Cognitive psychology methods

Conducted in a well-controlled environment – not necessarily a laboratory – and therefore accurate measurements are possible.

Researcher decides where the experiment will take place, at what time, with which participants, in what circumstances and using a standardized procedure. Participants are randomly allocated to each independent variable group. Eg. Milgram's obedience study 

• Strength: It is easier to replicate (i.e. copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength: They allow for precise control of extraneous and independent variables. This allows a cause and effect relationship to be established.
• Limitation: The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e. low ecological validity. This means it would not be possible to generalize the findings to a real life setting.
• Limitation: Demand characteristics or experimenter effects may bias the results and become confounding variables.

Field experiments are done in the everyday (i.e. real life) environment of the participants. The experimenter still manipulates the independent variable, but in a real-life setting (so cannot really control extraneous variables).

Eg. Holfling's hospital study on obedience

• Strength: Behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e. higher ecological validity than a lab experiment.
• Strength: There is less likelihood of demand characteristics affecting the results, as participants may not know they are being studied. This occurs when the study is covert.

• Limitation: There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Conducted in the everyday (i.e. real life) environment of the participants, but here the experimenter has no control over the IV as it occurs naturally in real life.

For example, Hodges and Tizard's attachment research (1989) compared the long term development of children who have been adopted, fostered or returned to their mothers with a control group of children who had spent all their lives in their biological families.

• Strength: Behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e. very high ecological validity.
• Strength: There is less likelihood of demand characteristics affecting the results, as participants may not know they are being studied.
• Strength: Can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g. researching stress.
• Limitation: They may be more expensive and time consuming than lab experiments.
• Limitation: There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

• Independent variable (IV): Variable the experimenter manipulates (i.e. changes) – assumed to have a direct effect on the dependent variable.

• Dependent variable (DV): Variable the experimenter measures, after making changes to the IV that are assumed to affect the DV.

Eg. the use of a high authority figure in a room to see the effects of obedience  levels of the participant- the use of a high authority figure is the IV, and the level of obedience is the DV

Hypothesis= precise, testable statement of what the researchers predict will be the outcome of the study.

• The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). It states results are due to chance and are not significant in terms of supporting the idea being investigated.

• The alternative/experimental hypothesis states that there is a relationship between the two variables being studied (one variable has an effect on the other). It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

In order to write the experimental and null hypotheses for an investigation, you need to identify the key variables in the study. 

A good hypothesis is short and clear should include the operationalized variables being investigated.

A one-tailed directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.

• E.g.: Adults will correctly recall more words than children.

A two-tailed non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.

• E.g.: There will be a difference in how many numbers are correctly recalled by children and adults.

Independent measures: Different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.  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. 

e.g. two groups; group A gets 2 hours of sleep, group B gets 10 hours sleep to see the difference in reaction time.

Pro: Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions they may become bored, tired and fed up by the time they come to the second condition, or becoming wise to the requirements of the experiment!

Con: More people are needed than with the repeated measures design (i.e. more time consuming).

Con: Differences between participants in the groups may affect results, for example; variations in age, sex or social background.  These differences are known as participant variables (i.e. a type of extraneous variable).

Repeated measures: The same participants take part in each condition of the independent variable.  This means that each condition of the experiment includes the same group of participants.

Pro: Fewer people are needed as they take part in all conditions (i.e. saves time) 

Con: There may be order effects. Order effects refer to the order of the conditions having an effect on the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e. practice effect).  Or their performance might be worse in the second condition because they are tired (i.e. fatigue effect).

Matched pairs: One pair must be randomly assigned to the experimental group and the other to the control group. e.g. group A- 2 hours sleep, group B 10 hours sleep matched for age, gender, normal sleeping length 

Pro: Reduces participant (i.e. extraneous) variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics. 

Pro: Avoids order effects, and so counterbalancing is not necessary.

Con: Very time-consuming trying to find closely matched pairs.

Con: Impossible to match people exactly, unless identical twins!

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study.

Operationalization has the great advantage that it generally provides a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability.

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15 minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room’.

Extraneous variables – These are all variables, which are not the independent variable, but could affect the results (e.g. dependent variable) of the experiment.

Extraneous variables should be controlled where possible-could provide alternative explanations for the effects.

Confounding variables: Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

1. Situation variables: aspects of the environment that might affect the participant’s behavior, e.g. noise, temperature, lighting conditions, etc. Situational variables should be controlled so they are the same for all participants.

Standardized procedures are used to ensure that conditions are the same for all participants. This includes the use of standardized instructions.

2. Participant variables; his refers to the ways in which each participant varies from the other, and how this could affect the results e.g. mood, intelligence, anxiety, nerves, concentration etc.

e.g. if a participant that has performed a memory test was tired, dyslexic or had poor eyesight, this could effect their performance and the results of the experiment. The experimental design chosen can have an affect on participant variables.

Situational variables also include order effects that can be controlled using counterbalancing, such as giving half the participants condition 'A' first, while the other half get condition 'B' first. This prevents improvement due to practice, or poorer performance due to boredom.

3. Experimenter effects: experimenter unconsciously conveys to participants how they should behave - this is called experimenter bias.

The experiment might do this by giving unintentional clues to the participants about what the experiment is about and how they expect them to behave. This affects the participants’ behavior.

personal attributes (e.g. age, gender, accent, manner etc.) of the experiment can affect the behavior of the participants.

4. Demand characteristics; clues in an experiment which convey to the participant the purpose of the research.

Participants will be affected by: their surroundings, the researcher’s characteristics, the researcher’s behavior (e.g. non-verbal communication), and their interpretation of what is going on in the situation.

Experimenters should attempt to minimize these factors by keeping the environment as natural as possible, carefully following standardized procedures. Finally, perhaps different experimenters should be used to see if they obtain similar results.

Internal validity refers to whether the effects observed in a study are due to the manipulation of the independent variable and not some other factor. In-other-words there is a causal relationship between the independent and dependent variable.

Internal validity can be improved by controlling extraneous variables, using standardized instructions, counter balancing, and eliminating demand characteristics and investigator effects.

External validity refers to the extent to which the results of a study can be generalized to other settings (ecological validity), other people (population validity) and over time (historical validity).

External validity can be improved by setting experiments in a more natural setting and using random sampling to select participants.

This is the degree to which a test accurately predicts a criterion that will occur in the future. For example, a prediction may be made on the basis of a new intelligence test, that high scorers at age 12 will be more likely to obtain university degrees several years later. If the prediction is born out then the test has predictive validity.

Internal reliability assesses the consistency of results across items within a test.

External reliability refers to the extent to which a measure varies from one use to another.

Inter-rater reliability: The test-retest method assesses the external consistency of a test. This refers to the degree to which different raters give consistent estimates of the same behavior. Inter-rater reliability can be used for interviews.

Observer reliability; when referring to observational research. researcher  observes the same behavior independently (to avoided bias) and compare their data. If the data is similar then it is reliable.

Parametric tests: tests that can be used when data a) have normal distribution, b) are interval/ratio (the mean average is used) and c) have similarity of variance.

Non-parametric tests: used if one or more of hese three conditions are not met. 

Level of measurement: nominal- categories, ordinal- ranked data and interval/ratio- real mathmatical scores. 

Hypothesis testing for a relationship= correlation design. Hypothesis testing for a difference:

Wilcoxon signed-rank test if level of measurement of the data is ordinal/interval/ratio and is a repeated measures or matched pairs design

Mann-Whitney U test if level of measurement of the data is ordinal/interval/ratio and is a independent group design 

Chi-squared test if level of data is nominal and is an independent groups design

(Spearman test= hypothesis predicts a correlation and the level of measurement of data is at ordinal data)

Results aren't accepted if there's a 1 in 10 likelihood of more of the results being due to chance- p≤0.10 

Results are often accepted as statistically significant if there's a 1 in 10 or less likelihood of the results being due to chance- p≤0.05

Results are nearly always accepted if there's a 1 in 100 likelyhood or less likelihood of the results being due to chace- p≤0.01

Concerns the issue of accepting or rejecting the null hypothesis incorectly. 

Type one error- made when someone optimistically accepts their alternative/experimental hypothesis: if the null is wrongly rejected (saying the study worked/ accecpted the alternative/experimental hypothesis)

Type two error- made when someone accepts the null hypothesis pessimistically; the null is wrongly accepted (saying the study didn't work and rejected the alternative/experimental hypothesis)

Normal distribution; found when resuls cluster nicely around the mean and around the centre point of a spread of scores. There's normal distribution when the mean, median and mode match.

If they do not match, then the distribution is skewed 

Descriptive statistics; describe the data including measures of central tendancy (mode median and mean) and measures of dispersion (range and standard deviation) and graphs and charts.

Inferntial statistics; draw inferences about the data, rather than merely describing them. Inferential stats invlolve the use of statistical tests:

-Mann-Whitney U

-Wilcoxon signed-rank test

-Spearman rank correlation coeeificent 

-Chi-squared