1.A elements of researching behaviour

Hypotheses and Variables

Hypotheses

Is a single testable statement

Two types

Alternative Hypothesis (H1)

Null Hypothesis (H0)

Predicts an effect

Predicts no effect

Also called research hypotheses

Either one tailed or two tailed

One tailed hypothesis

Predicts an effect in one direction

Two tailed hypothesis

Predicts an effect in either direction

Rejecting hypotheses

Through significance testing

You look at the data, and see if the is actually a difference between data set, if there is a difference, you can reject the null hypothesis which would claim there is no difference, therefore accepting the Alternative hypothesis

Through hypothesis testing

The two hypotheses are compared to each other using the collected data, and based on error rates, one is rejected and the other supported

"an educated guess"

Variables

Any quantity or quality that differs across a population

Quantitative Variables

Any variable that can be easily measured (usually numerically)

E.G Height, Weight, Reaction Time, Speed

Categorical Variables

Variables which could be used to apply a categories onto individuals

E.G Gender, Age, Nationality

Reification

This is where you take an abstract concept, or something that isn't real and make it real through research

should be careful of doing this, largely avoid

E.g "Verbal memory = a variable that can be measured with a list of words" but verbal memory isnt a real or concrete thing

Operationalised Variables

This is where you take a variable that isnt easily measured and give it an "Operational Definition", That is, how you will measure it in this study

E.G > Variable: Depression -------------> Example operational definitions: Depressive symptoms (Quantitative), score on depression scale (Quantitative), receiving therapy or not (Categorical)

Independant and Dependant variables

The independant variable is what is controlled and changed by the researchers

The dependant variable is what changes as a result of the independant variable changing, this is what is measured and studied by researchers

Sampling and Research designs

1 APPROACHES TO RESEARCHING BEHAVIOUR

1.B + 1.C Research Methods

1.D Analysing Data

1.E Evaluating Research

Sampling

Sampling techniques

Non-probability

Convenience sampling

Snowball sampling

Purposive sampling

Probability

Simple random sampling

Stratified sampling

Systematic sampling

Standardisation and Control

Often faces criticism for selection bias

Follows the idea that researchers follow this path

Target population = who the results can be generalised to

Sampling frame = who the researchers have access to

Sample = who are the people selected

Gives every person an equal chance of selection, can only be done through statistically random methods to select, e.g random number generator

Organise a sampling frame and then just choose using a statistically random method

Only really possible when every member of target population is available

Subgroups of the target population are made and then simple random sampling takes place within each subgroup

Provides a more representative sample as efforts are made to make sure that there is a variety of people within the sample

Choosing every Kth person in the sampling frame

Often one of the easiest probability sampling methods to do, and can draw a truly representative sample, however it can do the opposite, if e.g every person selected has same characteristics

Volunteer sampling

This is taking people that are nearby and want to participate, based on the convenience for the researcher

Samples based on who volunteers

Choosing people because they have the desired characteristics, qualities, etc

This is where the first participants (chosen through purposive samples) help recruit more participants for the researcher based on their connections and contacts

Research designs

Independant measures

Repeated measures

Matched pairs

Each participant group is only exposed to one condition of independant variable

This limits many potential biases as they might not be able to figure out the hypothesis and therefore change their behaviour

However it can lead to participant variables if the characteristics of this group are different to that of another group, therefore altering the data

This can be limited through the use of random assignment, where participants are randomly assigned to groups, avoiding potential researcher bias

All participants are exposed to all conditions of the independant variable

This is useful as it completely negates participant variables, e.g gender, age, health, etc, meaning you can see the exact difference between the independant variables as its the same people participating

Can result in order/carryover effects whereby the participation in one condition of the experiment affects participant behaviour in later conditions

e.g practice --> if the participant has gotten good at a task due to performing it earlier

e.g fatigue/boredom --> if the participant is tired and/or bored, meaning they wont do as well

e.g context --> testing during the first condition may affect how participants interpret later conditions

one way to counteract this is through counterbalancing

this is where one group will do the experiment in order AB and the other will do it in BA

this doesnt get rid of the effects, but cancels them out as the experiment was done in different orders, meaning the data average should be the same as if there were no effects

A balance between Independant and Dependant

Participants are matched into pairs based on similar characteristics and them one is assigned to one group whilst the other is assigned to the other group and the rest is carried out as an independant measures study

This is meant to keep the two groups as similar as possible so that participant variables would have no effect/there would be no participant variables

Standardisation

This is where efforts are taken to remove or prevent extraneous variables by making the process the same for every participant in every test

However this can lead to criticism, as it increases the artificiality of the study, meaning it cant be applied to real life

Standardised procedure

This is making variables the same, or standard, for all participants

E.g Standardised.........

Environment

Instructions

materials and tests

Procedure

The instructions that all participants receive are the same, usually accomplished through a script that is read aloud, with answers for any potential questions

Making sure that the location is the same each time, as factors such as time of day, temperature, etc can affect results

Research materials and tests they take should of course be the same for all, except for the variables researchers want to change

The procedure for how the test is run, how researchers interact, etc should be the same for all

Done to limit extraneous variables, which can become confounding variables

A confounding variable is one that may be an explanation for the research results, separate to what you are investigating

Control methods

Blinds

Hiding variables under study from participants, or researchers, or both

Single Blind Method

Double Blind Method

Leaves the participant unaware of whether they are in an experimental group or a control group

Having both participants and researchers unaware of which groups are experimental and which are control groups

Usually done with one researcher changing the independant variable with no knowledge of results, and another researcher measuring results, not knowing which groups are experimental and which are control

Ethical considerations

Informed consent

Physical/psychological harm, stress or discomfort

Right to withdrawal

Confidentiality and Anonymity

Deception

Debriefing

Quantitative research methods

Qualitative research methods

Goal is to classify, count and compare

Focuses on numbers, logic and objectivity

Objectivity means that the researchers bias and perceptions are removed so that any researcher can reach the same conclusions

Most follow these characteristics

Begins with a clear hypothesis that can be objectively measured or answered

Specifically designed to reduce extraneous variables and make sure that the data is replicable

Meant to be representative of a large population

Data collected using structured research instruments which allow for numerical comparisons

Data is usually collected in numerical or statistical form, which can be later presented in graphs and tables

Often considered reliable, as results are almost always replicable

Strengths

Weaknesses

Seeks to isolate variables and therefore establish cause-and-effect relationships

Allows for greater objectivity

Allows for greater accuracy

Large amounts of data can be summarised and compared to other research

Standardisation allows for later replication

Higher reliability and validity

Larger sample sizes allow for high generalisability

Rigid methodology means that insight into more vague concepts like emotion is hard

Often can lead to high artificiality, and therefore low ecological validity

Arguably represents human psychology too superficially

Easily impacted by things like research design or how variables get operationalised

Experiment Types (IV = Independant Variable)

Simple experiment

This is the most basic form of experiment, just two conditions for the independant variable (control and manipulated IV)

Field experiments

Usual manipulation of IV but is conducted in the "field", meaning it is in real life scenarios, allowing for less control over extraneous variables

Quasi-Experiments

No random assignment, the groups are formed based on pre-existing characteristics which act as the IV. E.g how will a group of X age vs Y age perform, or X culture vs Y culture

Natural experiments

Pre-existing IV's which are manipulated naturally, the researchers just observe, and therefore assignment of participants is not possible to experimental groups