (DV).

Field Experiment A field experiment takes place outside a laboratory, in a natural setting. However, the basic scientific

procedures are still followed:

The Independent Variable is manipulated;

To measure the effect on the Dependent variable.

However, field experiments don’t have to take place in a field – they can take place anywhere outside a

laboratory.

Natural Experiment In a natural experiment the researcher makes use of naturally occurring variables. These are not true

experiments because the research cannot really manipulate the IV. They are sometimes referred to as a

“Quasi-Experiments”.

FEATURES STRENGTHS WEAKNESSES

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Researcher controls as many variables as possible. Usually conducted in a laboratory, using standardised procedures where the IV is directly manipulated. The experimenter aims to control as many extraneous variable as possible.

High degree of control, both for the IV and EVs (extraneous variables). High level of replication - replication of procedures is relatively easy. The relationship between the IV and the DV can be determined and therefore the research can conclude cause and effect.

Can lack ecological validity - due to high levels of control, the situation can be very artificial and may not represent real life. There is a higher chance of experimenter bias and demand characteristics (in comparison to field and natural experiments).

Mr J. Robinson

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FIE

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An experiment performed in the natural environment, where the experimenter still manipulates the IV.

Higher level of ecological validity. Due to natural setting, results are more likely to show true behaviour and results can be generalised to other situations. Reduces demand characteristics as the participants are less likely to guess the aim of the experiment, when in their natural environment. The relationship between the IV and the DV can be determined and therefore the research can conclude cause and effect.

Less control over extraneous variables and therefore other factors may influence the results (DV). Low level of replication - replication is difficult as conditions are unlikely to be exactly the same again.

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An experiment performed in the natural environment. The researcher makes use of naturally occurring variables – this is a quasi-experiment, not a real experiment – there is no manipulation of the IV.

Useful when it would be unethical or impossible to manipulate the independent variable – often used in stress research. Higher level of ecological validity. Due to natural setting, results are more likely to show true behaviour and results can be generalised to other situations. Reduces demand characteristics as the participants are less likely to guess the aim of the experiment, when in their natural environment.

Less control over extraneous variables and therefore other factors may influence the results (DV). A causal link between the IV and DV can NOT be established (as the research has not manipulated the IV) and therefore cause and effect cannot be established.

2. Non-Experimental Research Methods

Correlational Study See Point 19.

Observational Study See Point 10.

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Questionnaires See Point 11.

Interviews See Point 12.

Case Studies Case studies involve an in-depth study over time, usually of an individual or small group. They are usually

undertake within a real-life context. Psychologists typically use the following methods when conducting

case studies - interviews and observations which produce qualitative (non-numerical) data. In your

course you will encounter the following case studies:

Clive Wearing

Genie

Patient KF

The Czech Twins

Strengths Weaknesses

Rich & Interesting Data – Case studies have a high degree of realism and can provide new insights into existing areas of study i.e. memory.

Low Reliability – The findings of a single case study are unlikely to be replicated even when similar cases are studied. Therefore, it is difficult to generalise.

Challenging Existing Theories – If the findings of a single case study contradict a well-established theory, then we have to consider modifying the theory to accommodate the new evidence.

Subjective – Case studies are often based on lengthy interviews, during which a relationship between the researcher and the ‘case’ may be established, this can lead to investigator effects / research bias.

3. Aims Before a researcher considers his/her aim, there is always a research question they are trying to answer,

for example: ‘Does hunger affect memory for different types of words?’

Thereafter, the researcher creates their aim: To examine the effect of hunger on memory of food

related words.

HINT: ALWAYS START YOU AIM WITH…TO EXAMINE THE EFFECT OF [INSERT IV] ON THE [DV].

4. Operationalised Hypotheses A hypothesis is a prediction about the variables in the study. The hypothesis SHOULD show the

INDEPENDENT VARIABLE and the DEPENDENT VARIABLE. For example, if the aim of the study was: to

examine the effect of hunger on the memory of food related words, the IV is hunger (hungry vs. not

hungry) and the DV is the number of food related words correctly recalled.

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Directional There will be an increase in the number of food related words correctly recalled for the participants who

are hungry, in comparison to those who are not hungry.

