Methodologies

Question 1

Quasi experiments

Answer 1

research method where the IV changes naturally and not manipulated by a researcher. The DV is measured.
Description -The main features of experiments are:
- Involves researching a naturally changing IV
- The effect on the DV of a changing the IV is measured
- Can involve a natural experiment (natural setting) or difference study (natural or controlled setting)
- The researcher has no control over allocating participants to IV groups.
E.g. Raine et al investigated the defences in brain activity of murderers compared to non-murderers

Question 2

Independent groups

Answer 2

• participants take part in one condition only . Each condition has different groups of participants.
  Adv- less chance of order effects
• reduces demand characteristics
• can be used when repeated measures design is inappropriate
  Dis- participant variables may affect the dv as there are separate groups of participants in each condition
• more participants are needed as 2 different groups are required

Question 3

Repeated measures

Answer 3

• all participants take part in all conditions of the experiments
  Adv - participant variables between participants are removed
• requires fewer participants as the same participants take part in all conditions

Dis - increase chance of demand characteristics
- order effects may occur
- inappropriate to use for a quasi experiment

Question 4

Matched pair designs

Answer 4

• participants are matched on important characteristics eg age or IQ . Each member of the pair is then put in each condition

Adv- controls the participant variables of participants
- no order effects
Dis- very time consuming to match up and find similar participants
- difficult to completey match participants on important characteristics therefore participant variables may affect still may have an effect

Question 5

Quantitative data - interval

Answer 5

• Measurements are on a standardised scale of fixed units separated by equal distances
• Standardised personality and IQ scales are generally accepted as having equal distances between the units, but are sometimes referred to as ‘plastic’ scales as we cannot know this for certain
• Values can be positive or negative, e.g., temperature
• Although the scale is of equal intervals, the intervals are not always meaningful, e.g., room temperature increasing by 5 degrees is more likely to be noticed if the starting temperature is -3 degrees rather than 33 degrees

Question 6

Quantitive data - ordinal

Answer 6

Data is placed in rank order which allows meaningful comparison
• We can make statements about the relative magnitude (size) of
nominal
• Possible to state one value is higher than another, but not to assume more than this, e.g., if people are placed in height order and given a rank of 1-10, we cannot say the tallest person is twice as tall as the person ranked fifth
Data gathered on unstandardized, invented scales, e.g., attitude scales or responses to Likert-style questions should be treated at this level

Question 7

Quantitive data - ratio

Answer 7

• Strongest level of measurement
° Like interval scales, but measurement starts a genuine zero point.-absolute zero
• The data is represented physical quantities such as time, weight, pressure, length, wages
We can say someone who weighs 88kg is twice the weight of a person weighing 44 kg or that someone who completes a reaction time task in 32 seconds did it in half the time of someone taking 64 seconds

Question 8

Quantitive data - nominal

Answer 8

• Weakest, most basic level of measurement
• Also referred to as ‘categorized’ or ‘frequency’ data
• People and objects are classed together on the basis of common features
• These categories are mutually exclusive: it is only possible to belong to one category
• Named categories can be given arbitrary numbers, labels or codes

Question 9

Quantitative data

Answer 9

Quantitative research gathers data in a numerical form which can be put into
units of measurement. This type of data can be categories, or in rank order, or measured in
used to construct graphs and tables of raw data

Question 10

Quantitive data examples

Answer 10

Data collected in experiments
Closed answer questionnaires
Observations - tallies on behaviour checklists
Content analysis - number of occurrences of categories.

Question 11

Quantitative data evaluation 6 marks

Answer 11

Easier to analyse numerical measurements compared to descriptive qualitative data and make comparisons - this makes it easier to draw conclusions
+ Numerical data is easier to collect from a large group of participants than descriptive qualitative data. You can therefore collect more data in the time frame or it takes less time to collect the data.

May lack validity as numbers may over simplify reality, e.g. closed answer questionnaires using rating scales may not represent participants’ true feelings - therefore conclusions may be meaningless.
- Some aspects of human thought and behaviour may be difficult to operationalise in a numerically measurable form, e.g. feelings towards parents.

Question 12

Qualitative data

Answer 12

Data which is descriptive rather than quantifie or counted, therefore it is observed or reporte rather than measured.

Question 13

Qualitative data examples

Answer 13

Case studies - e.g. Bowlby’s 44 Juvenile Thieves study Open answer questionnaires
Observations - description of behaviours observed, e.g. Milgram’s description of the distress participants showed when administering the shocks.
Content analysis - converts qualitative data into quantitative data

Question 14

Qualitative data 6 mark evaluation

Answer 14

+ May have high validity as qualitative data provides detailed information which provides insights into participants’ true thoughts or behaviour, therefore meaningful conclusions can be made
+ Unlike quantitative data, qualitative data represent all aspects of human thought and behaviour as it is descriptive, e.g. feelings towards parents.

