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Research Methods > Data Handling and Analysis > Flashcards

Data Handling and Analysis Flashcards

1
Q

what is quantitative data?

quantitative and qualitative

A

data that is in numerical form. e.g. reaction time, tally of times behaviour is shown, rating out of 10.

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2
Q

what is qualitative data?

quantitative and qualitative

A

data that is in the form of words. e.g. descriptions of behaviour, feelings or emotions

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3
Q

how can qualitative be turned to quantitative?

quantitative and qualitative

A

operationalise the behaviour (behavioural categories) and then tally.

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4
Q

how is quantitative data collected?

quantitative and qualitative

A

experimental and observational techniques, closed questionnaires.

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5
Q

how is qualitative data collected?

quantitative and qualitative

A

case studies, open question interviews and questionnaires

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6
Q

what are the strengths and limitations of quantitative data?

quantitative and qualitative

A

S: objective so no bias, descriptive statistics allow data t be summarsied in graphs, more reliable
L: lacks depth and detail, only focus on behaviours that can be mathematically tested.

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7
Q

what are the strengths and limitations of qualitative data?

quantitative and qualitative

A

S: rich in depth and detail to provide high understanding + validity
L: subjective so risk of bias, challenging to summarise, less reliable

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8
Q

what is primary data?

primary and secondary data

A

data collected first hand by the researcher, specifically for the purpose of the investigation

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9
Q

what is secondary data?

primary and secondary data

A

data that is collected second hand from already published sources that didn’t initally set out to answer the current research question.

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10
Q

what are the strengths and limitations of primary data?

primary and secondary data

A

S: high validity as data collected to answer Q specifially and researcher has control of collection process.
L: requires time and effort to develop resources, may be costly compared to secondary data which can be easily accessed.

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11
Q

what are the strengths and limitations of secondary data?

primary and secondary data

A

S: already exists so very quick
L: low validity as not collected initially to answr Q and researcher has no control over collection process (variables)

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12
Q

what is a meta analysis?

primary and secondary data

A

collecting and combining the results from previously conducted studies investigating a similar question and analysing them.

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13
Q

what are the strengths and limitations of a meta analysis?

primary and secondary data

A

S: large sample size, more trustworthy as extraneous influence reduced
L: secondary data, choice of which studies are or arent included leads to bias, statistically unimportant studies not included

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14
Q

what are descriptive statistics?

descriptive statistics

A

describe and summarise data collected.

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15
Q

what are the two brances of descriptive statistics?

descriptive statistics

A
  1. measurs of central tendency
  2. measures of dispersion
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16
Q

what is a measure of central tendency?

descriptive statistics

A

a single value that summarises a set of data, identifying a typical value (the average)
- mean
- mode
- median

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17
Q

what is the mean?

descriptive statistics

A

the arthimetic average of a set of data, calculated by adding all values and dividing by number of values.

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18
Q

what are the strengths and limitations of using the mean?

descriptive statistics

A

S: all raw data used so most sensitive measure
L: easily disrorted by extreme scores (outliers)

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19
Q

what is the mode?

descriptive statistics

A

the most frequently occuring value in a set of data.
if 2 - bimodal
if more than 2 - multimodal

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20
Q

what are the strengths and limitations of using the mode?

descriptive statistics

A

S: unaffected by outliers, only way to give average in categories (e.g. pet type)
L: doesn’t take all values into account, unreliable in small data sets (no mode if all values are the same)

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21
Q

what is the median?

descriptive statistics

A

the middle value of a set of scores by putting values into numerical order and selecting middle. if even, halfway between the middle data points.

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22
Q

what are the strengths and limitations of using the median?

descriptive statistics

A

S: easy to calculate, unaffected by extreme outliers.
L: doesn’t include all values in the data set, if even set, the median won’t be a value in the data set at all.

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23
Q

what are measures of dispersion?

descriptive statistics

A

values that summarise the spread of the data.
- range
- standard deviation

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24
Q

what is the range?

descriptive statistics

A

the difference between the highest value and the lowest value in a set of data.

