Chapter 14 - Data collection

Question 1

Problem solving cycle - Stage 1 Problem specification and analysis

Answer 1

• make the goal of the investigation clear
• plan how to achieve goal
• what data is needed, how it is going to be collected, what will be done with the data

Question 2

Problem solving cycle - Stage 2 Information collection

Answer 2

• taking a sample from all possible data (parent population)
• can collect data from whole population (100% sample is called a census)

Question 3

Problem solving cycle - Stage 3 Processing and representation

Answer 3

• cleaning the data (dealing with outliers, missing data and errors)
• presenting the data in a diagram that shows its main features
• calculate summary measures e.g. central tendency (mean, standard deviation), spread (range)

Question 4

Problem solving cycle - Stage 4 Interpretation

Answer 4

• draw conclusions about investigation
• explain what the calculations tell you, whether the hypothesis fits in
• report observations in words
• ask yourself if findings are realistic, if it answers the question; if not, you have to collect more or different data

Question 5

Sample

Answer 5

• provides a set of data values of a random variable

Question 6

Parent population

Answer 6

• overarching set can be finite e.g. all professional golf players or infinite e.g. points where a dart can land on a dart board
• Greek letters are used as parameters (measured quantity of a statistical population that summarises or describes an aspect of the population) e.g. μ is the mean
• a sample is representative of the population

Question 7

Sampling frame

Answer 7

• a list of the items available to be sampled e.g. sheep in a flock, but some things e.g. cod in the North Atlantic has no frame

Question 8

Sampling fraction

Answer 8

• the proportion of the available items that are actually sampled

Question 9

Sampling error

Answer 9

• difference between an estimate of a parameter derived from the sample set and the true value
• to reduce sampling error, the sample needs to be as representative as possible of the population

Question 10

Bias

Answer 10

• systematic error e.g. using a sample of hockey players to estimate time young women take to run 100m (hockey players are fitter than general population)

Question 11

Sampling techniques - Simple random sampling

Answer 11

• every member of the parent population is likely to be selected
• needs sampling frame
• done using random number generator

Question 12

Sampling techniques - Stratified sampling

Answer 12

• identify strata (sub-groups) which give different patterns
• ensure all strata are sampled
• proportional stratified sampling - proportional to the size of their populations in the parent population
• selection from each stratum is chosen using simple random sampling

Question 13

Sampling techniques - Cluster sampling

Answer 13

• items are chosen from one or several of the strata (now called clusters)
• each cluster should be reasonable representative of the parent population e.g. small cluster of puffin on an island are representative of the entire North European population

Question 14

Sampling techniques - Systematic sampling

Answer 14

• choosing individuals from a sampling frame at certain intervals
• cyclic patterns could lead to the drawing of incorrect conclusions

Question 15

Sampling techniques - Quota sampling

Answer 15

• choosing individuals for a sample depending on a quota e.g. how many males and females
• non-random version of stratified sampling

Question 16

Sampling techniques - Opportunity sampling

Answer 16

• circumstances make a sample readily available
• can result in bias - interviewing delegates of a hospital conference about what they think of doctors
• useful where research will lead to individual case studies rather than results which are applied to the whole population

Question 17

Sampling techniques - Self-selecting sample

Answer 17

• those involved volunteer to take part (given the choice to participate or decline) e.g. online survey
• can result in bias - someone who refuses to answer some of the questions
• not used in serious research