Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Psychology > Cognitive Methods > Flashcards

Cognitive Methods Flashcards

You may prefer our related Brainscape-certified flashcards:
AP® Psychology flashcards GRE® Psychology flashcards Psychology 101 flashcards
1
Q

what is objectivity?

A

Objectivity is a feature of science, and if something is objective it is not affected by the personal feelings and experiences of the researcher.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what is reliability?

A

the experiment can be easily repeated to get the same results - this involves using standardised procedures

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what is validity?

A

can be either internal or external:

internal: e internal validity of an experiment is the extent to which we can be sure that changes to our dependent variable or variables are purely a product of our independent variables; this includes CONSTRUCT VALIDITY (how well does the experiment test what it sets out to test) and POPULATION VALIDITY (how well do the participants represent the target population of the test).

External: includes ecological validity, field have better than lab as they are carried out in a realistic setting.
Task validity, if the task is similar to one they would encounter in real life

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

what is an experimenter effect?
also explain the difference between a single blind procedure and a double blind procedure:

A

this occurs when the experimenter has a strong expectation of what their study will show - this can unconsciously affect how they communicate with their participants and so affect their results, the best experiments involve standardised instructions so it all happens the same way

experimenter effects can be controlled by:

a single blind procedure - participants do not know which condition they are taking part in and stops participants expectations affecting the results

double blind procedure -
A type of study in which neither the participants nor the researcher knows which treatment or intervention participants are receiving until the clinical trial is over

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what are demand characteristics?

A

these are any features of the experiment that give away to participants what the study is about - once the participants know their opinions this will affect the results, repeated experiments can cause this to be an issues as it can make it obvious what the study is about and potentially alter the results.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

explain the difference between the independent and dependant variable?

A

independent is manipulated
dependent is measured

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

explain experimental and null hypothesis:

A

a hypothesis is a statement about an outcome of a study of two or more variables. It is a prediction - experimental methods look for cause and effect - IV has an affect on the DV.

Experimental hypothesis:/ alternative hypothesis:
significance

null:
no significance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

difference between a one tailed and 2 tailed test and hypothesis:

A

1 tailed: when the hypothesis predicts the direction of the results

2 tailed: if a hypothesis does not state a direction but simply says one factor affect another/ there will be a difference between 2 sets of scores without saying the direction of the difference

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what is an operationalise variables:

A

where you narrow down the topic area in order to measure it directly

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what is an extraneous variable:

A

factors that an experimenter will try to control in order to reduce any unwanted influences on the IV, so it is more reliable - e.g. noise

if the extraneous variable does influence the DV and makes it look as though the effect was on the IV then it is called a CONFOUNDING VARIABLE

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what are situational variables:

A

the situation is kept constant as possible for each participant, good if in a lab as easy to control

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what is a participant variable:

A

any characteristic or aspect of a participant’s background that could affect study results, even though it’s not the focus of an experiment.

e.g. age, gender, culture etc

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

what is a lab experiment and explain it:

A

non natural and artificial setting for participants, controlled conditions

for standardised controls:
a common design is to have an experimental group and a control group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

2 strengths of lab experiments:

A
  • high reliability as high control over variables
  • increased objectivity as good control over extraneous variables
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

2 weaknesses of lab experiments:

A
  • lack ecological validity as it is is an unnatural setting for participants
  • internal validity is reduced as every variable cannot be controlled like how participants are feeling on the day
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

explain and describe field experiments:

A

familiar conditions when IV is manipulated to see an effect on the DV - natural setting

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

2 strengths of a field experiment:

A
  • good ecological validity as it is in a natural place so is more generalisable
  • no experimenter bias an participants do not know they are in an experiment, so demand characteristics are low.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

give 2 weaknesses of a field experiment:

A
  • reduced reliability as it is hard to control extraneous variables and therefore the findings are less certain
  • bad ethics as you dont get informed consent or debrief them and there is an invasion of privacy, so harder to get passed by BPS/APA.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

what is independent measures design?