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25
Q

what are the strengths and limitations of using the range?

descriptive statistics

A

S: easy to calculate
L: easily distorted by extreme values, doesn’t represent every value in the data set

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26
Q

what is the standard deviation?

descriptive statistics

A

a measure of how much each score deviates from the mean on average.

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27
Q

what is the equation to calculate standard deviation?

descriptive statistics

A

sum of (value - mean) squared divided by number of values - 1. all square rooted.

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28
Q

what does the calculated standard deviation value mean?

descriptive statistics

A

the larger the SD, the more spread out the data is around the mean -> less representative and less reliable.

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29
Q

what are the strengths and limitations of using the SD?

descriptive statistics

A

S: uses all values so more sensitive measure of spread
L: more difficult to calculate, distorted by extreme results

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30
Q

how are percentages used in psychology?

descriptive statistics

A

https://www.youtube.com/watch?v=TsSWLB0aEWs&list=PLUQ8QDGvbAwhFY-fZkcJ3k4R2NCnZlqB4&index=19

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31
Q

what are the three types of tables that can be used to display data?

displaying quanitative data

A
  1. raw data table
  2. frequency table
  3. descriptive statistics table
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32
Q

what is a raw data table?

displaying quanitative data

A

a record of individual data points.

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33
Q

what is a frequency table?

displaying quanitative data

A

a tally chart, log of the number of times a behavioural category is seen

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34
Q

what is a descriptive statistics table?

displaying quanitative data

A

a table with the measures of central tendencies and measures of dispersion of behavioural categories.

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35
Q

what is a bar chart?

displaying quanitative data

A

a chart that summarises the frequency of a category

36
Q

what type of data does a bar chart display?

displaying quanitative data

A

nominal data (categories). e.g. type of pet

37
Q

what variable goes on which axis on a bar chart?

displaying quanitative data

A

X - category
Y - frequency

38
Q

what does the height of the bar represent in a bar chart?

displaying quanitative data

A

the frequency

39
Q

what is important when drawing a bar chart?

displaying quanitative data

A

LINES DO NOT TOUCH AS DATA IS NOT CONTINUOUS - THATD BE A HISTOGRAM

40
Q

what is a pie chart?

displaying quanitative data

A

a circular graph that represents all the data in the set. each wedge represents the proportion of one category.

41
Q

what type of data is displayed in a pie chart?

displaying quanitative data

A

nominal data -> each category has a ‘wedge’ where its size represents the proportion

42
Q

what does a scattergram display?

displaying quanitative data

A

the relationship between two co-variables. (correlations)

43
Q

what is on the x and y axis of a scattergram?

displaying quanitative data

A

the co variables.
e.g X - attendance
Y- test score

44
Q

what does each point on a scattergram represent?

displaying quanitative data

A

one participant’s measure. e.g. their attendance and their test scores.

45
Q

what can a scattergram indicate?

displaying quanitative data

A

a positive or negative correlation between the co variables.

46
Q

what is a histogram?

displaying quanitative data

A

a graph that displays the frequency of continuous, numerical data.

47
Q

what variable is placed on which axis in a histogram?

displaying quanitative data

A

X - continuous variable e.g. age group, months etc.
Y - frequency

48
Q

what is important when drawing a histogram?

displaying quanitative data

A

THE BARS ARE TOUCHING AS IT IS SHOWING CONTINUOUS NOT CATEGORIAL DATA.

49
Q

what is a line graph used for?

displaying quanitative data

A

to display two sets of continuous data on the same graph.

50
Q

how is a line graph drawn?

displaying quanitative data

A

place an X on the midpoint of the top of each bar and join with a line.

51
Q

what is a line graph also known as?

displaying quanitative data

A

a frequency polygon

52
Q

what variables go on which axis on a line graph?

displaying quanitative data

A

X - continuous variable

53
Q

what graph is used in distributions?

distributions

A

histograms

54
Q

what is a normal distribution

distributions

A

when the frequency distribution forms a symmetrical bell shaped curve

55
Q

what does a normal distribution curve indicate?

distributions

A

more ppts are in the middle, with fewer on either side

56
Q

where is the mean, mode and median found on a normal distribution curve?

distributions

A

the top at the centre of the curve

57
Q

where is the mode found on a normal distribution curve?

distributions

A

the top of the curve as most frequent.

58
Q

where is the median found on a normal distribution curve?

distributions

A

the middle score

59
Q

where is the mean found on a normal distribution curve?

distributions

A

the middle as an equal number of outliers on either side

60
Q

why does the normal distribution never touch the x-axis?

distributions

A

because there will always been an extreme score from at least one person.

61
Q

what is standard deviation like on a normal distribution curve?

distributions

A

68% fall within one SD of the mean, 95% within two SD and 99.7% within three SD.

62
Q

what is a skewed distribution?

distributions

A

when the distribution of the scores are assymetric - most scores on one side.

63
Q

what does a posiitve skew look like?

distributions

A

the ‘tail’ points to the right, with the majority on the left.

64
Q

what does a negative skew look like?

distributions

A

the ‘tail’ points to the left, with the majority on the right.

65
Q

how is data distributed on a positive skew?

distributions

A

more scores at the lower end, outliers at higher end of x axis.

66
Q

how is data distributed on a negative skew?

distributions

A

more scores at the higher end, outliers at the lower end of x axis.

67
Q

where is the mode found on a skewed distribution?

distributions

A

the highest point still

68
Q

where is the median found on a skewed distribution?

distributions

A

at the point where 50% of graph is either side.

69
Q

where is the mean on a skewed distribution?

distributions

A

shifted towards the outlier scores

70
Q

what are the 3 levels of measurement?

levels of measurements

A
  • nominal
  • ordinal
  • interval/ratio
71
Q

what is nominal data?

levels of measurements

A

categorical data that has no natural order - the frequency count of the variable is recorded. e.g. birth country, pets, career choice

72
Q

what are the variables in nominal data?

levels of measurment

A

discrete (non overlapping).

73
Q

what is ordinal data?

levels of measurment

A

categorical data that has a natural order - it can be placed into some kind of order or scale

74
Q

is the difference consistent on ordinal scales?

levels of measurement

A

no

75
Q

what are examples of ordinal data?

levels of measurement

A

positions in a competition, height in a class, choice on a likert scale.

76
Q

what is interval data?

levels of measurement

A

data that is measured in fixed units with equal distance between points on the scale. e.g. temperature, a mm ruler - precise and continuous

77
Q

what is ratio data?

levels of measurement

A

interval data with an absolute zero e.g kelvin scale

78
Q

how do you convert interval to ordinal data?

levels of measurement

A
  1. gather the interval data
  2. list each ppt’s data from highest to lowest
  3. assign a rank
79
Q

how do you convert ordinal to nominal?

levels of measurement

A
  1. create categories (fast and slow)
  2. place highest half into one category and lowest into the other.
80
Q

how do you perform content analysis?

content analysis and coding

A
  1. decide on a research question
  2. select a sample from all possible content
  3. coding : decide on categories (coding units) to be recorded
  4. work through data - tally number of times category appears
  5. data analysis
81
Q

what is a coding unit in content analysis?

content analysis and coding

A

the operationalised categories decided beforehand that will be looked for in the content analysis

82
Q

how do you test for reliabaility of a content analysis?

content analysis and coding

A
  1. test-retest reliability: run the analysis again on the same sample and compare
  2. inter-rater reliability: a second investiator repeats the content analysis and compare
83
Q

how do you test the consistency between the original CA and the repeats?

content analysis and coding

A

correlation study e.g. spearman’s rho.
0.8 or higher is accepted as reliable

84
Q

what are the strengths and limitations of content analysis?

content analysis and coding

A

S: high external validity (generalisable), easy to gather sample
L: possible observer bias, the ‘artefacts’ are written for purposes other than the research so may lack validity

85
Q

what is thematic analysis?

content analysis and coding

A

when researchers read the text first and then allow themes to emerge. not predetermined

86
Q

how do you perform a thematic analysis?

content analysis and coding

A
  1. gather sample of texts
  2. read texts and spot patterns that can be coded
  3. re-read and look for the themes.
87
Q

what are the strengths and limitations of thematic analysis?

content analysis and coding

A

S: stops observer bias as theories come after theme discovery, same as content analysis
L: same as content analysis

Research Methods (5 decks)

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