Using statistics to describe

Question 1

define concepts

Answer 1

the nouns that our research is focusing on e.g. social disadvantage, educational attainment, student satisfaction etc

Question 2

define measures

Answer 2

the techniques we use to capture numeric information about the concepts that we are interested in e.g. the questions asked in an interview, the items that are included in an observation etc

Question 3

define variables

Answer 3

the place where we store the numeric data that has resulted from the use of our measures (e.g. in SPSS) e.g. participant ID, year of education, percentage in test etc

Question 4

after designing a quantitative research project what are the next 5 steps?

Answer 4

1. Collect our numeric data using measures in measurement
   
   2. Store this numeric data within variables in a suitable programme e.g. SPSS
   3. Describe the data that is stored within these variables using descriptive statistics - this includes how much ‘missing’ data there is for each variable
   4. Make inferences and draw conclusions about a broader population from the sample of participants who have taken part in the research using inferential statistics
   5. Report research using the descriptions of the data and the inferences that have been made

Question 5

what is the difference between descriptive statistics and inferential statistics

Answer 5

descriptive statistics is used to describe the data stored whereas inferential statistics is used to make inferences and draw conclusions

Question 6

Structure of a typical research report in quantitative educational research:

Answer 6

Example Headings and Sub-headings
- Literature Review
- Method
* Research design
* Sample
* Measures
* Analytic approach
- Results
* Research question
* Research question
- Discussion and conclusion
- References

Question 7

Examples of descriptive statistics

Answer 7

• Averages/ measures of central tendency
  
  • Measures of dispersion/ measurements of spread - how close data is to the average
  • Frequencies - how often each response is given
  • Proportions - from 0 to 1
  • Percentages - from 0 to 100

Question 8

what are the three measures of central tendency and definitions?

Answer 8

mode - most frequent value in a set of data values of our variable
medium - the middle number in a variable when ranked data from lowest to highest
mean - all numbers in a variable added up and divided by how many participants contributed these numbers

Question 9

limitations of measures of central tendency

Answer 9

not useful for describing data on its own as it only provides a single number to describe everyone
people may be really close or really far from that value
has no range

Question 10

which measures of spread accompany the measures of central tendencies

Answer 10

mode - has no accompanying measures of spread
median - the inter-quartile range
mean - standard deviation

Question 11

what are the measures of spread?

Answer 11

inter-quartile range - difference between Q3 and Q2
standard deviation - how close participants numbers are to the mean

Question 12

the median and IQR

Answer 12

Q1 - Quartile 1
Q2 - Median
Q3 - Quartile 3
IQR = Q3-Q1

Question 13

how is the median and IQR illustrated?

Answer 13

using boxplots

Question 14

the mean and standard deviation

Answer 14

a smaller SD means that participants’ numbers are closer to the mean on average
a larger SD means that participants’ numbers are further away from the mean on average

Question 15

how is the mean and SD illustrated?

Answer 15

histograms

Question 16

which descriptive statistics should be used for each level of measurement?

Answer 16

for categorical/nominal variables: only the mode holds meaning
for ordinary variables: only the mode and median with IQR have meaning
uses boxplots
for continuous/scale variables: the mode, median and mean with SD have meaning
uses histograms