Section C: Data Handling and Analysis

Question 1

i) What is ‘qualitative data’? - is it numerical?

ii) How can it be collected and analysed?

iii) What are some characteristics of qualitative data?

iiii) Where is it often collected?

Answer 1

i) Qualitative data is imprecise, non-numerical data (e.g. text, video, recordings).

ii) Qualitative data can be collected using diary accounts or in-depth interviews, and analysed using thematic analysis (grouped according to themes).

iii) Rich in detail, often subjective. Often used to reveal attitudes, beliefs and opinions. Low in reliability.

iiii) Often collected in real life settings.

Question 2

i) What is ‘quantitative data’ - numerical?

ii) How is quantitive data analysed?

iii) What are some characteristics of quantitative data?

iiii) Where is it often collected?

Answer 2

i) Quantitative data is PRECISE, NUMERICAL data that can be quantified. It can be counted or measured.

ii) Quantitative data lends itself to statistical analysis, while qualitative data is grouped according to themes.

iii) Objective. Brief and concise, limited –> low in detail. Not used to reveal attitudes, beliefs and opinions. High in reliability.

 iiii) Collected in artificial, controlled settings.

Question 3

Would data collection through observations via the use of behaviour categories and ratings of behaviour produce qualitative or quantitative data?

Answer 3

QUANTITATIVE (tallies).

Question 4

A recovering patient describes his experience of schizophrenia - would this produce qualitative or quantitative data?

Answer 4

QUALITATIVE (rich in detail, subjective).

Question 5

Do case studies usually produce qualitative or quantitative data?

Answer 5

Qualitative

Question 6

(A03) Strength of QUANTITATIVE DATA. (think is it scientifically objective?)

Answer 6

P: Quantitative data is scientifically objective.

E: Quantitative data is more objectively analysed than qualitative data. This is because numerical data can be interpreted using statistical analysis.

E: This is a strength as there is less interpretation involved using quantitative data and comparisons can be drawn more easily between data sets. This is because statistical analysis are based on the principles of mathematics and allow researchers to objectively conclude whether statistically significant relationships or differences have been found.

L: This means analysis is free from bias and interpretation, so high in objectivity.

Question 7

How does qualitative data compare in terms of being scientifically objective?

Answer 7

P: Qualitative data is highly SUBJECTIVE.

E: This is because qualitative data is non-numerical and includes data that is rich in detail and therefore subjective.

E: As such, data cannot be easily compared or categorised

L: This means analysis is open to bias and interpretation, so is scientifically subjective.

Question 8

(A03) Strength of QUANTITATIVE Data (REPLICABILITY- how much researcher involvement?)

Answer 8

P: QUANTITATIVE data can be very easily replicated

E: This is because it is based on measured, NUMERICAL values.

E: Such data requires minimal involvement from researchers.

L: So a consistent analysis by multiple researchers is highly reliable.

Question 9

(A03) Strength of QUALITATIVE Data (depth of detail/insight).

Answer 9

P: QUALITATIVE data is highly valid.

E: This is because it is based on non-numeric, detailed responses / accounts.

E: This is a strength as such data is in-depth and insightful.

L: Therefore, a researcher has the opportunity to capture rich, descriptive data about how people think and behave.

Question 10

(A03) Limitation of QUALITATIVE Data - how does it compare in terms of depth of detail/insight?

Answer 10

P: QUANTITATIVE data is less valid.

E: This is because it is based on numeric data and data that is quantifiable.

E: This is a limitation as such data is narrow and lacks DEPTH of detail and the nature of turning thoughts and feelings into numbers can be seen as superficial.

L: Researchers therefore have less opportunity to capture rich data about how people think and behave.

Question 11

Give an example of how QUALITATIVE Data lacks validity?

Answer 11

(E.g. closed questions limit your response to prescribed options).

When gathering quantitative data, respondents may be forced to select answers which do not represent their real thoughts and feelings, (YES, NO, IN THE MIDDLE) leading to data which is superficial, lacks detail and therefore has lower validity.

Question 12

In what type of setting is qualitative data likely to be gathered?

