What is psychology?

The scientific study of human thought and behaviour. Investigations need to be scientific; psychologists want evidence to support their ideas and theories. They want their ideas/theories to be:

• Tested and agreed with by other people.
• Applicable to lots of different sorts of people all over the world.
• Useful and beneficial to others.

Psychologists use a range of ways to carry out scientific investigations these are known as RESEARCH METHODS. Because we are often investigating things we cannot physically see (memories, dreams, fears, plans, love), psychologists have to be very careful when planning their investigations.

One of the RESEARCH METHODS in psychology are experiments.

Experiments have an Independent Variable: A variable that you can manipulate and change e.g. giving a group of people chocolate and another no chocolate, and a Dependent Variable: A variable that you measure e.g. asking people to rate their happiness on a scale of 1-10. But, before we go on…

THE MOST IMPORTANT WORDS IN PSYCHOLOGY! LEARN THESE BEFORE YOU GO ANY FURTHER:

RELIABILITY: This refers to how consistent a study/procedure or measuring device is. A measurement is said to be reliable or consistent if it can produce similar results if used again in similar circumstances. As a psychologist you have to ask yourself ‘Did every participant experience my study in the same way?’ If they can say yes to that question, they have high reliability.

VALIDITY: As a psychologist you have to ask yourself ‘Is my study testing what I want it to test?’ Having high validity can also be thought of as collecting data that truly reflects how a participant would behave. A psychologist must ensure that their participants aren’t effected by anything else, other than what they are testing. They may ask ‘Are my participants lying? Are they changing their behaviour due to my presence?’ If the answer is no to these questions, they have high validity.

Types of Validity:

External Validity	Whether the findings will generalise to other populations, locations, contexts and times and still hold true.
External Validity: Ecological validity	Refers to the extent to which the findings of a research study are able to be generalised to real-life settings.
External Validity: Population Validity	How representative the sample used is to other populations.
External Validity: Historical/Temporal Validity	Will the findings still be valid as society changes over the years e.g. will a study conducted about female behaviour in 1965, generalise to females today?
Internal Validity	Within your measure, the IV is the only variable effecting the DV.
Internal Validity: Face Validity	The degree to which a procedure, especially a psychological test or assessment, appears effective in terms of its stated aims.
Internal Validity: Concurrent Validity	Whether a measure produces similar results for a participant as another test that claims to measure the same thing e.g. a participant completes a brand-new test for autism and gained very similar results in a previously well-established test of autism.

Experiments:

Experiments are the only research method that manipulates variables. We manipulate one variable (IV) and see if it could have an effect on another variable (DV)

EXAMPLE: I want to see if the amount of sleep has an effect on reaction times. I manipulate the amount of sleep by giving one group of participants 8 hours sleep and another group 4 hours sleep (IV). I then measure reaction times by timing how long it takes them to press a button when a picture of a dog appears on a screen (DV)

Experiments are carried in highly controlled conditions in order to establish cause and effect between the IV and the DV. Experiments:

• Can manipulate one variable and keep everything else the same (control)
• Allocate participants into groups (we will cover this in more depth later on)
• If the groups think or behave differently then you know it’s the thing you have manipulated (IV) that caused the different result (DV), as everything else was kept the same.

Throughout the research methods content we will look at four research methods:

1. Experiment
2. Self-report.
3. Correlation
4. Observation

These research methods are often used alongside experiments.See the examples below. You will learn about the research methods above in more depth later on in the course.

Experiment using a Self-Report:

Experiments can be used alongside other research methods; . For example a self-report can be used as your dependent variable. A self-report is when you ask people how they think and behave. This can be done through interviews questionnaires. Psychologists then make conclusions about how people think and behave from their answers.

I want to know whether the temperature of a room has an effect on people’s ability to concentrate.

For example: Ask people to rate from 1-10 how hard they find it to concentrate (using a questionnaire as the dependent variable) in a warm or a cold room (this would the independent variable, as it is the thing that changes).

Experiment using an Observation

Experiments can also use an observation as the dependent variable. An observation is where you watch how people behave. Then psychologists make conclusions based only on what you can observe

I want to know whether the temperature of a room affects people’s ability to concentrate.

For example: Go to a library and observe how many books they read/how distracted they get/how long they stay for, when the temperature is cold compared to warm

Ethics

Psychologist John Money suggested that baby David who had a botched circumcision should be raised as a girl. He wanted to see if gender was biological (nature) or learned (nurture), as David had a twin brother called Brian who would be raised as a boy. Money may have lied regarding many of his results suggesting that nurture caused gender. David, later reported feeling unhappy as a girl (Brenda) and shown biology determines gender, when he became David. Both David and Brian eventually committed suicide.

Sampling

Before we go into experiments, you need a group of participants to carry out these experiments on! In your exams, you will need to understand the various methods that psychologists use to collect their participants. Firstly you need to learn about the key terms associated with participant sampling and then build an understanding of the different methods use to gather participants.

Target Population: The set of people researchers want to find out about.

Sample: A small set of people taken from the target population.

Representative: How well a sample reflects the target population.

Target Population	Sample
Nurses	42 nurses from Daventry 39 female 3 male
The Elderly	481 care home residents 481 male
People	20 psychology A-level learners 17 female 3 male

Sampling Bias

Samples can be biased; they do not reflect the target population and this affects the conclusions we can draw from these samples. Some sample bias terms are outline below:

Androcentric: a sample that contains a large proportion of males.

Gynocentric: a sample that contains a large proportion of females.

Cultural bias sample: a sample that is too focused on one culture, isn’t representative of all cultures.

Ethnocentric: Ethnocentrism is the tendency to judge or interpret other cultures according to the values and standards of one’s own culture. This can lead researchers to assume that findings from one culture can be applied universally, even when cultural differences may affect behaviour.

Population validity: Being able to generalise results from our sample to the target population and still hold true.

Therefore, we need to be careful in selecting participants for our study. There are four different sampling techniques we use to do this.

Sampling Methods

Opportunity Sampling:

Anyone who is available at the time of your research e.g. I want to research into the eating habits of men. I walk around the college and survey the first 20 males I find

Strengths:

1. Quick and easy to carry out. This is because it relies on people who are around at the time and is therefore more time efficient than other sampling methods.
2. Can help to collect participants with similar characteristics as people who share characteristics tend to segregate in the same areas. This is a strength because it can help to generalise (apply) findings to a target population.

Weaknesses:

1. May not always be representative, as the kinds of people available are likely to be limited, and therefore similar, so although they may apply to the target population, it may be difficult to generalise to the wider population.
2. Increased chance of researcher bias as they may only approach people who they feel will give them the results they want.

Volunteer/Self-selected Sampling:

Participants choose themselves to take part in the study. They could be recruited through; using online email surveys, signing up or applying to take part, or responding to adverts or posters.

Strengths:

1. This method is relatively easy as the participants come to you. This is a strength because they are likely to remain committed to the study and less likely to drop out, preventing the chance of a small, unrepresentative sample.
2. Can reach a wider variety of participants through emails, posters, advertisements compared to opportunity sample, which will only cover a small area.

Weaknesses:

1. This method may have an issue with sample bias. This is because certain individuals tend to volunteer for studies and therefore may not be representative of all people. For example if I wanted to investigate the effect of exercise on mood, people who are good at exercise are likely to come forward. Therefore, the results would not be valid when generalising to people who do not exercise.
2. Sometimes there will not be enough interest in your studies advert which can lead to a small sample. This is a weakness as it could lead to an unrepresentative sample.

Random sampling:

Every member of the population has a fair and equal chance of taking part. e.g. Everybody puts their name into a hat, draw first 25 names out of the hat for the sample.

Strengths:

1. Random sampling can produce a more representative sample because every member of the target population has an equal chance of being selected. This reduces selection bias and increases the likelihood that a range of different characteristics will be included in the sample, making the findings more generalisable to the target population.
2. Random sampling can reduce researcher bias because the researcher does not choose which individuals are selected to participate. This makes the sample less likely to be influenced by the researcher’s conscious or unconscious preferences, which can increase the validity of the findings.

Weaknesses:

1. Random sampling can be time-consuming and difficult to implement because researchers need access to a complete sampling frame of the target population. In practice, this information may be unavailable or incomplete, making it difficult to ensure that every member has an equal chance of being selected.
2. Random sampling does not guarantee a representative sample. By chance, certain groups may be overrepresented or underrepresented. For example, a random sample could contain disproportionately more females than males, reducing the extent to which the sample represents the target population and limiting generalisability.
3. Random sampling can be impractical when the target population is very large, as every member must be identified and assigned a number before selection can occur. As a result, researchers may use more practical sampling techniques, such as opportunity sampling, even though these may introduce greater bias.

Stratified Sampling:

Here the sampler divides or ‘stratifies’ the target group into sections, each showing a key characteristic which should be present in the final sample. Then each of those sections is sampled individually. All types of members of the population are represented by deliberately selecting participants from all strata e.g. elderly, middle-age, young adults, teenagers, children. The sample thus created should contain members from each key characteristic in a proportion representative of the target population. For example, if there were strata’s of the class system – upper, middle and lower class, there would need to be a much larger sample for the lower class sample compared to the upper class, as there are a lot more people in the lower class population compared to upper class.

Strengths:

1. Stratified sampling is likely to produce a more representative sample because key subgroups are represented in the same proportions as they are in the target population. This increases the extent to which the findings can be generalised to the target population.
2. Stratified sampling can reduce sampling bias because participants are selected from each subgroup of the population rather than relying on researcher judgement or convenience. If random selection is used within each stratum, every member of that subgroup has an equal chance of being chosen.

Weakness:

1. Stratified sampling can be difficult to carry out because researchers must identify all relevant subgroups within the target population and determine their proportions accurately. This information may not always be available.
2. Stratified sampling is more time-consuming than many other sampling techniques because researchers must first identify the strata, calculate the correct proportions, and then select participants from each subgroup.

Systematic sampling:

A systematic method is chosen for selecting from a target group, e.g. every fourth person in a list could be used in the sample. It differs from random sampling in that it does not give an equal chance of selection to each individual in the target group.

Strengths:

• It can provide an unbiased sample as the researcher has no part in deciding who is selected, therefore reduces the chance of researcher bias, increasing validity.
• Higher chance of gaining a representative sample as it is likely to gain a varied sample from the systematic formula. It would be quite unlucky to only pick females for example.

Weaknesses:

• If the list has been assembled in any other way it could unintentionally create a biased sample. For example if every fourth person in the list was male, you would have only males in your sample.

Experimental Designs:

Now that we understand how to be highly ethical, operationalise the IV and the DV, and how to collect a sample, you then need to decide how to group your participants.

Independent measures design:

This is when different participants participate in each condition (each level of the IV). For example, having Dave, Steve and Julie complete a condition where they eat chocolate and then rate their happiness on a scale of 1-10, then a different group of people; Harry, Jerry and Phil complete a condition where they have no chocolate, and then rate their happiness on a scale of 1-10.

Strengths:

1. Different participants are used in each condition, so there are no order effects, such as practice, fatigue or boredom effects. For example, if participants completed a basketball shooting task both with and without an energy drink, any improvement on the second attempt might be due to practice rather than the energy drink itself. Independent groups designs avoid this problem because each participant only completes one condition.
2. Participants only experience one condition, making it more difficult for them to identify the purpose of the study by comparing different conditions. This may reduce demand characteristics and increase the validity of the findings. Demand characteristics can be further reduced by carrying out a single blind or a double blind procedure.

Single-blind procedure: Participants are unaware of the aims of the study or which treatment/condition they are receiving. This reduces demand characteristics because participants are less likely to alter their behaviour based on expectations.

Double blind test – This is when neither the researcher or the participants are aware of which condition an individual is in. This ensures that demand characteristics are reduced from the participants, but also researcher bias is also reduced. Researcher bias is when the researcher intentionally or unintentionally influences the behaviour of the participants in order to get the participants to fit in with the results that they want. If the researcher is unaware of what condition it is, they would find it more difficult to influence.

Weaknesses:

1. Individual differences could act as an extraneous variable (an extra variable that you do not want to have an effect on your DV. This is because you only want to measure the effect of the IV in order to establish cause and effect) and make it look like the IV effected the DV, but in fact it was something about the individual. For example, if everyone in the condition of 5 people present were on the basket ball team, and the group with 1 person present had participants that didn’t play sport, it could look like crowds of people improve sport performance, but in reality, it was due to the participants previous shooting hoop skills.
2. Independent measures designs require more participants because a different group is needed for each condition. This can make the study more time-consuming and expensive to conduct, as recruiting a large number of suitable participants may be difficult.

What are extraneous variables?

These are variables that COULD potentially effect the DV when we don’t want them to. We as researchers only want the IV that we have created to effect the DV. There are different types of extraneous variables:

Situational variables: are things that could effect participants behaviour in the environment, such as lighting, sound, temperature.

Design variables: are things like order effects, fatigue effects i.e. tiredness by carrying out a repeated measures design. For a independent measures design, you have the issue of comparing two different groups of people, so there will be participant variables.

Participant variables/Individual differences: are things like mood, intelligence. Things about the participant that may effect their behaviour in the study.

Repeated measures design:

When the same participants, participate in each condition.

Strengths of Repeated measures design:

• Individual differences are less likely to distort the effect of the IV on the DV because the same participants take part in all conditions of the experiment. For example, if a researcher wanted to investigate whether classical music or rock music affects maths performance, differences in natural maths ability would not affect the comparison because each participant completes both conditions. This means any difference in scores is more likely to be due to the type of music rather than differences between participants.
• A repeated measures design is vulnerable to order effects, such as practice effects and fatigue effects. However, researchers can use counterbalancing, where different participants complete the conditions in different orders. This helps to balance out order effects across the conditions, increasing the validity of the findings.

What is counterbalancing??

Counterbalancing works by splitting a condition of participants in half and making them experience all possible orders of the study.

For example:

IV – Distance of a person from the participant. E.g. sat up close next to the participant, and sat at a distance opposite the participant.

DV – How fast the participant can complete the Stroop test? If any errors are made, they must be corrected before moving on.

Stroop test: when you have to say the colour of the word, rather than read the word.

Lets say I had 8 participants. To counterbalance I would need to split into 2 groups – we call this the ABBA effect i.e. AB is 4 participants and BA is the other four participants.

Counterbalancing would look like this, in the example above:

AB – 4 participants:

You will be completing the test in this order:

Firstly: complete Stroop test with someone close to you

Secondly: complete Stroop test with someone opposite you.

BA – 4 participants:

You will be completing the test in this order:

Firstly: complete Stroop test with someone opposite you.

Secondly: complete Stroop test with someone close to you

How does it resolve order effects???

The participants who did the conditions in the close then distant order have experienced the order effects of that specific order. The participants who did the conditions in the distant then close order have experienced the order effects of that specific order. Counterbalancing works when both groups have been effected by the order effects equally as much as each other.
This means that the order effects are cancelled out! But it does mean that they have both equally experienced the advantage of doing the stoop test twice. This means that the results are equally effected and we can more confidently say that it was the IV (distance from participant) caused a change, rather than extraneous variables (having practice of the Stroop test by doing it twice)

Weaknesses of a Repeated measures design:

1. Order effects may occur because participants complete more than one condition. This can lead to practice effects, where performance improves due to experience of the task, or fatigue effects, where performance worsens because participants become tired, bored or lose concentration. As a result, changes in the dependent variable may be due to the order of conditions rather than the independent variable, reducing the internal validity of the study.
2. Participants experience the experimental task more than once, making them more likely to work out the aim of the study. This increases the likelihood of demand characteristics, where participants change their behaviour to fit what they believe the researcher expects. This reduces the validity of the findings.

Matched pairs design:

This is when different participants participate in each condition. However, each participant in one group is matched on a certain characteristic to another participant in the other group. The matching is done on relevant variables. For example: an experiment investigating the effect of sleep on Maths ability. Participants maybe matched on their Maths GCSE grade to ensure that it doesn’t act as a participant extraneous variable.

Strengths of a Matched pairs design:

• No order effects. Different participants are used in each condition, so there are no order effects such as practice effects or fatigue effects. This means that performance in one condition cannot influence performance in another condition, increasing the internal validity of the study.
• Reduced demand characteristics. Participants only experience one condition, making it less likely that they will identify the true aim of the study through comparison of conditions. This can reduce demand characteristics and increase the validity of the findings. However: This is generally considered a strength of both matched pairs and independent measures designs rather than a unique strength of matched pairs.
• Controls participant variables. The effects of individual differences are largely controlled because participants are matched on characteristics that are relevant to the study. This reduces the influence of participant variables and means differences in the DV are more likely to be due to the IV.

Weaknesses of Matched pairs design:

1. Matching may not be completely accurate – The similarity between matched participants may be limited because researchers can only match participants on known and measurable characteristics. Other important variables may remain uncontrolled. For example, two participants may be matched on GCSE Maths grades, but one may use maths regularly in their job whereas the other may not, which could still affect performance.
2. Time-consuming and difficult: Finding participants with similar characteristics can be difficult and time-consuming, especially when several matching variables are required. This can make the research more expensive and less practical to conduct.
3. Participant variables can still affect results. Even after matching, participants are not identical. Unmatched characteristics such as motivation, intelligence, personality, mood, or experience may still influence performance and therefore confound the results.

Experiments:

This is a research method that always uses an Independent Variable (a variable that you change) and a Dependent Variable (a variable that you measure). For example if I were to study whether having chocolate EFFECTS happiness. My IV would be giving one group a bar of chocolate and then another group no chocolate. My DV would be a rating scale of 1-10, 1 being very unhappy 10 being very happy. You should always use the word EFFECT when talking about experiments.

Different types of experiment:

A laboratory experiment:

This is a study that is carried out in unnatural settings with a highly controlled environment. This means that researchers can control for things such as the extraneous variables mentioned earlier. Lab experiments are also highly standardised. This means that participants experience the same instructions, environment, test, researcher etc. This links in to reliability, being highly standardised will mean that you measure is consistent. By having high control and high standardisation, researchers can be more confident that the IV EFFECTED their DV. It also means that the study is easy to replicate, as the instructions on what happened within the study is very clear and standardised.

Strengths:

1. There is high control of extraneous variables – therefore increasing confidence that the IV EFFECTED the DV. This is known as Cause and Effect.
2. Standardised procedures and instructions are used which enables researchers to repeat the study in the exact same way with other participants. If the results are the same with different people. We can be more confident that the IV effected the DV.
3. In a repeated measures design, counterbalancing can be used to reduce design extraneous variables such as order effects.

Weaknesses:

1. The unnatural/artificial situation of a lab may mean that participants do not display behaviour that reflects how they would behave in real life – this is known as low ecological validity.
2. Due to having a researcher present, participants may suffer with demand characteristics. This means that they behave in a way that would please the experiment i.e. fit in with what they are trying to find out. This again means that the participants are not behaving in true way.

Field experiments:

In a field experiment, there is still an IV and a DV. but the setting of the experiment is normal in relation to the behaviour observed, for example, carrying out a study on shopping behaviours in a local super market.

Strengths:

1. As participants are in their normal environment/situation, their behaviour is likely to be more valid, as it will reflect their true behaviour.
2. Because normal environments are used, participants are more likely to be unaware that they are being studied, and therefore less effected by demand characteristics.

Weaknesses:

1. Control over extraneous variables is more difficult because the situational extraneous variables are difficult to control. This can make them less reliable and difficult to replicate in a standardised way.
2. The researcher cannot be sure that the IV caused the effect on DV. This is due to the lack of control over the environment.
3. Participants could be unaware that they are being studied as many field experiments are in natural environments such as supermarkets, schools etc. This raises ethical issues.

Quasi experiments:

A quasi-experiment is an experiment in which the independent variable (IV) is not manipulated by the researcher because it is a pre-existing difference between participants. The researcher compares groups that already possess different levels of the IV. Examples include age, gender, disability, mental health conditions, or brain injury.

For example, a psychologist may want to investigate whether age affects concentration. As researchers cannot manipulate a person’s age, participants might be divided into two naturally existing groups, such as younger adults (15-25 years) and older adults (26-50 years), and their concentration scores compared.

Quasi experiments are also useful when studying a variable that would be unethical for a researcher to manipulate. For example, IV: Presence of a brain injury (brain injury vs no brain injury). DV: Score on a memory test. It would be highly unethical to deliberately cause brain injuries in participants in order to manipulate the IV.

Strengths:

1. Quasi-experiments allow researchers to study real-life participant variables that would be impossible or unethical to manipulate, increasing the practical usefulness of the findings.
2. Quasi-experiments are often more ethical than true experiments because researchers do not have to manipulate potentially harmful variables.

Weaknesses:

1. Because participants already differ in many ways besides the IV, extraneous variables are difficult to control. This reduces internal validity and makes it harder to establish a causal relationship between the IV and DV.
2. Some participant variables are uncommon (e.g. rare neurological disorders), making it difficult to obtain large representative samples and reducing generalisability.
3. Quasi-experiments require independent groups because participants cannot be placed into different levels of the IV. This increases the likelihood of participant variables affecting the results.

Natural Experiments:

Natural experiments are conducted in the everyday (i.e. real life) environment of the participants, but here the experimenter has no control over the independent variable as it occurs naturally in real life.

A psychologist noticed that a new leader in Madagascar appeared to reduce poverty levels. They carried out a natural experiment by comparing crime rates (DV) before the leader was elected (higher poverty levels) and after the leader was elected (lower poverty levels). The naturally occurring change in poverty level acts as the IV.

Strengths:

• The IV is a naturally occurring event or situation that reflects real life, meaning findings may have higher ecological validity than laboratory experiments.
• Participants are often unaware they are being studied or cannot alter the naturally occurring IV, reducing the likelihood of demand characteristics affecting behaviour.
• Natural experiments can be used when it would be unethical or impractical for researchers to manipulate the IV themselves, such as investigating the effects of natural disasters, poverty, or brain injury.

Weaknesses:

• Due to the naturally occurring IV, researchers have little control over extraneous variables that may influence the results, reducing validity. Because extraneous variables are difficult to control, it is harder to determine whether changes in the DV were caused by the IV.
• Naturally occurring events cannot always be recreated, making natural experiments difficult to replicate and reducing reliability.

Aims & Hypotheses

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