Predictions that the researcher makes about what they will find.

Statement predicting the direction of a relationship between variables.

Statement predicting only that one variable will be related to the other, not the direction of the relationship.

Statement stating that any difference or correlation in the results is due to chance.

Another name directional hypothesis

Another name for non-directional hypothesis

This study seeks to explore how doodling in class affects students' scores on their final exam.

Students who doodle during class will score higher on the final exam compared to students who do not doodle.

There will be a difference in final exam scores between students who doodle during class and students who do not doodle.

Doodling during class will have no effect on students' final exam scores, showing no difference compared to students who do not doodle. 

Variable that the researcher manipulates to see what effects it has on the dependent variable (e.g. doodle vs. non-doodling)

Variable that the researcher is measuring (e.g. final exam).

The process of defining exactly how you will measure and/or manipulate the variables in a study.

The extent to which the researcher is measuring what they think they are measuring.

A different group of participants is used for each IV level (e.g. one group will doodle and the other one will not). 

 Participants perform at every IV level (e.g. Participants will both doodle and then switch to not doodling).

Participants are arranged in pairs. Each pair is similar in important ways to the study, and one member performs at a different level of the IV (e.g. Two boys with similar IQs are divided into doodling and non-doodling).

There are no order effects as participants take part in only one condition (e.g. fatigue, boredom, or practice effect). 

Less chances of demand characteristics as participants are less likely to guess the aim of the study. 

Participant variables are controlled as the same people do both conditions. 

Fewer participants are needed, which is useful if samples are limited. 

Participant variables are controlled.

There are no problems with order effects.

More participants are needed to gather data.

There is no control for participant variables. For example, participants in one group may be naturally better at the task given.

Order effect can occur. Chances of demand characteristics are increased.

It is questionable whether all relevant variables can be matched. 

It can be difficult (and time-consuming) to find and match participants.

Random Allocation: Participants are randomly distributed to each level of the IV to reduce the effect of individual differences (which is a confounding variable) affecting the study’s results. This can be done by tossing a coin or allowing a computer to allocate groups.

Counterbalance: Each condition is tested first in equal amounts. So half the participants first experience condition A and then condition B, while the other half of participants first experience condition B and then condition A.

If possible, recruit a large sample in order to create the matched pairs, one of which will be allocated to each group.

Ensure that any changes to the dependent variable are due to the manipulation of the independent variable.

Individual characteristics that affect how a person behaves in a study and can affect the validity of an experiment if they vary systematically with the independent variable. 

Features of the environment that affect how a person behaves in a study and can affect the validity of an experiment if they vary systematically with the independent variable.

The procedure is consistent/the same for every participant so the study can be replicated and tested for reliability. 

Factors which may affect the study’s validity. A variable other than the independent variable that might skew the results.

Describes human behavior and experience using numbers and statistical analysis. It is objective as it can be measured. 

1. Quantitative Data

2. Qualitative Data

Deals with descriptive, in-depth detail of behavior and experience. It is subjective as it cannot be measured.

A score recorded for each participant, the time taken to complete a task. 

Descriptions of events, quotes from participants, descriptions of responses to a task. 

Scores can be compared. Results can be compared if the study is replicated.

Numbers and statistics are more objective and less prone to research bias.

It is more scientific as statistical tests can be conducted.

It does not always allow us to understand what a participant is thinking or feeling.

Scales may limit how participants can respond, reducing validity. 

It reduces behavior to a single number, failing to find out why a participant has behaved a particular way. 

Data provides an in-depth understanding of the thoughts and feelings of participants. 

Data can be rich in detail and insightful, so not reductionist. 

Researchers can gain an understanding of why people behave in a particular way. 

Analysis is prone to researcher bias as the interpretation is more subjective.

Statistical analysis cannot be made, so it is less scientific. 

Studies are harder to replicate so findings can be less reliable. 

Selecting those available at the time of research.

Each participant is randomly selected from the target population. Every member of the group has an equal chance of being selected (e.g. the names of every member of the group is put in a hat and the required number of names is picked out.

Asking for volunteers to take part in research through advertisements. 

A large sample can be obtained quickly and without much effort.

More likely to be representative.

People are more likely to participate if they have already volunteered so the drop-out rate should be lower, making generalizations potentially stronger. 

A researcher may choose people who look suitable and therefore bias the sample (similar characteristics).

Some of the people picked may not want to take part and will need replacing.

People may not see the advert or make time to reply, or they may just ignore it. Those who do volunteer may be different from those who do not choose to volunteer.

Participants should be asked if they want to take part and be given relevant information about what is involved.

Participants should be made aware they can withdraw from the study at any time during or after data collection.

Participants should be protected from all physical and psychological harm.

Participants should not be deceived about the aims or misled about the study. 

Participants’ data should not be passed to others who are not involved in the research and it should not be published in a way that would reveal their identity. 

Invasive/private questions should be avoided. Participants should be made aware of their right to ignore the questions they incur during the study.

Participants should be told what has happened, asked if they have concerns and given explanations at the end of the study. 

Researchers should minimize harm, discomfort and suffering to the animals and maximize the benefit of the research (e.g. applying findings to help other animals or humans). 

Alternatives to using animals should be considered where possible (e.g. computer simulations or videos of previous studies).

Appropriate species should be chosen (e.g. those least likely to suffer). SOme animals are considered more sentient than others (i.e. have the ability to feel). Non-human primates should be avoided due to the high levels of sentience.

Researchers should use the smallest number of animals possible to meet the research aims. Animals should not be used over a long period of time as this could prolong suffering. 

No procedure should cause physical or psychological harm or distress. For any procedure that might, a special project license is needed. 

Death, disease and psychological or physical discomfort should be avoided. An animal’s environment should be enriched where possible. Potential harm and the benefits being gained should be balanced. 

The social and natural behavior of the species should be considered. Animals who would normally live in social groups should not be isolated. Overcrowding should also be avoided to prevent stress and aggression. 

Normal feeding patterns should be adhered to and deprivation or aversive stimulation should be avoided, or kept to the minimum needed to achieve the goals of the study. 

Whether the study accurately shows that the independent variable caused changes in the dependent variable.

Participants may want to present themselves in the best way possible, so answers may not represent true thoughts and feelings. 

The researcher’s interpretation of behavior may be biased by personal thoughts, feelings and opinions. 

Features of the environment which give away the aim of the study. 

A lack of control over extraneous variables may lead to other factors affecting the dependent variable. 

Whether the study's findings can be generalized to other people, settings, and times.

The extent to which a study’s findings can be meaningfully applied to the target population.

The extent to which the participants’ behavior reflects how they would behave in their everyday life. 

The extent to which a study’s findings can be applied to other time periods. 

Whether the findings of a study are consistentent due to standardized procedures and control variables. Ways to measure reliability:

The extent to which two researchers agree in their scoring of a questionnaire or test. 

The extent to which two researchers agree in their recording of behaviors in an observation. 

Participants repeat a test or questionnaire after a time period to see if they gain similar scores. 

Whether a study can be repeated in exactly the same way again through standardized instructions and procedures.

A single value that describes the whole of a data set.

How spread out or close together the numbers in a group are

Which of the following is a measure of central tendency?

Which of the following is a measure of spread?

Calculating by adding all of the scores together and dividing by the number of scores.

The ‘middle’ value when a list of numbers (scores) is put in order from smallest to greatest.

The most frequently occurring score.

The difference between the highest and lowest scores in a set.

Tells us how data is spread around the mean.

Used to show categorical data. Bars do not touch.

Used to show continuous data. Bars touch. 

Used to show a relationship between co-variables.