A

different individual in different groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

2 strengths of independent measures design:

A
  • order effects like practice, fatigue and boredom are avoided as they only do one condition
  • no demand characteristics as they cant compare different conditions of the study because they only took part in one so are unlikely to guess the aim
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

2 weaknesses of independent measures design:

A
  • you have to find 2 sets of people which can be time consuming and expensive
  • statistical tests can be less reliable as there is more variation between the two conditions
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

explain randomisation:

A

Involves allocating individuals to certain groups, in the case of independent measures design, there are three ways that you can randomly allocate participants. One involves manual selection which means that all names are placed in a hat for example, and participant 1 pulled out goes to condition one, and participant 2 to condition 2. The others involve computer selection or using a random number table.
Randomly allocating participants to different conditions can eliminate bias as it is due to chance factors that those involved end up in the different conditions.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

what is repeated measures design:

A

same individuals and them them on 2 or more separate occaasions

24
Q

give 2 strengths of repeated measures design:

A
  • fewer participants needed so more economical because each participant is in both conditions
  • higher quality statistical tests can be applied because of less variation between the conditions
25
Q

give 2 weaknesses of repeated measures design:

A
  • there may be order effects like boredom or fatigue etc by doing both conditions (these can be counterbalanced)
  • demand characteristics may occur if the person guesses what the study is about in the first condition and commits social desirability for example when doing the second condition
26
Q

what is matched pairs design:

A

an experimental design where pairs of participants are matched in terms of key variables, such as age and IQ

27
Q

give 2 strengths of matched pairs design:

A
  • demand characteristics are less of an issue as participants only take part in one condition so are unlikely to guess the aim of the research
  • order effects are avoided as participants only take part in one condition so they should not get bored.
28
Q

give 2 weaknesses of matched pairs design:

A
  • it is time consuming and not always effective because close matches can be hard to find
  • if one person drops out then the pair has to be dropped which could be expensive if they have to be replaced so less economical
29
Q

what are mixed designs:

A

where experiments involve more than one IV are are a mix of repeated and independent measured.

30
Q

explain the mean, median, and mean (measures of central tendency):

A

Measures of central tendency inform us about central or middle values of a set of data.
There are three different “averages” - ways of calculating a typical value for a set of data.
There are three typical ways to assess this.
One is the mode. This tells you what the most common score is. To work it out, count how many times each value appears in your data. The most common is your mode. If two values appear the same number of times, then you have two modes. The mode is most useful when you have data that is categorical or ordinal, for example if your data is ‘yes/no questions.
Another is the median. To work this out, put your data in order, and count half way through. The value right in the middle is your median. If two values are in the middle, work out what is half way between them: that is your median. This is a particularly useful statistic if you have extreme scores.
Finally there is the average, or mean. To work this out, add up the data and divide it by the number of values you have. For example, if ten participants gave you a speed estimate, add up the ten estimates and divide this by ten. This is the most common way to describe the middle of your data set.
You may be asked to give your calculations to decimal places or significant figures. The number of decimal places is the number of digits after the decimal point. So, 8.5219 has four decimal places, 8.5 has one decimal places and 8 has no decimal places. In any number, the first significant figure is the one with the highest place value. It is the first nonzero digit counting from the left. So, 8.5219 to one significant figure is 8.

31
Q

give 2 strengths of the mean:

A
  • the mean makes use of all the values in a set of data so as they are all used it is more accurate and reflective of what has been found
  • the mean is a powerful statistic used in estimating differences in research and for this is has more credability
32
Q

give 2 weaknesses of the mean:

A
  • extreme values in the set can distort the mean because a high or low score will skew the final results and may not reflect what the majority of the scores show.
  • the mean suffers from ‘nonsensical’ values which can be misleading like how can you have 0.4 of a child!!!
33
Q

give 2 strengths of the median:

A
  • the median unlike the mean is not affected by extreme scores and is unlikely to be distorted.

- Easier and quicker to calculate than the mean if there are no large groups.

34
Q

give 2 weaknesses of the median:

A
  • the median is not as sensitive and the mean because not all the values are reflected in the median.
  • The median does not take into consideration the exact distances between values, therefore a true reflection of what the data means cannot be fully achieved.
35
Q

give 2 strengths of the mode:

A
  • the mode is useful for data that is in categories
  • the mode is unaffected by extreme scores
36
Q

give 2 weaknesses of the mode:

A
  • it is not a useful way of describing data when there can be several models
  • the mode does not take into consideration the exact distances between values, therefore a true reflection of what the data means cannot be fully achieved
37
Q

what is a frequency table:

A

a table of data that shows the number of times, or frequency, of each data value or group, or combined values. - It can be used for discrete data or continuous data. - the grouped data must not overlap.

  • Discrete data - data that stands alone - counted
  • Continuous data - data that starts from a set point and continues through a range of data scores - measured
38
Q

what is the range?

A

the difference between the highest and lowest scores.

39
Q

give 2 strengths of the range:

A
  • it provides you with direct info. from all data including extreme scores so is reflective of what is found
  • it is easy to calculate and quick to complete compared to standard deviation which can be time consuming and difficult
40
Q

give 2 weaknesses of the range:

A
  • affected by extreme scores, therefore the results can be misleading and unreliable
  • does not take into account the number of times a piece of data occurs in the set, therefore we do not know how the individual scores lie in relation to the others
41
Q

what is the standard deviation?

A

The standard deviation is calculated based on the average distance from the mean of your data: if you have a lot of extreme scores, you will get a higher standard deviation; if your data are very stable and clustered closely around the mean, you will get a lower standard deviation. What is most useful for you to look at is the difference between the standard deviations of the groups in your study. If there is a big difference in the standard deviation, this means one group has more variation in their scores.

42
Q

give 2 strengths of the standard deviation:

A
  • it is a more precise measure of dispersion because all the values are taken into account
  • the standard deviation and variance are the most sensitive of all the descriptive measures, therefore is more likely to be accurate and reliable.
43
Q

give 2 weaknesses of the standard deviation:

A
  • may hide some of the characteristics of the data set e.g. extreme values, therefore extreme values could distort the results
  • both can be time consuming and difficult to calculate is you do not have an appropriate calculator.
44
Q

what is the difference between a bar chart and a histogram?

what does a histogram do?

A

histograms MUST HAVE gaps between bars but bar charts MUST NOT

it shows how data is distributed across the different intervals

45
Q

explain the differences between symmetrical, positive skew and negative skew in terms of distribution:

A

symmetrical is normal distribution,
positive skew - more scores are on the left
negative skew - more scores are on the right

46
Q

explain ratio, interval, ordinal and nominal data:

A

Ratio - difference between the measurements, true zero exists

interval - differences between measurements but no true zero

ordinal - ordered categories (rankings, order, or scaling)

nominal - categories (no ordering or direction)

47
Q

what does it mean to sense check data?

A

This is simply to have a ‘quick look’ at your data before you begin any analysis to see if it appears significant or not. For example, if you had a set of sores for Group A of 1, 2, 1, 2, 1, 3, 2 and for Group B of 1, 2, 1, 1, 2, 3 then a ‘sense check would tell you that logically, there doesn’t look to be any significant difference between the scores and you may decide not to conduct a statistical test at this point, but instead improve your study and try to test the effect of the IV on the DV with different controls or measures. This can also be used to decide on a statistical measure, for example you would be able to sense check the data of 1, 2, 3, 4, 5, 6, 7, 8, 9, 125 and decide that the range would probably not be a suitable measure of dispersion here because of the extreme values.

48
Q

explain probability and levels of significance (type I and type II errors):

A

Using the ps.0.05 level of significance means if the null hypothesis is true, we would get our result 5 times out of 100 (or 1 out of 20). So, we take the risk that our study is not one of those 5 out of 100. Rejecting the null hypothesis and accepting that the hypothesis is correct, for 95% of the time.
There is always a possibility that we are making a mistake in rejecting or accepting the null hypothesis. Mistakes in rejecting are called a Type I Error - rejecting the null hypothesis when it is true. Alternatively, if the significance level is above the cut-off value, we accept the null hypothesis this is a Type Il Error - accepting the null hypothesis when it is not true.
Errors like these are most likely when the level of significance for statistical tests is too high to allow for chance (for example ps.0.01, which is 1% chance) or too low to eliminate chance (for example p≤.0.50, which is 50% chance)
Why do psychologists take the bigger gamble of 0.05 rather than 0.01 cutoff? There is a trade-off when studying human behaviour between overestimating and underestimating the effect of chance.
When you use a statistical test (such as a Wilcoxon), the results will show an exact p value
(e.g., p = 2) in the table of critical values

49
Q

what is the man whitney test?
and when do you use it?

A

The Mann-Whitney U is one of the non-parametric tests of difference and is used to test a null hypothesis that two independent samples of scores could have been drawn from the same population. The medians of any two independent samples of scores will always differ to some degree. However, you cannot tell just by inspecting the scores whether the difference is meaningful, or not. On the one hand it could be the result of sampling from a single undifferentiated population. On the other hand, it might be because the two groups really come from different populations. This might happen after
being taught different mnemonic techniques before learning a list of words, or after being tested under different conditions.
The Mann- Whitney U Test tells you whether the difference between the samples is so great as to make it unlikely that the null hypothesis, (that they came from the same population), is correct.

When to use it?
You can use the Mann Whitney when:
You require a test of difference between two samples of data.
The samples are independent, i.e., each participant contributes only one score to only one of two treatment groups.
The scores represent measures on either the ordinal or interval scales.
The population distribution is either unknown or likely to be very non-normal.

50
Q

what is the wilcoxon fomula and when do you use it?

A

The Wilcoxon Test is one of the non-parametric tests of difference and is used to test the null hypothesis that two related sets of scores could have been drawn from the same, or identical, populations. Any two related sets of scores will always differ to some degree.
However, you cannot tell just by inspecting the scores whether the difference is a real one or not. On the one hand it could be the result of sampling from different parts of a single population. On the other hand, it might be because the two groups really come from different populations. For example, a researcher might be looking into whether counselling really helps people feel better about themselves and so she administers a psychometric test to a sample of volunteers before and after some sessions of counselling. The related sets of data obtained in this way can be analysed using the Wilcoxon Test to find out whether the difference between the samples is so great as
to make it unlikely that the null hypothesis, (that they came from the same, or identical, populations), is correct.

When to use it?
You can use the Wilcoxon Test when:
You require a test of difference between two samples of data.
The samples are related i.e., each participant contributes a score to both of two treatment groups.
The scores represent measures on either the ordinal or interval scales.
The population distribution is either unknown or likely to be very non-normal.

51
Q

when can we say significance can be shown? - wilcoxon

A

if the calculated value is equal to or less than the critical value in the table - wilcoxon

52
Q

what is a case study?
explain the case study of HM:

A

A case study is a non-experimental research method. It is an in-depth and detailed study of a single individual or single group of people and can involve many procedures, such as interviewing, observing or testing one person/group. A case study is used where an unusual example of behaviour can provide an insight into a theory or psychological process. For example, a case study of a brain-damaged person can provide some knowledge about what a particular part of the brain does.

HENRY MOLAISON (HM)
Scoville and Milner (1957) described the case of H.M. who fell off his bicycle when he was 7 years old, injuring his head. He began to have epileptic seizures when he was 10. By the age of 27 the epileptic attacks prevented him from living a normal life. Scoville performed an experimental surgery on H.M.’s brain to stop the seizures. Specifically, he removed parts of HM’s temporal lobes (part of his hippocampus along with it). The seizures stopped but H.M. suffered from amnesia for the rest of his life.
For 55 years Henry participated in numerous experiments, primarily at Massachusetts Institute of Technology (MIT) where Professor Suzanne Corkin and her team of neuropsychologists assessed him. Henry died on December 2nd, 2008, at the age of 82.
Until then, he was known to the world only as “HM” but on his death his name was revealed. A man with no memory is vulnerable, and his initials has been used while he lived in order to protect his identity.

After Henry’s death his brain was dissected into 2000 slices and digitized as a three-dimensional brain map that could be searched by zooming in from the whole brain to individual neurons. Thus his tragically unique brain has been preserved for posterity.
The case study of H.M. provides information on how particular brain areas and networks are involved in memory processing. This helped scientists to formulate new theories about memory functioning.

53
Q

give 2 strengths of case studies:

A

They gather a lot of in-depth and detailed information about one person/ group which other research methods can miss.

The rich data that they obtain can be used to develop new theoretical ideas.

54
Q

give 2 weaknesses of case studies:

A

They are difficult, or even impossible, to replicate as they are usually based on an individual’s unique circumstances.

There is a lack of generalisability (the sample is too small to be representative).

55
Q

what is qualitative data:

A

data which is expressed in detailed descriptions - usually from open questions/ case studies etc

56
Q

give 2 strengths of qualitative data:

A
  • It is useful to describe information that is lost in quantitive analysis
  • It can generate ideas for further research
57
Q

give 2 weaknesses of qualitative data:

A
  • The process of obtaining qualitative data is often interpretative, it is therefore subjective with no one correct interpretation of the data is more open to bias and misinterpretation.
  • It is difficult to replicate findings and therefore such research may lack reliability.

Psychology (10 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions