Organising and interpreting data

Question 1

what are the different types of data

Answer 1

primary secondary, qualitative, quanititative, objective and subjective

Question 2

why do researchers use more than 1 type of data

Answer 2

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

Question 3

primary data

Answer 3

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

Question 4

secondary data

Answer 4

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

Question 5

quantitative

Answer 5

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

Question 6

examples of collection of quantitative data

Answer 6

closed ended surveys, rating scales, multiple choice questions

Question 7

qualititative data

Answer 7

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

Question 8

collection of qualitative data

Answer 8

open ended questionaires and interviews

Question 9

objective data

Answer 9

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

Question 10

examples of objective data

Answer 10

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

Question 11

subjective data

Answer 11

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

Question 12

benefits of subjective data

Answer 12

provides rich, qualitative, descriptions of person experience

Question 13

self reports

Answer 13

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

Question 14

why do we process quantitative data

Answer 14

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

Question 15

outliers

Answer 15

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

Question 16

why cant outliers be removed

Answer 16

conformation bias

Question 17

when are outliers more likely to occur

Answer 17

in data sets with large sample sizees

Question 18

how to calculate percetage

Answer 18

given number/total number x 100

Question 19

how to calculate percentage change

Answer 19

new percentage-old percentage / old number x 100

Question 20

what are measures of central tendency

Answer 20

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

Question 21

three measures of central tendency

Answer 21

mean, median, mode

Question 22

mean

Answer 22

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’

Question 23

when is mean less helpful

Answer 23

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

Question 24

median

Answer 24

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

Question 25

mode

Answer 25

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

Question 26

what are measures of variabilty

Answer 26

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

Question 27

what are the two measures of variabilty

Answer 27

range, standard deviation

Question 28

range

Answer 28

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

Question 29

standard deviation

Answer 29

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

Question 30

why is standard deviation useful

Answer 30

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

Question 31

how can data be presented

Answer 31

tables, graphing conventions, bar charts, line graphs

Question 32

tables

Answer 32

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

Question 33

graphs

Answer 33

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

Question 34

bar chart

Answer 34

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

Question 35

line graphs

Answer 35

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