Psychology - self reports and correlations

Self-Report:

Asking a participant about their thoughts and behaviour and recording their answers

Self-reports can be used in a variety of different ways: (4 types)

• Questionnaires

• Diary entries

• Interviews

• Psychometric tests

how can self reports be used as a part of an experiment?

as a way of measuring the dependant variable

example of self report:

For example I am interested in finding out if the amount of chocolate that is consumed affects our mood. The amount of chocolate would be manipulated (IV) and I would measure the mood of the participant by using self-report (asking them to rate on a scale of 1-10, 10 = being happy).

However, a self-report can be used with no experimental manipulation (i.e. no IV). For example, interviewing someone about their life.

Qualitative data:

Non-numerical data, rich in detail, usually textual or verbal and provides descriptions.

Quantitative data:

Numerical data, measurements of quantity or amount or how often something has occurred.

Questionnaires

A series of questions in a written form.

Strengths of questionnaires:

1. Easy to administer and can be emailed to participants, making them time and cost efficient.

2. Participants maybe more truthful than in an interview if answers are personal as they are writing them down without immediate judgement of someone.

3. Data easy to analyse if quantitative because patterns and trends can be easily identified due to everyone being asked the same questions (good reliability).

4. Can collect both quantitative and qualitative data through the use of closed questions and open questions.

Weaknesses of questionnaires:

1. Response biases – e.g. tending always to say no, or always ticking the same box for every question. This is more likely if only closed questions are used.

2. If open questions are used, people may provide little detail or may leave them blank because they can’t be bothered.

3. Limited because there is less flexibility. If someone has written an answer that you do not understand, it can lead to the research being misinterpreted

Interview: 

A series of questions are given verbally, face-to-face between an interviewer and an interviewee.

Different types of interview:

1. structured

2. semi-structured

3. unstructured

Structured: 

Predetermined questions, no other questions are asked apart from the ones planned.

Semi-structured: 

Some questions are pre-planned before the interview and then others are created at the time of the interview.

Unstructured: 

A topic of discussion is planned, but no questions are decided in advance, they tend to be open questions, but can be a mixture of open and closed.

Strengths of interviews:

1. Structured interviews are easier to analyse if quantitative.

2. Semi-structured and unstructured interviews enable the researcher to gain detailed info by being able to ask further questions if they need to seek clarity.

3. In face-to-face interviews, the interviewer can respond more flexibly to gain useful, detailed info.

4. Can collect both quantitative and qualitative data through the use of closed questions and open questions.

5. Structured interviews can be easily repeated to increase external and internal reliability.

Weaknesses of interviews:

1. Structured interviews are limited by fixed questions. This may lead to the interviewer with data that they don’t understand as they are unable to ask further questions to seek clarity. This could then reduce validity.

2. Researcher bias can occur. The expectations of the interviewer may alter the way the respondent answers questions.

3. Participants can be affected by biases such as social desirability and leading questions.

4. Only some people are willing to participate in interviews so may not be representative of the population.

Types of questions:

1. open questions

2. closed questions

Open questions

provide qualitative data, as you allow the participant to respond however they want and this can be rich in detail.

Strengths of open questions:

1. They produce qualitative data, giving participants an opportunity to fully express their opinions, thus increasing validity.

2. All info is analysed so information is not lost by averaging answers – increasing validity.

Weaknesses of open questions:

1. Qualitative data is time consuming to analyse as themes need to be identified.

2. Interpretation of qualitative data from open questions can be subjective, leading to bias. This can lead to issues of validity. In addition, the inconsistency of interpreting data can lead to low inter-rater reliability.

3. Open questions may not always collect insightful and detailed data as participants can be reluctant to provide long answers.

Closed questions

only provide quantitative data, as you limit the number of responses the participant gives, so their response is lacking in detail. Yet, you also can count how often someone gives a response providing quantitative data.

types of closed questions:

• Fixed choice

• Checklist

• Ranking

• Likert scale

• Rating scale

Fixed choice question

phrased so that the respondent has to make a fixed choice answer usually ‘yes’ or ‘no’.

example

Do you revise?      Yes/No

Checklist question

give a list of options and told to choose as many as apply to the participant:

example

Which of these methods do you use to revise? (Tick all that apply.)

