15. Data Collection

Question 1

Qualitative Data P.227

Answer 1

Data based on descriptive information. Usually collected in a free-form manner.

Question 2

Quantitative Data P.228

Answer 2

Data that can be measured, verified, and manipulated, also known as numerical data.

Question 3

Discrete Data P.228

Answer 3

Count data and are sometime called categorical/attribute data.

Question 4

Continuous Data P.228

Answer 4

Exist on an interval, or on several intervals. Variable data that can be transformed into attribute data, but the reverse is not true.

Question 5

Measurement Scales P.229

Answer 5

• Nominal
• Ordinal
• Interval
• Ratio

Question 6

Nominal Scales P.229

Answer 6

Classify data into categories with no order implied.

Question 7

Ordinal Scales P.229

Answer 7

Refer to positions in a series, when order is important but precise differences between values aren’t defined. Bright, Brighter, Brightest.

Sometimes collected as discrete data but manipulated as continuous data and analyzed with parametric tests.

Question 8

Interval Scales P.230

Answer 8

Scales have meaningful differences but no absolute zero, so ratios aren’t useful.

Question 9

Ratio Scales P.230

Answer 9

Scales have meaningful differences, and an absolute zero exists.

Question 10

Sampling concepts P.231

Answer 10

• Representative sample (random drawn from population)
• Homogeneity
• Bias (1. less likely to be included than other, 2. nonrandom collection)
• Accuracy (how close the sample statistic to population)
• