different ways to analyse data, study's, etc

Question 1

meta analysis

Answer 1

-method of analysis data which produces an effect size
-examines data from a number of independent studies in the same subject to determine overall trends

Question 2

effect size

Answer 2

quantitative measure of studies effect

Question 3

case study

Answer 3

the detailed study of a single individual, institution or event using info from a range of sources e.g. family friends or person concerned

Question 4

longitudinal (case studies)

Answer 4

follow group over extended amount of time

Question 5

what observation is content analysis

Answer 5

indirect, observing the individual from the artifacts they produce e.g songs, books, paintings

Question 6

meta analysis strengths

Answer 6

-reviewing from range of studies increases validity
-reduces contrast in studies by producing statistics

Question 7

meta analysis weaknesses

Answer 7

-research designs in diff studies may vary meaning u cant truly compare them
-so arent always valid

Question 8

case study strengths

Answer 8

-rich in depth data
-overlooked data likely to be identified
-used incases where experiments arent ethical e.g how respond to certain events

Question 9

case study weaknesses

Answer 9

-difficult to generalise data
-as it is identified after the event we cannot be sure the apparent changed weren’t present originally

Question 10

content analysis strengths

Answer 10

-based on what people actually do, real communications that are current and relevant
-high ecological validity
-when sources are obtained, findings can be replicated

Question 11

content analysis weaknesses

Answer 11

• observer bias may reduce objectivity and validity of findings
  -diff observers interpret the meaning of behavioural categories differently (e.g anger)
  -lack internal validity

Question 12

types of extraneous variables

Answer 12

demand characteristics, investigator effects, situational variables, participant variables

Question 13

demand characteristics

Answer 13

if participant knows/guesses the experiment and changes their behaviour

Question 14

investigator effects

Answer 14

any aspect of the researcher’s behaviour, appearance or gender that could affect participant responses

Question 15

situational variables

Answer 15

features of a research situation that may influence participants behaviour e.g. order effects, heat, time of day

Question 16

participant variables

Answer 16

differences between participants (e.g. IQ, age)

Question 17

confounding variable (not extraneous)

Answer 17

variables that interfere with the effect of the IV and the DV

Question 18

extraneous variable

Answer 18

variable that only effects the DV

Question 19

content analysis

Answer 19

a method used to analyse qualitative data

Question 20

benefits of content analysis

Answer 20

-high ecological validity
-high mundane realism
-analysis can be repeated so reliable

Question 21

weakness of content analysis

Answer 21

-big culture bias and interpretation of verbal or written content affected by language and culture of observer
-observer bias- affects objectivity and validity

Question 22

how to deal with validity in content analyses

Answer 22

researcher needs to ensure sample is representative
-use a double blind technique

Question 23

how to deal with issues of reliability

Answer 23

-test retest (another researcher retests analysis)
-inter observer reliability (two or more observe same artefacts)
-training observers in use of coding system through practice

Question 24

what average goes with nominal data

Answer 24

mode

Question 25

Nominal data

Answer 25

categorical data- discrete and mutually exclusive

Question 26

Ordinal data

Answer 26

ordered in some way but the intervals aren’t known/ not equal, lack of objectivity as its based on how you rate it

Question 27

what average is used with ordinal data

Answer 27

the median

Question 28

interval data

Answer 28

similar to ordinal data but we know the size of the difference e.g. Time in a race- objective and scientific

Question 29

average used with interval data

Answer 29

the mean

Question 30

similar to interval but has clear definition of 0- when 0 means none of that data e.g. temp

Answer 30

ratio data

Question 31

limitation of nominal

Answer 31

overly simplistic- no measure of dispersion (spread of data)

Question 32

limitation of ordinal data

Answer 32

intervals aren’t equal- an average cannot be used as a measure of central tendency

Question 33

limitation of interval data

Answer 33

intervals are arbitrary- e.g. 100c is not 2x 50c

arbitary-

based on random choice or personal whim, rather than any reason or system.

Question 34

strength of quantitative data

Answer 34

-easy to analyse statistically
-more objective

Question 35

disadvantage of quantitative data

Answer 35

-lacks representativeness since its generated from closed questions answers which are narrow
-lack meaning and context
-not representation of true life, lack validity

Question 36

strength of qualitative data

Answer 36

-rich detail
-can develop answers so high external validity

Question 37

limitation of qualitative data

Answer 37

-subjective
-interpretations of data can rely on opinions- bias on conclusions drawn

Question 38

strength of primary data

Answer 38

-authenticity/ specificity
-data generated will fit aims of experiment, reduce time wasted on checking the data is relevent

Question 39

weakness of primary data

Answer 39

-designing and carrying out psychological study can take a lot of time and effort
-equipment needs purchasing so expensive

Question 40

strength of secondary data

Answer 40

-less time consuming
-easier