Organising and interpreting data Flashcards

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1
Q

what are the different types of data

A

primary secondary, qualitative, quanititative, objective and subjective

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2
Q

why do researchers use more than 1 type of data

A

the research findings is more robust (assesses the hypothesis more accurately)

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3
Q

primary data

A

data collected first hand by a researcher e.g experimentation, observation, study

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4
Q

secondary data

A

data sourced from others prior researcg, not directly from the current researcher e.g literature review

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5
Q

quantitative

A

data that is expressed numerically, such as test scores or measurements of weight

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6
Q

examples of collection of quantitative data

A

closed ended surveys, rating scales, multiple choice questions

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7
Q

qualititative data

A

data that is expressed non-numerically, it may be converted into quantitative data using systematic methods and analyses

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8
Q

collection of qualitative data

A

open ended questionaires and interviews

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9
Q

objective data

A

factual data that is observed and measured independently of person opinion, collected using tools that ensure that same results are obtained by different researchers

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10
Q

examples of objective data

A

amount of brain waves per second, milliseconds taken to respond to stimuli

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11
Q

subjective data

A

data that is informed by personal opinion, perception or interpretation, comes from participants own qualitative descriptions and self reports, not same for different researchers

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12
Q

benefits of subjective data

A

provides rich, qualitative, descriptions of person experience

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13
Q

self reports

A

in the form of surveys, questionarries, interviews, is subjective but can be both qualitative and quanititative

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14
Q

why do we process quantitative data

A

to make meaningful comparisons, observations, patterns, summerise organise and describe the raw unprocessed data

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15
Q

outliers

A

values that differ significantly from other values in a data set, as they make the mean a less accurate summary of the average data value

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16
Q

why cant outliers be removed

A

conformation bias

17
Q

when are outliers more likely to occur

A

in data sets with large sample sizees

18
Q

how to calculate percetage

A

given number/total number x 100

19
Q

how to calculate percentage change

A

new percentage-old percentage / old number x 100

20
Q

what are measures of central tendency

A

descriptive statictics that summarise a data set by describing the centre of the districution of the data set with a single value

21
Q

three measures of central tendency

A

mean, median, mode

22
Q

mean

A

average of the data set, calculated by adding up the total of all data values then dividing this total by the number of data values in the set, better when data values are distributed around a ‘centre’

23
Q

when is mean less helpful

A

when data values are widely distributed, in which case that data set is likely to be influenced by extreme values and outliers

24
Q

median

A

the middle value in a data set ordered from lowesr to highest, best used to identify a typical response when the data is not evenly distributed around the centre of outliers

25
Q

mode

A

the most frequently occuring value in a data set, helps researchers to understand the centre of the data set when the mean/median cannot be calculated

26
Q

what are measures of variabilty

A

statictics that summarise and describe the spread and disstribution pf a data set, indicate how widely participants responses vary in a data set

27
Q

what are the two measures of variabilty

A

range, standard deviation

28
Q

range

A

obtained by subtracting the lowest value in a data set from the highest value

29
Q

standard deviation

A

shows how much data deviates from the mean, calculated using a mathematical formula

30
Q

why is standard deviation useful

A

shows the dispersion of data, which provides more detailed information about the true nature of a data set compared to the range, allows comparisons to be made between different data sets based on their dispersions

31
Q

how can data be presented

A

tables, graphing conventions, bar charts, line graphs

32
Q

tables

A

helps researchers organise and summarise their data in a more accessible format, as well as show the variables between certain variables

33
Q

graphs

A

include a title, x,y axis with variables, units of measurements

34
Q

bar chart

A

graph displaying the relationshipp between at least two variables using rectangular bars with length proportional to the values they represnt , width remaiins constant, one axis show categories, other shows frequencies/amounts

35
Q

line graphs

A

graph displaying between at least two variables using a straight lines to connect data points, show changes over time