Answer 12

QUALITATIVE data is likely to have been gathered in more natural environments (e.g. a researcher carrying out a case study of a person’s experience of mental illness would make use of a RANGE of qualitative methods such as interviews, observations in hospital/ at home).

Question 13

How is this setting a STRENGTH of qualitative data? (setting qualitative data is likely to be gathered in).

Answer 13

P: Qualitative data is highly valid.

E: This is because QUALITATIVE data is likely to have been gathered in more natural environments (e.g a researcher carrying out a case study of a person’s experience of mental illness would make use of a range of qualitative methods such as interviews, observations in hospital / at home).

E: This is a strength because in natural environments, participants are more likely to behave more naturally/ more relaxed, meaning they are more likely to give honest/realistic answers and their behaviour is likely to be more representative of the real world.

L: Therefore, the findings are more likely to be credible and valid.

Question 14

In what type of setting is QUANTITATIVE data likely to be gathered?

Answer 14

In artificial, controlled environments (i.e. a lab).

Question 15

How is this a limitation of quantitative data? (setting quantitative data is collected in).

Answer 15

P: QUANTITATIVE data is less valid.

E: This is because QUANTITATIVE data is likely to have been gathered in artificial, controlled environments (E.g. researchers looking at the impact of rehearsal on memory might recall the NUMBER OF ITEMS RECALLED CORRECTLY from a list).

E: This is a weakness as if carried out in unnatural environments, ppts are likely to behave unnaturally as opposed to natural environments.

L: Therefore, findings may not be credible from quantitative data.

Question 16

Which type of data gathering is more TIME and COST effective?

Answer 16

QUANTITATIVE DATA.

Question 17

How is this a STRENGTH of quantitative data? What does this allow for? (think practically - cost/time effective?)

Answer 17

P: QUANTITATIVE data is more time and cost effective to gather.

E: This is because methods that gather QUANTITATIVE data immediately produce information that is numerical from large sample sizes (e.g. questionnaires that are largely distributed).

E: This is a strength as data can be easily compared and analysed without transformation.

L: Therefore, so such methods produce a large amount of data fairly quickly.

Question 18

Whereas how does qualitative data compare? (in terms of practicality - is it cost and time efficient?)

Answer 18

P: Gathering QUALITATIVE data is less time and cost effective.

E: This is because methods that gather QUALITATIVE data produce information that has to be transformed before analysis can be carried out (i.e. thematic)

E: This is a weakness as transforming data into categories can be a lengthy and subjective process.

L: Therefore, this means that such methods may be more difficult to run.

Question 19

What type of data is more useful in making broad generalisations?

Answer 19

Quantitative —> We are able to generate conclusions/generalisations from data. More nomothetic.

Question 20

In terms of issues and debates, what kind of approach does the use of qualitative data take?

Answer 20

IDIOGRAPHIC.

Question 21

(A01) What is meant by primary data?

How can primary data be obtained?

Answer 21

Primary data is collected FIRST-HAND by the RESEARCHER directly from a group of participants FOR A SPECIFIC RESEARCH PURPOSE.

The researcher might collect information via observation, psychometric test, interview etc and this data may be qualitative or quantitative.

Question 22

(A01) What is meant by secondary data?

How does a researcher use secondary data?

Answer 22

When a researcher uses secondary data, this means that SOMEONE ELSE HAS ALREADY COLLECTED THE INFORMATION FOR A DIFFERENT PURPOSE and the INFORMATION HAS BEEN STORED ON RECORD FOR USE BY OTHER RESEARCHERS.

The researcher will re-analyse this second hand data for a new purpose.

Question 23

Give some examples of types of secondary data?

Answer 23

1. Statistical data from the government.
2. Medical records of patients with Schizophrenia from a psychiatric hospital that somebody else has gathered.

Question 24

(A03) What is a limitation of primary data? (think practical issues - is it time consuming?).

Answer 24

P: Primary data can be TIME CONSUMING and EXPENSIVE to conduct.

E: The researcher has to recruit participants to their sample and set up their experiment in a research environment (e.g. a lab). They must also consider ethical guidelines (i.e. the right to withdraw and informed consent). Although research is permitted to be carried out without consent in a public domain, issues arise concerning right to withdraw.

L: This is a limitation of primary data as it is therefore more costly and demanding than accessing pre-existing data from primary sources.

Question 25

How do practical issues of primary data compare to those of secondary data?

Answer 25

P: Secondary data is a lot MORE TIME-EFFICIENT AND COST-EFFICIENT than primary data. E: The researcher simply has to consult pre-existing data from previous studies, rather than conducting their own. E: This is a strength as they do not need to gather research materials or recruit participants/ gather consent to make their research ethically valid as the data already exists in the public domain to be used in further studies.

Question 26

(A03) Criticism of Secondary Data (think validity compared to primary data). ii) How does this also link to temporal validity?

Answer 26

P: Secondary data is LOWER IN VALIDITY than primary data. E: This is because SECONDARY DATA has been collected for a different purpose. E: This means that FINDINGS MIGHT NOT BE ENTIRELY RELEVANT TO THE RESEARCH QUESTION. L: Therefore data MAY NOT NEATLY FIT THE NEEDS OF THE INVESTIGATION. This also links to temporal validity --> LOW temporal validity of secondary data --> Primary data (collected for a different purpose) may have been compiled numerous years ago and may not be generalisable to the target population at the time the secondary research is being conducted. Therefore, means data is not representative of the population today. Meanwhile, primary data is directly collected by the researcher and has high temporal validity as participants take part in the study DURING THE TIME PERIOD that the data is looking into.

Question 27

(A03) Why is primary data likely to be reliable? (think in terms of replication - is it easily replicated?)

Answer 27

P: Primary data is likely to be reliable E: This is because data is collected first-hand. As such, a researcher can carefully plan their research and operationalise variables to measure behaviour. E: These procedures can be well-documented and carried out in a controlled manner (e.g. following a standardised procedure). L: This makes replication possible to check for the consistency of research findings.

Question 28

(A03) Why might replication not be possible for secondary data?

Answer 28

-When reviewing primary data and using it as secondary data, we might not know the procedure the researcher used to conduct their study. -Without this knowledge of the study's procedure and methodology, other researchers are not able to replicate the study and check for the consistency of the research findings.

Question 29

Ethical Considerations - Primary Data - why is it much more difficult compared to secondary data?

Answer 29

P: A limitation of using primary data is that there are many ethical considerations that need to be considered. E: Primary data involves gathering participants and setting up a research study directly. If the research objective studies potentially sensitive issues such as abnormality and mental illness, it would involve the use of vulnerable participants. E: Therefore, researchers will need to take care when designing their research not to cause psychological harm to participants and ensure fully informed consent is gained prior to research. Ethical approval for research would also need to be gained from the BPS.

Question 30

Are there ethical considerations that need to be made when using secondary data? ii) What do these issues centre around? iii) In what instance is consent implied? iiii) When may ethical approval need to be sought?

Answer 30

There are LESS but still ethical issues pertaining to the use of secondary data. They largely centre around the issue of confidentiality, consent, and safe storage. If data is in the public domain (e.g. internet source) then consent is implied. Ethical approval need only be sought if personal information can be used to identify participants or where access to data is restricted.

Question 31

(A01) What is meta-analysis?

Answer 31

-A QUANTITATIVE research technique that aims to find the results by POOLING data from multiple studies to arrive at ONE COMBINED ANSWER. -Data from these studies are reviewed together and the combined data can be tested by statistical techniques to assess effect size.

Question 32

What does meta-analysis integrate results from? What is the sample size in a meta-analysis? How does this overcome the issue of small sample sizes?

Answer 32

Integrates results from ALL published studies on the same topic. The sample size is the number of studies regarding the same research question. This in turn helps overcome the issue of small sample sizes because it combines data/conclusion from lots of smaller studies into one large study, it allows the identification of trends and relationships that wouldn't be possible from individual smaller studies.

Question 33

Give an example of when a meta-analysis may be used? What does it generally focus on?

Answer 33

In clinical psychology, examples include the effectiveness of treatments across different patient groups. It generally focuses on effect sizes. For example, a meta-analysis on the effectiveness of CBT for depression will focus on the size of the effect of CBT found by all of the research gathered.

Question 34

(A03) Strength of meta-analysis (are they robust and generalisable?) i) What limitation does meta-analysis overcome (think sample size and does it settle controversies concerning conflicting results)? ii) How do meta-analysis increase the generalisability of the results from individual studies?

Answer 34

P: Meta-analysis overcomes the limitation of small sample sizes of individual studies, and reduces the risk of false-negative results. Moreover, meta-analysis can settle controversies resulting from studies with conflicting results. In addition, combining studies with varying sample sizes and patient populations can increase the GENERALISABILITY of the results of individual studies; this allows the results of the meta-analysis to be more representative of a wider population.

Question 35

(A03) Limitation of meta-analysis (what kind of studies are likely to be published?)

Answer 35

Meta analyses may be impacted by a publication bias. The problem here is that "positive" studies (where statistical significance has been found) are more likely to be published and subsequently, more likely to be accessed and used within a meta-analysis. This may lead to inaccurate conclusions being drawn.

Question 36

What is meant by the 'file drawer problem' and how is this a problem with meta-analyses?

Answer 36

" The File Drawer Effect,” coined in 1979 by Robert Rosenthal, refers to the fact that in science many results remain unpublished, especially negative ones. The exclusion of studies with negative findings may lead to SKEWED REPRESENTATION OF THE AVAILABLE EVIDENCE.

Question 37

How can this issue with publication bias and the 'file drawer effect' in meta-analysis be dealt with? What may happen if there are too many studies to include all?

Answer 37

Psychologists should locate as many studies as possible that might be suitable for inclusion using as many techniques as possible (e.g., computer and Internet searches, e-mails to active researchers, consulting reference lists, manual searching of related journals). If there are too many studies to include all, the analyst might randomly sample from the studies or, more commonly, narrow the focus to a meaningful subliterature.

Question 38

How might researcher bias impact upon the studies selected for a meta-analysis? How can this be dealt with and improved by meta-analysts?

Answer 38

Researchers often have a tendency to favour studies that align with their preconceived notions or desired outcomes. This bias may lead to the exclusion of studies with contradictory findings (the 'file drawer effect') resulting in a skewed representation of the available evidence. To improve this, it is important for meta-analysts to employ rigorous and transparent selection criteria and consider a wide-range of studies to minimise the impact of researcher bias.

Question 39

(A03) ETHICS - Strength of meta-analysis? However, what is a potential issue with this - is consent always guaranteed?

Answer 39

Meta-analysis is SECONDARY data. Secondary data already exists in the public domain so researchers make the assumption that informed consent has already been gathered. However, this is not always guaranteed like it is when primary research is conducted.

Question 40

(A03) Are meta-analyses scientific in their approach? How kind of testing can be carried out from data from meta-analysis? Is this an objective way to analyse research findings?

Answer 40

Meta analyses adopt a scientific approach. They largely gather QUANTITATIVE data to calculate the size of an effect. Scientific testing can be carried out on the data to look for significance of findings which is an OBJECTIVE way to analyse research findings.

Question 41

How might reliability be established within a meta-analysis? What should researchers look for when pooling the data from multiple studies?

Answer 41

Researchers should assess the reliability of the data from multiple studies BY REVIEWING THE PROCEDURE and METHODOLOGY of the individual studies. They should see if a standardised procedure has been followed --> replicability high to check for the consistency of findings.

Question 42

  What do measures of central tendency analyse?

Answer 42

Measures of central tendency analyses how close scores are to the average participant response. -They are used to summarise sets of numbers, giving a score which is representative of the set.

Question 43

What are the THREE measures of central tendency?

Answer 43

-MEAN -MEDIAN -MODE

Question 44

What kind of data is the MEAN used as a measure of central tendency for?

Answer 44

INTERVAL DATA

Question 45

What is the mean?

Answer 45

The arithmetic average; it is calculated by first adding all of the data scores together and then dividing this total by the actual number of scores. E.g. 2 + 5 + 6 + 7 + 10 = 30 Then 30 / 5 (total no. of scores) = 6 

Question 46

(A03) What is a strength of using the MEAN as a measure of central tendency?

Answer 46

The mean can be considered as an accurate and sensitive measure of the average set of scores as it takes ALL scores in the data set into consideration. This is a strength as it is a highly representative measure of central tendency so is an accurate representation of the entire data set.

Question 47

(A03) What is a limitation of using the MEAN as a measure of CENTRAL TENDENCY?

Answer 47

A weakness of using the mean is that it can be skewed by an anomalous score. This is a weakness because rogue scores in the data set can significantly increase or decrease the calculated mean score which makes it unrepresentative of the raw data set (inaccurate reflection of data).

Question 48

What kind of data is the MEDIAN used as a measure of central tendency for?

Answer 48

ORDINAL DATA

Question 49

What is the MEDIAN? How is it calculated?

Answer 49

This is the middle score or value, found when the data is turned into an ordered list:  E.g.1, 2, 5, 6, 7, 9, 11= 6  If there is an even number of scores, the two middle scores are averaged to find the median.

Question 50

(A03) What is a strength of using the median as a measure of central tendency?

Answer 50

P: A strength of using the MEDIAN is that it is unaffected by extreme scores/ outliers as it is only concerned with the middle number in a raw set of data. E: This is a strength because it may more accurately reflect central tendency as a result. IT IS ALSO RELATIVELY EASY TO CALCULATE!

Question 51

(A03) What is a limitation of using the median as a measure of central tendency? (Two points)

Answer 51

P: The median may not be an actual score if there’s an even number of scores, so may be limited in how representative it is of the data set. P: It is not appropriate to use in certain circumstances, for example with small data sets where there are large differences e.g. 1, 2, 1000, 1001 = 501.

Question 52

What kind of data is the MODE used as a measure of central tendency for?

Answer 52

NOMINAL DATA (categorical).

Question 53

What is the MODE?

Answer 53

This is the most commonly occurring/most frequent score:  E.g.2, 2, 2, 5, 6, 9, 11 = 2  If there are two scores which are most common the data set is bi-modal.  If there are three or more scores which are most common the data is multi-modal.  

Question 54

(A03) What is a strength of using the MODE as a measure of central tendency? (2 points)

Answer 54

P: A strength of using the mode is that it is unaffected by extreme, anomalous scores. This is a strength because it may more accurately reflect the central tendency data set. The mode is always an actual score (e.g. no 6.5), so can be considered an accurate representation of central tendency.

Question 55

(A03) What is a limitation of using the MODE as a measure of central tendency? (2 points)

Answer 55

P: A weakness of using the mode as a measure of central tendency is that sometimes a data set doesn’t have a mode, or has many. This means the MODE can be limited in usefulness. P: Also, the mode doesn’t use all the data as it is only concerned with the most frequently occurring numbers from the raw data set. As such, the accuracy of this measure can be questioned.

Question 56

What measure of central tendency is generally preferred?

Answer 56

THE MEAN --> Preferred measure as it uses all of the scores and can be used in other more advanced mathematical analyses such as calculating the standard deviation. The mean is USEFUL if there is a fairly even distribution of scores around the central value.

Question 57

When is the MEDIAN preferred over the MEAN?

Answer 57

If there are a few extreme high or low scores, the MEAN might give a MISLEADING PICTURE. Therefore, in this case the MEDIAN would be preferred (the MIDDLE value --> not skewed by anomalous results).

Question 58

What is the least used measure of central tendency?

Answer 58

The MODE is the least used --> this is because it does not tell us anything about the other scores in the set. It also becomes less meaningful when there is more than one mode.

Question 59

What do MEASURES OF DISPERSION analyse?

Answer 59

Measurement of dispersion analyse how far away scores are from the average participant response ( i.e. their spread or variability). - i.e. how far scores are spread around the mean.

Question 60

Normally, what are the two reasons behind large dispersion?

Answer 60

-Individual Differences AND/OR -Poor experimental control (i.e. procedures aren’t standarised).   

Question 61

What type of data would THE RANGE be used as a measure of dispersion for?

Answer 61

ORDINAL DATA

Question 62

What is the range? How do we calculate it?

Answer 62

This is difference between the highest and lowest score in a set of data; it is calculated by subtracting the lowest score from the highest score.

Question 63

What are two brief strengths of the range as a measure of dispersion?

Answer 63

- The range is easy and simple to calculate. -The range takes into account extreme values.  

Question 64

What are two brief limitations of using the range as a measure of dispersion?

Answer 64

-The range ignores most of the data – it does not reflect the true distribution around the mean.  -The range is easily distorted by extreme values.

Question 65

What type of data would STANDARD DEVIATION be used as a measure of dispersion for?

Answer 65

-INTERVAL DATA (universal scale)

Question 66

What does STANDARD DEVIATION indicate? Does it take into account every score? The larger the standard deviation, are scores more spread out or less spread out relative to the mean?

Answer 66

Standard Deviation indicates the AVERAGE of the DISTANCES OF ALL SCORES AROUND THE MEAN. -It takes into account every score. -The larger the standard deviation of a set of scores, the more spread out they are relative to the mean. -If SD is small, most scores occur very close to the mean.

Question 67

How is STANDARD DEVIATION calculated? What does it tell us?

Answer 67

Standard Deviation is the result of a calculation which measures (collectively) how much individual scores deviate from the mean, and presents this finding as a single number. -It tells us how much data is spread (dispersed) around a central value (the mean).  A large SD score tells us there was lots of variation around the mean/scores were spread widely i.e. that participants in the sample were all responding very differently. 

Question 68

(A03) Strengths of standard deviation to measure dispersion? (Think is data precise, are all values taken into account? Does it allow for the interpretation of individual scores?)

Answer 68

Standard deviation is precise, as all values are taken into account --> this gives a more accurate representation of data distribution / spread than the range meaning that detailed conclusions can be made. It also allows for the interpretation of individual scores in terms of how far that score falls away from the mean.

Question 69

(A03) What are TWO limitations of using standard deviation to calculate dispersion? (i.e. Is it quick and easy to calculate and is it meaningful if data is not normally distributed?)

Answer 69

Standard deviation is complex to compute and difficult to understand as compared to other measures of dispersion. As such, it is not easy or quick to calculate. It is less meaningful if data is not normally distributed.

Question 70

What does LARGER the size of standard deviation show? What does this suggest? (i.e. is data consistent? Role of individual differences?)

Answer 70

The LARGER the size of the SD, the MORE SPREAD OUT the results are --> so the greater the variability in the data. A large spread of data suggests there may be inconsistencies in the data, highlighting individual differences in the data.

Question 71

What does SMALLER size of standard deviation show?

Answer 71

The smaller the spread, the more similar the scores. (More consistency in the data).

Question 72

Calculating Percentages: Formula for percentage change?

Answer 72

Change/ Original x 100

Question 73

Positive, Negative and Zero correlations: What are correlations best described as? Give an example of a STRONG POSITIVE correlation coefficient? Give an example of a WEAK NEGATIVE correlation coefficient?

Answer 73

Coefficients between -1 and 1 Strong positive correlation coefficient = +0.89 Weak negative correlation coefficient = –0.18 (As the closer the figure is to zero, the weaker the correlation).

Question 74

If the coefficient is close to +1 or -1, what does this show?

Answer 74

The variables are very closely related.

Question 75

What is a positive correlation?

Answer 75

As one co-variable increases, so does the other co-variable.

Question 76

What is a negative correlation?

Answer 76

As one co-variable increases, the other co-variable decreases.

Question 77

What is a zero correlation?

Answer 77

There is no apparent relationship/ data/ trend in the two data co-variables.

Question 78

Percentages: Important Note!

Answer 78

‘Per Cent’ means ‘out of 100’. Therefore, 5% literally means 5 out of 100. Convert to decimal = 0.05. To change a fraction to a percentage, divide the numerator by the denominator. For example, for the fraction of 19/36 you would do 19÷36 (on a calculator!)= 0.52777778. 

Question 79

EXAM Q: A total of 375 dreams reported by males included social interaction. 60% of these dreams were classified as aggressive - how many dreams is this?

Answer 79

60/100 = 0.60 SO 0.60 x 375 (to work out 60% of 375) = 225

Question 80

Name FIVE ways of presenting and displaying quantitative data?

Answer 80

1. Graphs 2. Tables 3. Scattergrams 4. Bar charts 5. Histograms

Question 81

What are the different types of graphs?

Answer 81

-Bar Charts -Scatter Graphs -Histograms -Pie Charts

Question 82

How are bar charts displayed? When are bar charts likely to be used?

Answer 82

-Displayed as SEPARATE BARS. -All types of data (often categoric). - When comparing multiple groups on anything - Bar charts are the best for comparing averages (means) and most common (modes)

Question 83

How are scatter graphs presented? When are scatter graphs likely to be used?

Answer 83

(DISPLAYED AS DOTS OR CROSSES) Used for correlational data (i.e. when there is one group with 2 variables such as age and height).

Question 84

How are histograms presented? When are they likely to be used?

Answer 84

-Displayed with bars TOUCHING. -Used for CONTINUOUS, INTERVAL data only.

Question 85

What are pie charts good for showing?

Answer 85

Showing NOMINAL results in a TARGET POPULATION.

Question 86

In an experiment, what axis do the IV and Dv go on?

Answer 86

In an experiment, the INDEPENDENT VARIABLE (variable that is being manipulated) would go along the X AXIS. The DEPENDENT VARIABLE (what is being measured and the scale) would form the Y axis.

Question 87

When choosing a graph, what two things do researchers need to consider?

Answer 87

-Consider the level of data. AND -Whether you are looking at a study of difference or relationship.

Question 88

When should researchers use a bar chart? (Clue: Looking for a DIFFERENCE between groups? What is the type of data?)

Answer 88

-When looking for a DIFFERENCE between 'groups' (i.e. categories). -When the labels are discrete (unrelated) and categorical --> NOMINAL DATA.

Question 89

What THREE things does construction of a bar chart require?

Answer 89

-A TITLE containing FULLY OPERATIONALISED VARIABLES. (i.e. 'A graph to show...') -Both axes need to be LABELLED AND FULLY OPERATIONALISED (e.g. number of/ measured in seconds). -Bars are NOT touching (as data is not related).

Question 90

When should researchers use a scattergram?

Answer 90

-When looking for a RELATIONSHIP between co-variables (no IV or DV) - CORRELATIONAL DATA. -Data must be at least ORDINAL – both axes (can be interval / ratio too). - NUMBERS ON THE AXES. NOTE: It is NOT possible to correlate nominal (categoric) data.

Question 91

What THREE things does construction of a scatter gram require?

Answer 91

-Title (variables fully operationalised). -X and Y axis must be LABELLED and FULLY OPERATIONALISED (units of measurement). -Both axes made up of numbers (no categories).

Question 92

What is a correlation coefficient?

Answer 92

-A correlation coefficient is a number between -1 and 1 that measures the STRENGTH OF A LINEAR ASSOCIATION BETWEEN TWO VARIABLES. -A value of 0 indicates that there is no association between the two variables. -A value greater than 0 indicates a positive association and a value less than 0 indicates a negative association.

Question 93

The stronger the association of the two variables, the closer the correlation will be to?

Answer 93

-The STRONGER the ASSOCIATION of the two variables, the CLOSER the correlation coefficient will be to either +1 or -1 depending on WHETHER THE RELATIONSHIP IS POSITIVE OR NEGATIVE, RESPECTIVELY. -Achieving a value of +1 or -1 means that all your data points are included on the line of best fit ---> there are no data points that show any variation away from this line. The closer the value of r to 0 the greater the variation around the line of best fit.  With real data, you would not expect to get values of r of exactly -1, 0, or 1.

Question 94

When should researchers use a histogram?

Answer 94

-Histograms are a type of graph used for continuous data (e.g. age, height, IQ score).

Question 95

How should a histogram be presented?

Answer 95

-There should be no space between the bars, because the data is continuous (e.g. 1-9, 10-19, 20-29, etc.) -Continuous scores are placed along the x axis (horizontal) and the frequency of these scores along the y axis (vertical). (e.g. memory scores on a test, order of who scored worst to best).

Question 96

Give an example of ONE study in psychology where a histogram would be appropriate to present data?

Answer 96

Milgram's study --> How many people went to the full 450 Volts in intervals of 15 Volts.

Question 97

What do results tables do?

Answer 97

Results tables SUMMARISE THE MAIN FINDINGS OF DATA. Therefore, results table differ from data tables which present raw, unprocessed scores (pre stats analysis). e.g. Informs us of TOTAL SCORES, MEAN, RANGE AND NUMBER OF PPTS.

Question 98

What are pie charts used to show? (the .... of catergories?)

Answer 98

Pie charts are used to show THE FREQUENCY OF CATEGORIES. The chart is split into sections, each one of which represents the frequency of a category. The sections are often colour coded.

Question 99

What are the TWO types of distributions in psychology?

Answer 99

-Normal Distributions -Skewed Distributions

Question 100

How can researchers tell if a skew is POSITIVE OR NEGATIVE? (Simplest terms),

Answer 100

Positive Skew = The Mean is HIGHER than the median and the mode. Negative Skew = The mean is LOWER than the median and the mode.

Question 101

What characterises a 'normal distribution'?

Answer 101

A normal distribution is a PROBABILITY DISTRUBITION THAT IS SYMMETRIC ABOUT THE MEAN, showing that data near the mean are MORE FREQUENT IN OCCURENCE THAN DATA FAR FROM THE MEAN. -In graph form, normal distribution will appear as a bell curve.

Question 102

What are the properties of a NORMAL DISTRIBUTION?

Answer 102

The normal distribution has the following properties: -The mean, median and mode all occur at the same point, and have the same value (at the highest point, in the middle). -The mean, mode and median scores should be very similar. -It is bell shaped and has the same shape either side of the mean — the pattern of scores is exactly the same above the mean as it is below. -Mode - Highest/ midpoint. The highest point in a histogram is the most frequent score --> Always at the top of the curve. -Median - Highest/midpoint - An equal number of scores on each side (symmetrical). The curve is symmetrical, you will have 50% of scores on each side of the highest point. -Mean - Highest/midpoint - An equal number of outlier scores on each side --> the extreme scores of each side balance out, keeping the mean in the centre.

Question 103

Characteristics of normal distributions: When data is normally distributed what is the standard deviation? (%s)

Answer 103

When data is normally distributed... -68% of scores in the data set fall WITHIN ONE STANDARD DEVIATION OF THE MEAN. -95% OF SCORES ARE WITHIN TWO STANDARD DEVIATIONS OF THE MEAN.

Question 104

Other than IQ, what other behaviours or characteristics are likely to be normally distributed?

Answer 104

-Height + Weight of the population on average.

Question 105

Explain the characteristics of a POSITIVE skew?

Answer 105

Characteristics of a SKEWED DISTRUBTION: --The Mean is HIGHER than the median and the mode. -Data more towards the lower side. -The distribution of scores is ASYMMETRIC. -Most of the scores are on one side, with long skews (tails) on the opposite side to the majority of scores. -More scores at the LOWER END of the graph, outliers at the HIGHER end. -If the long tail on the graph is pointing to the RIGHT, THEN IT IS A POISITIVE SKEW.

Question 106

Explain the characteristics of a NEGATIVE skew?

Answer 106

-The mean is LOWER than the median and the mode. -The distribution of scores is ASSYMETRIC --> Most of the scores are on one side, with long skews (tails) on the opposite side to the majority of scores. -If the tail points to the LEFT, IT IS A NEGATIVE SKEW. -More scores at the higher end of the graph, outliers at the lower end.

Question 107

How could researchers limit the chances of getting a skewed distribution?

Answer 107

By NOT CONSIDERING outliers/ anomalous scores in their studies as they may not be representative of the average and may instead give the researcher an inaccurate and unrepresentative mean.