Difficult to make comparisons across different groups or participants compared to numerical quantitative data as it is not uniformed and often very complex, therefore it is difficult to analyse data.
- Reduces access to a greater amount of data, as it is harder and more time consuming to collect descriptive data from large groups rather than numerical quantitative data.

Question 15

Primary data

Answer 15

Data which is collected or observed directly by the researcher/s first hand from participants which is specifically for the purposes of the research study, using questionnaires, interviews, experiments etc. for their research.

Question 16

Primary data examples

Answer 16

Data collected in experiments - e.g. Loftus and Palmer
Responses to questionnaires
- Observations conducted by researchers
Interviews carried out by researcher

Question 17

Primary data evaluation 6 marks

Answer 17

The researcher controls the methods and tools
- As the researcher designs the study and collects
used to collect the data; therefore, they can
the data themselves it can be more time
ensure they specifically test the hypothesis of
consuming than using secondary data.
their study.
+ As the researcher designs the methods used to collect the data, including controlling extraneous variables etc., they can ensure internal validity.
- Can be difficult, impractical or unethical to collect large sets of data for some behaviours, e.g. national crime rates or number of mental health diagnoses across different countries.

Question 18

Secondary data

Answer 18

Data collected indirectly by someone other than the researcher of the study whose purpose was for something other than the aims set by the researcher.

Question 19

Secondary data examples

Answer 19

Government statistics
Meta-analysis - combining data from several different studies
Literature review -e.g. Myers & Diener (1995)
study
- Artefacts for content analvsis

Question 20

Secondary data 6 mark evaluation

Answer 20

+ Quicker than collecting primary data as the researcher has not designed the study and collected the data themselves.
+ Can analyse data which might be impractical or unethical to collect using primary sources such as large sets of data for some behaviours like national crime rates or number of mental health diagnoses across different countries.

As the researcher does not control the design of the methods used to collect the data, including controlling extraneous variables etc., they cannot ensure the internal validity.
- The researcher does not control the methods and tools used to collect the data, therefore the data may not fully match the aims and hypothesis of their study.

Question 21

Measures of central tendency: mean

Answer 21

How’s it calculated -To calculate this, you add up all numbers in a data set and then divide this by the number of numbers
Advs -Appropriate to use for further statistical analysis such as a standard deviation.
+
Appropriate to use for ordinal, interval and ratio levels of data.
Dis - - It is affected by extreme scores ( or low) and can
the exam you r
misrepresent the
, andard deviatic
numbers as a resur
Misleading as it ma, produce a value tha no participant in the data set achieved.

Question 22

Measures of central tendency : mode

Answer 22

-This is calculated by counting which score occurs the most and can generate more than one modal score if there are the same number of scores for two or more numbers.
Adv- Not biased by extreme
scores
+ It can be used with nominal (categorical) data, and that it provides information about frequency.
+ Unlike the mean and median there is always a modal score in the set of data, even if it is bimodal.
Dis-It ignores values by only looking at the frequency of numbers this may lead to a biased representation as an outlier score maybe the most frequent.
- Can be unclear as the data may have several
modes (bi-modal = 2+
modes)

Question 23

Measures of central tendency - median

Answer 23

-This is calculated by arranging scores in order and finding the mid-point
Advs-Makes use of all the values but is not as biased by extreme scores as using the mean.
+ It can use with ordinal data
Dis- More open to bias from extreme scores than the mode.
- Unhelpful for further statistical analysis such as using SD.

Question 24

Measures of central tendency- range

Answer 24

-A measure of the spread of scores, shown by the difference between the highest and the lowest value
Advs-It’s easier to calculate than standard deviation
+ Takes into account the complete breath of values because it uses the highest and lowest scores, unlike SD.
Dis– It is affected by extreme values;
- It does not give info on whether the scores are clustered around the mean or spread out.

Question 25

Standard deviation

Answer 25

-This is the measure of the spread of data around the mean. The higher the value the more variation in your scores, Adv-It gives a more precise and informative measure of dispersion than the range, as it reflects every score in the data set rather than just the highest and lowest scores. Less affected by anomalous results than range as it gives you an indication of how close the majority of the scores are to the mean, so extreme scores have less Impact. Dis-Cannot use with nominal data and restricted to ordinal data or above Cannot be used when the data set is positively or negatively skewed, restricted to normal distribution

Question 26

How to calculate standard deviation

Answer 26

1-catulate the mean 2-Subtract the mean from each score 3- square difference 4-find sum of square values 5-divide sum of squared values by the number of scores (n-1) 6- find square root