** Note: In the above directional hypothesis the IV (hunger) is shown to increase the DV (number of food

related words correctly recalled) **

HINT: ALWAYS START A DIRECTIONAL HYPOTHESIS WITH…THERE WILL BE AN INCREASE/DECREASE…

Non-Directional There will be a significant difference in the number of food related words correctly recalled for

participants who are hungry in comparison to those who are not hungry.

** Note: In the above non-direction hypothesis the IV (hunger) is shown to have an effect on the DV

(number of food related words correctly recalled) BUT we have not said if the effect will be more/less

(increase of decrease) **

HINT: ALWAYS START A DIRECTIONAL HYPOTHESIS WITH…THERE WILL BE A SIGNIFICANT

DIFFERENCE…

5. Independent (IV) and Dependent Variables (DV) Independent Variable (IV) – The variable that the researcher manipulates and which is assumed to have

a direct effect on the dependent variable (DV).

Dependent Variable (DV) – The variable that the research measures. The variable that is affected by

changes in the independent variable (IV).

NOTE: It is exceptionally unlikely that you will be asked to define what is meant by the terms

independent and dependent variable. You are more likely to be asked to IDENTIFY the IV and DV within

an extract, for example: Q8. A psychologist showed participants 100 different cards, one at a time. Each card had two unrelated words

printed on it, eg DOG, HAT. Participants in one group were instructed to form a mental image to link the words.

Participants in the other group were instructed simply to memorise the words. After all the word pairs had been

presented, each participant was shown a card with the first word of each pair printed on it and asked to recall

the second word.

(a) What is the independent variable (IV) in this study? (2)

The type of memory strategy (1 mark) – those instructed for form a mental image to link the

words VS. Those instructed to simply memorise’ (1 mark).

(b) What is the dependent variable (DV) in this study? (2)

Recall (1 mark)

The number of words correctly recalled (2 marks).

HINT: Before answering any question where you have to identify the IV and DV, read the extract carefully

and highlight your IV and DV. Once you have decided your IV, in this case ‘the type of memory strategy –

those instructed for form a mental image to link the words VS. those instructed to simply memorise’ – ask

yourself the following question: Is it possible for the experimenter to MANIPULATE this variable? If your

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answer is ‘yes’ then this is likely to be your IV. If your answer is ‘no’ (it wouldn’t be possible for the

experimenter to manipulate or change this variable) then this is unlikely to be your IV.

6. Extraneous Variables Extraneous variables (EVs) is a general term for any variable, other than the IV, that might affect the

results (the DV). Where EVs are important enough to cause a change in the DV, they become confounding

variable(s).

There are a number of different types of EVs that psychologists need to take account of when designing

their investigations:

Situational variables – these are variables connected with the research situation, for example

the temperature, time of day, lighting, materials, etc. Situational variables are controlled

though standardisation, ensuring that the only thing which differs between the two groups is

the IV. For example making sure that the temperature is the same, the time of day is the same

etc.

Participant variables – these are variables connected with the research participants, for

example age, intelligence, gender etc. Participant variables are controlled through the

experimental design – such as matched-pairs design (where participants in one condition are

matched to someone similar in the other condition) or by randomly assigning participants to

conditions, which helps to reduce bias.

7. Demand Characteristics and Investigator Effects

Demand Characteristics Demand characteristics occur when the participants try to make sense of the research and act

accordingly to support the aim of the research. Demand characteristics are a problem as the participants

behave in a way to support the hypothesis, making the results less valid.

Conversely, the participant may deliberately try to disrupt the results, a phenomena known as the ‘screw-

you’ effect.

Investigator Effects Investigator effects are where a researcher (consciously or unconsciously) acts in a way to support their

prediction. This can be a particular problem when observing events that can be interpreted in more than one way

(for example, one research might observe children fighting as an act of violence, while another might observe

rough and tumble play).

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8. Experimental Design

Independent Groups Different participants are used in each condition of

the experiment.

This should be done by random allocation, which

ensures that each participant has an equal chance of

being assigned to one group or the other.

Independent measures involves using two separate

groups of participants; one in each condition.

Strengths Weaknesses

Avoids order effects (such as practice or fatigue) as participants only take part in one condition of the experiment and therefore they are less likely to become bored and give up. As a result their performance should NOT be affected in an independent groups design.