• Mind maps

• Mnemonics

• Story technique

• Use of imagery

• Flashcards

• Quizzes

• Read notes

Ranking question

whereby participants are instructed to put a list of options into order.

example

Rank the following activities according to how much time you spend on them each day

(1 = most time, 5= least time):

• Revising

• Homework

• Reading

• Texting

• Using social media sites

Likert scale question

whereby participants indicate on a scale how much you agree with a statement. It is also known as a verbal rating scale.

example

“Psychology is the most important subject ever”. Circle one answer.

• Strongly agree

• Agree

• Unsure

• Disagree

• Strongly Disagree

Rating scale question

This type of question asks the the individual a question, and then they must highlight on a numerical scale where they feel best reflects their view

example

On a scale of 1-10 How much do you love Chocolate? (1 being low, 10 being high)

1 2 3 4 5 6 7 8 9 10

Strengths of scale questions:

1. scale questions are less restrictive, as participants have a wider scale of options to choose from, this could improve validity.

2. easy to analyse due to numerical data.

Weaknesses of scale questions:

1. scale questions can be tempting to tick randomly

2. scale questions can be subjective – someones 2 maybe another’s 3 – therefore reliability and consistency may be an issue

Strengths of closed questions:

1. Closed questions are quick and easy for participants to answer.

2. Closed questions are more likely to be structured in a certain order, there fore high in internal reliability.

3. Due to time efficiency, large samples can be collected increasing generalisability.

4. Quantitative data easy to analyse e.g. find median, modes and draw graphs.

Weaknesses of closed questions:

1. Lacks detail, participants can’t express opinions fully, lacks validity.

2. Risk of response bias e.g. saying yes to everything.

3. The score for all participants on each question is only nominal data so only the mode can be calculated. Limited analysis.

Psychometric tests:

A series of standardised closed questions, to measure a mental characteristic such as IQ, emotional intelligence and personality traits.

Strengths of Psychometric tests:

• It is a test that attempts to measure and express numerically the characteristics of behaviour in individuals.  It is therefore usually seen as an objective and scientific way of describing people and their behaviour.

• This technique, of course, provides lots of quantitative data which is easy to analyse statistically.

• Psychometric tests are usually easy to administer.

Weaknesses of Psychometric tests:

• Constructing valid and reliable tests is very difficult.

• Tests usually contain culture bias, especially intelligence tests.

• Most tests will contain designer bias, in the sense that any test is biased in the direction of the author’s view.

• Most tests make the assumption that characteristics to be measured are fixed and unchanging, both in relation to time and also in relation to circumstance or situation. Many studies in psychology, especially social psychology, demonstrate that this is not so.

• There is the danger that the labeling of an individual as possessing a particular trait or ability will tend to encourage conformity to that trait.

Validity of self-reports:

1. internal

2. external

3. population

Internal 

does the PROCEDURE/MEASURE test what is says it does? – same as standard validity.

External

do the RESULTS still hold true when applying findings outside of the study?

Population

can results be generalised to the target population.

If we lack EXTERNAL VALIDITY then…

our RESULTS can’t be generalised to real populations or environments.

Population Validity:

how well the results can apply to the target population or wider population.

Low population validity 

results can’t be generalised to larger populations.

Reliability of self-reports:

1. test retest

2. split half

Test retest:

testing the same individual with the same measures over a period of time. Tests external reliability. If the results are the same over a period of time then the measure has test-retest reliability.

Split half:

splitting the results of a test in two and seeing how consistent they are. Tests internal reliability. If both halves have similar scores then it has split half reliability.

In the exam you may be asked how to make a piece of research more valid or reliable.

Validity can be improved by:

1. Removing leading/unclear/socially desirable/recall questions.

2. Adding open questions with qualitative data.

3. Ensuring answers will be anonymous and confident.

Reliability can be improved by:

1. Training interviewers so they are standardised.

2. Providing standardised questions.

3. Adding closed questions with quantifiable data.

4. Using split test/test-retest methods.

Correlations

measure of how strongly two or more variables are related to each other.

example:

• Height is positively correlated to shoe size

• The taller someone is, the larger their shoe size tends to be.

how is correlations similar to self reports and observations?

there is no manipulation of data, conditions or groups in correlations.

is there IV or DV?

no, just two co-variables

whats this different to?

experiments

why can cause and effect not be established?

As there is no IV to manipulate

why is this the case?

We don’t know which variable is causing the other, we just know there is a relationship between them.

Correlations can be both…

the primary method or secondary technique.

Self reports and observations can both be used as…

a way to gather data on variables, and then see if there is a relationship between them.

eg. Primary method:
Correlations

Secondary technique:
Self report/Observation

For example I want to see if there is a relationship between Facebook friends and happiness therefore I will used a correlational research method. However, I would like to use questions to collect the data for each co-variable of Facebook friends and happiness, therefore my secondary method is self-report.

what can experiments compare?

the data between two groups using correlations.

eg.

I find out men have a stronger correlation between age and time spent looking in the mirror than women.

whats the primary method and secondary technique in this example?

Primary method:
Experiment

Secondary technique:
Correlation

Types of Correlation:

1. positive correlation

2. negative correlation

3. no correlation

Positive Correlation:

as one variable increases, so does the other.

Negative Correlation:

as one variable increases, the other decreases.

No Correlation:

there is no relationship between the variables.

Correlation Coefficient:

a number between -1 and 1 that tells us how strong the relationship is. We will be learning about statistical tests that calculate the correlation coefficient later on in the course.

How is Correlation Coefficient interpreted?:

+1.0 perfect positive correlation
+0.8 strong positive correlation
+0.5 moderate positive correlation
+0.3 weak positive correlation
0 no correlation
-0.3 weak negative correlation
-0.5 moderate negative correlation
-0.8 strong negative correlation
-1.0 perfect negative correlation

How are correlations presented?

Scatter graphs

Scatter graphs:

We can display correlation data in scatter diagrams.
One variable (amount of revision done) along one axis and another variable (final grade) along the other.
Each ‘point’ on the scatter diagram represents one participant: how much revision they put in and what their final grade was.

Hypotheses for correlations:

Null Hypothesis
Alternate hypothesis (one tailed or two tailed).

Instead of the word effect (which is only used for experiments) in a correlation we use the word…. when writing a hypothesis

relationship

Null hypothesis: 

There will be no significant relationship between co-variable 1 and co-variable 2.

Directional Alternate hypothesis:

There will be a significant positive/negative relationship between co-variable 1 and co-variable-2

Non-directional Alternate hypothesis:

there will be a significant relationship between co-variable 1 and co-variable 2.

what do you have to do when writing a hypothesis in exams?

fully operationalise each co variable

eg. A fully operationalised one-tailed hypothesis:

There will be a positive relationship between how many Facebook friends someone claims to have via an open question and how happy they rate them selves on a scale of 1-10, 1 being very unhappy and 10 being very happy.

Strengths of correlations:

1. Makes a good pilot study to generate a hypothesis for an experiment.

2. Can research variables that would be unethical to manipulate.

3. Can understand the relationship between two variables (positive/negative, weak/strong).

4. Only collects quantitative data, so tends to be easy to collect and easy to analyse by plotting data on a scatter graph. From the graph, you can see straight away of there is a relationship.

Weaknesses of correlations:

1. Correlations do not show causation. They CANNOT establish cause and effect!

2. They have the same weakness as whatever method was used to gather the data for the co-variables (observation/self report).

3. An observed correlation between two variables may be due to the common correlation between each of the variables and a third variable rather than any underlying relationship (in a causal sense) of the two variables with each other. In other words, when two variables, a and b, are found to be positively or negatively correlated, it does not necessarily mean that one relates to the other: It may be that changes in an unmeasured or unintended third variable, c, are causing a random and coincidental relationship between the two variables by independently changing a and b. For example, as the sales of air conditioners increase, the number of drownings also increases: The unintended third variable in this case would be the increase in heat.

4. Correlations can be misleading i.e. Bacon causes cancer!!!! does it?

NEVER USE……. when describing a correlation

DIFFERENCE, EFFECT OR CAUSE