More participants are required as different participants take part in the different conditions of the experiment, making the design more costly and time consuming.

Reduces demand characteristics as participants are only taking part in one condition of the experiment and they are less likely to guess the aim of the experiment and display demand characteristics, making the results more valid.

Participant variables may affect the results. For example, differences in age, sex or social background may affect the results acting as an extraneous variable.

Repeated Measures The same participants take part in each condition of

the experiment.

This means that each condition of the experiment

includes the same group of participants.

Strengths Weaknesses

Fewer participants are required as the same participants take part in the different conditions of the experimenting, making the design less costly and time consuming.

There may be order effects. Participants may experience orders effectives such as practice effects or fatigue from taking part in both conditions.

Participants who experience practice effects may perform better in the

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second conditions as they know what is expected of them;

Participants who experience fatigue (boredom) may perform worse in the second condition, because they give up.

Reduces participants variables as the same participants take part in both conditions of the experiment and therefore any differences in age, sex or social background are reduced, as they are the same in both conditions.

Increased chance of demand characteristics as participants are taking part in both conditions of the experiment. As a result they are more likely to guess the aim of the experiment and display demand characteristics, making the results less valid.

How might we combat ‘order effects’?

EXTENSION: COUNTERBALANCING:

To combat order affects the researcher counter balances the order of the conditions for the

participants. The sample is split in two groups experimental (A) and control (B). For example, group

1 does ‘A’ then ‘B’, group 2 does ‘B’ then ‘A’ this is to eliminate order effects – the participants are

counter balanced.

Matched Pairs Pairs of participants are matched from participants in terms of key variables, such as age and IQ. One

member of each pair is then placed in the Experimental group and the other member in the Control

group.

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Strengths Weaknesses

Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.

More participants are required as different participants take part in the different conditions of the experiment, making the design more costly and time consuming. Furthermore, it is very time-consuming trying to find closely matched pairs.

Avoids order effects (such as practice or fatigue) as participants only take part in one condition of the experiment and therefore they are less likely to become bored and give up. As a result their performance should NOT be affected in an independent groups design.

Impossible to match people exactly, unless using identical twins!

Independent Repeated Matched

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Avoids order effects Reduces demand

characteristics

Fewer participants required (saves time / money)

Reduces participant variables

Reduces participants variables Avoids order effects

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Increased chance of order effects

Increased chance of demand

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More participants requires Impossible to match people

exactly

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9. Sampling Techniques Psychologists use sampling techniques to choose people to represent the target population. If your

sample is representative then you can generalise the results of your target population.

Random For random sampling every member of the target population has an

equal chance of being selected. This involves identifying everyone in

the target population and then selecting the number of participants

you need in a way that gives everyone an equal chance of being

selected (i.e. pulling names from a hat).

Opportunity Opportunity sampling consists of selecting anyone who is available & willing to take part in the study.

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Volunteer Volunteer sampling consists of participants self-selecting to become part of a study because they

volunteer when asked, or in response to an advert.

TYPE OF SAMPLE

DEFINITION METHOD TO OBTAIN

SAMPLE ISSUES (WEAKNESSES)

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Every member of target population has an equal chance of being selected.

Every member of population is identified and a random sampling technique is used to select the sample e.g. names are drawn from a hat.

High Population Validity: The sample is likely to be representative, so the results can be generalised to the target population. However, it is difficult and time consuming to get full details of the target population. Also, people that are selected may be unwilling to take part.

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Participants self-select themselves. They offer to take part in the research.

A researcher advertises his/her study and participants respond to the advert.

Low Population Validity: A particular type of person is likely to take part in research (helpful and willing), thus the sample is likely to be biased and therefore we can’t generalise the results to the target population.

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Selecting participants who are available to the researcher and willing to take part.

Researcher approaches

whoever is available and

asks them to take part in

their study.

Low Population Validity: High chance that sample will be biased. E.g. Opportunity samples often use available university students, which are not representative of the target population.

Mr J. Robinson

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10. Observations (including behavioural categories) When conducting observations researchers have the choice between four different types, including:

Type of Observation Description

Covert Observation Also known as ‘undisclosed’ observations. Observing people without their knowledge. For example, using a one-way mirror.

Overt Observation An observational technique where the observations are ‘open’. i.e. The participant know/are aware that they are being observed.

Participant Observations made by someone who is also participating in the activity being observed. This may affect their objectivity.

Non-Participant Observations made by someone who is not participating in the activity being observed.

Naturalistic Observation

An observation carried out in an unaltered setting in which the observer does not interfere in any way, but merely observes the behaviour in question.

Controlled Observation

An observation conducted under controlled conditions, such as an observation room.

Structured The researcher uses various ‘systems’ to organise behaviour, such as sampling techniques or behavioural categories.

Unstructured Observation

Every instance of behaviour is recorded in a much detail as possible. This is useful if the behaviour you are interested in do not occur very often.

When conducting observations psychologists have to decide what specific behaviours should be

categorised. They need to operationalize the behaviour, e.g. How can we measure aggression:

Physical…Hair Pulling…Time/Quantity…Verbal?

Behavioural Categories (See Point 20) need to be as objective as possible. Therefore, two people using

the same behaviour categories should record the same behaviour, this is known as inter-observer

reliability – the extent to which observers agree in their rating of an observation.

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Strengths of Observations Weaknesses of Observations

Value as a preliminary research tool:

observations can lead to the identification of

areas for further investigation or prevent wasting

time by carrying out unrealistic experiments. For

example, once Ainsworth identified different

types of attachment other psychologists could

then conduct further research examining these

types of attachment.

Validity: Naturalistic observations can provide a

useful way of checking whether experimental

findings apply outside the laboratory.

Non-participants and covert observations often

have high levels of ecological validity because

they reduce demand characteristics.

Observer Effects: Overt and participant

observations may cause observer effects where

the participants behave different because they

are being watched – this can increase demand

characteristics and reduce validity.

Ethics (Privacy, Confidentiality, Protection from

Harm) – Studies based on observations must

respect privacy and psychological wellbeing.

Unless participants have given their consent to

being observed, then observation research is only

acceptable in situations where those being

observed would expect to be observed by

strangers i.e. in a high street or shopping centre.

11. Questionnaires (including question wording) Questionnaires are a type of ‘self-report’ technique, where participants provide information relating to

themselves, including: thoughts, feelings and behaviours.

Open Questions - Allows the participant to answer however he/she wishes and generate qualitative data.

Closed Questions – Restrict the participants to a predetermined set of responses and generates

quantitative data. There are three types of closed questions, including:

1. Checklist - A type of question where participants tick those which apply. For example: What is

the highest academic qualification you hold?

GCSEs

A – Levels

Batchelor Degree

Post-graduate Degree

2. Likert Response Scale - A type of question where participants rate, on a scale, their

views/opinions to a question. For example: Psychology is the most interesting A-Level subject.

(Circle the number that applies).

1 2 3 4 5

Strongly Agree Agree Not sure Disagree Strongly Disagree

3. Ranking Scale - A type of question where participants place a list of items, in their preferred

order. For example: Rank the following activities according to how much time you spend on

them each day (1 = most time, 4 = least time)

Talking face to face

Talking on the telephone

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Text messaging

Other (e.g. MSN, IRC chat)

Questionnaire Design – The following 9 points are considerations Psychologists must make when

designing questionnaires:

1. Keep it simple & clear (don’t use complicated words)

2. Keep it short

3. Be sensitive, avoid personal questions

4. If you must, collect personal information at the end

5. Avoid hard ‘memory questions’

6. Pilot and modify the questionnaire

7. Do not use multiple questions

8. Do not use leading questions

9. Don’t use questions that make assumptions

Strengths Weaknesses

If quantitative (numerical) data is collected, it is easy to analyse and look for patterns and trends in the data that can lead to further research being conducted.

Less chance of research bias (especially if

the questionnaire is anonymous) as participants can answer the questions on their own, without pressure from the researcher.

If the wording is unclear participants may not understand the questions and therefore answer incorrectly, making the data less valid.

When answering questionnaires participants may include socially desirable answers, where they try and portray themselves in the best possible way, making the results less valid.

12. Interviews (including question wording)

Structured Interviews In this interview questions are decided in advance and asked in the same order for each interviewee;

Often the interviewee responds to each question from a fixed set of response. E.g., Yes / No and

therefore this type of interview typically produces quantitative data.

Semi-Structured Interview This interview contains mostly prepared questions which can be supplemented with additional

questions.

Like unstructured interviews (see below), the interviewee responds in their own words (not from a

fixed set) and therefore this type of interview typically produces rich qualitative data.

Unstructured Interview This interview is more like a conversation and the interviewer only facilitates the discussion.

Very little is decided in advance (only the topic and 1/2 questions) and therefore this type of interview

typically produces rich qualitative data.

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Strengths Weaknesses

Rich and interesting qualitative data can be obtained (especially if using semi-structured or unstructured interviews). This allows the research to clarify the meaning and gain further information if required.

Unstructured interviews may encourage the participant to be honest in their answers (reducing social desirability) because participants are able to justify their answers.

Difficult to analyse - Statistical analysis can be difficult if the interview is unstructured and the data collected is qualitative making it more difficult to identify patterns and trends.

Time consuming & expensive – Interviews are more time consuming and costly in comparison to questionnaires, as interviews required trained psychologists to administer them.

13. Reliability The term reliability means consistency. Reliability means that two or more measurements, or

observations of the same psychological event, will be consistent with each other.

How is reliability tested? Test-retest reliability – we can assess reliability using the test-retest method

which is used to determine the reliability of a test by administering it two (or more) times. If similar results

are found then the test is seen as reliable, however if different results are obtained then the test is seen

as unreliable.

14. Validity

How to test validity?

Face validity is the simplest way of assessing validity. Here different researchers (experts) assess

whether the test being carried out is appropriate and may suggest improvements. If all researchers agree

then the test has high face validity.

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Concurrent validity involves comparing the results of a new test with those from an older test, known

to have good validity. For example, if a participant achieved an IQ score of 148 on an older IQ test, but

then scored 113 on a new test, questions may be raised over the validity of the new results.

Predictive validity is the ability of the test to predict the performance on future test. If it can do this then

the test is said to have good predictive validity. For example, if GCSE scores are a good predictor of A-

level results, then GCSEs would be said to have good predictive validity.

15. BPS (British Psychological Society) Code of Ethics

Deception

Right to Withdraw

Informed Consent

Privacy

Protection from Harm

Definition Why is it unethical? Dealing with the issue…

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Information should not be withheld form participant and they should not be misled (lied to).

It prevents participants from giving informed consent and the participant might be taking part in research which goes against their views or beliefs (lacks respect) Participants may become distrustful towards psychologists in the future.

At the end of the experiment the participants should be debriefed and told the true aim of the research. At this point the participant should be given the ‘right to withdraw’ until the publication of the results and the contact details of the experimenter, should he/she have any further questions or queries.

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Participants have the right to withdraw (remove themselves from the experiment) at any stage of the experiment. This includes the later stages, after the research has been conducted, in which case the research has to destroy any data or information collected.

Participants who are not given the right to withdraw may feel unnecessary or undue stress (i.e. Milgram) and are therefore not protected from harm. Participants may become distrustful towards psychologists in the future.

At the end of the experiment the participants should be debriefed and told the true aim of the research. At this point the participant should be given the ‘right to withdraw’ until the publication of the results and the contact details of the experimenter, should he/she have any further questions or queries.

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When someone consents to participate in research, their consent must be informed. The aims of the experiment should be made clear.

Lack of informed consent may mean that the participant is taking part in research which goes against his/her wishes or beliefs (lacks respect). Participants may become distrustful towards psychologists in the future.

Presumptive consent – this involves taking a random sample of the population and introducing them to the research, including any deception. If they agree to take part in the research and we can generalise this consent to all participants. Children as participants – this involves gaining the consent of the parent(s) for children under the age of 16 to participate in research.

Pri

vacy

Privacy is the right of individuals to decide how information about them is communicated to others.

A skilled researcher may obtain more information from a participant than he/she wishes to give and this would be seen as an invasion of privacy and the participant may feel ashamed or embarrassed. Participants may become distrustful towards psychologists in the future.

The participant should be provided with fully informed consent and the right to withdraw at any stage. Furthermore, the researcher should explain to participants the ways in which their information will be protected and kept confidential, i.e. no names will be published in the final report and any written information or video information will be destroyed.

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16. Pilot Studies A small scale prototype of an investigation to find out if there are any problems with the following:

Experimental design;

Instructions for participants;

Measuring instruments, including the behavioural categories in observational research.

A pilot study should be designed to test the reliability of the data collection tool and the researcher

should make any necessary changes before carrying out their full investigation to save time and money.

17. Quantitative Data (Bar Chart, Histogram, Scattergram) Bar Chart - Used when data is in categories or you want to display mean scores from different groups;

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Psychologists have the responsibility to protect their participants from physical or mental harm, including stress. The risk of harm must be no greater than that which they are exposed to in everyday life.

Participants should leave the research the same as when they entered it. If they are harmed they may suffer from long-term effect which could impact their future lives (i.e. Milgram).

The research should remind participants of their right to withdraw at any point during the research. The research should terminate the experiment if the level of psychological or physical harm is higher than expected. Participants should be debriefed at the end of the experiment.

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Histogram - Consists of vertical bars of equal width but varying height to represent the frequency of

each score. Unlike bar charts, it’s used to present continuous data, like salary, test scores, time, and

age.

Scattergram – See Point 19.

18. Measures of Central Tendency (Mean, Mode, Median) & Measures of

Dispersion (Range, SD)

Measures of

Central Tendency Definition: How is it calculated?

Mean ‘Average’ – Calculated by adding up all the scores and then dividing by the

number of scores.

Median ‘Middle score’ – calculated by putting all scores in order then picking the

middle score.

Mode The most frequently occurring score.

Question: If your data set contains outliers (extreme scores) what measure(s) of central tendency

should you use? Why? Median or mode as the outliers will not distort them. Outliers (extreme scores)

can distort the mean.

Measures of Dispersion describe the spread of data around a central value (mean). They tells us how

much variability there is in the data;

You need to know two measures of dispersion:

Range (subtract the lowest score from the highest

scores)

Standard Deviation (SD)

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19. Correlation Co-Efficient Correlation refers to the relationship between two or more variables. There are 2 types of correlation:

Positive correlation - As one variable increases the other variable increases.

Negative correlation - As one variable increases the other variable decreases.

Correlation is a non-experiment method used to measure how strong the relationship is between the

two (or more) variables. Psychologists use a statistic called a correlation coefficient to measure this

strength. A correlation coefficient can range between -1.0 and +1.0.

The number represents the strength of the relationship (extent to which the two variables are related).

The nearer the number is to +1 or -1 the stronger the correlation.

A scattergraph is a graph that shows the correlation between two sets of data (or co-variables) by plotting dots to represent each pair of scores. It indicates the degree of correlation between the co-variables.

Strengths Weaknesses

Correlational analysis can be used when a lab experiment would be unethical as the variables are NOT manipulated. For example – number of hours spent in days care and type of attachment.

It is not possible to establish cause and effect relationships through correlations – that is, we cannot say that one variable caused the other variable to increase/decrease, there could be other factors which influenced the relationship.

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Measures the strength of a relationship between two variables – for example the number of cigarettes smoked and incidence of lung cancer which allows for further research to be conducted.

Correlations only identify linear relationships and not curvilinear. For example, the relationship between temperature and aggression is curvilinear, that is the relationship is positive to a point however at very high temperatures aggression declines.

20. Content Analysis Content Analysis is where we analyse ‘data’ according to themes or categories. This data can be in the

forms of videos, magazines, newspapers, etc.

The FOUR Cs of Content Analysis:

Step 1 – Categorise (Create a list of categories that you are going to measure)

Step 2 – Count (Tally every time the behaviour occurs)

Step 3 – Conclude (Write a conclusion, based on your findings)

Step 4 – Compare (inter-rater reliability)

One of main issues of content analysis is researcher bias. Even after a list of categories has been created

the researcher may interpret the meaning of these categories differently to fit his/her expectations. What

one research categories as a ‘hit’ another may not and therefore it is important for researcher to clearly

define their categories.

Reliability can be assessed using inter-rater reliability (this is where we ‘Compare’ our results). The

degree to which two observers agree in their assessment of behaviour. High inter-rater reliability can be

accepted as valid results.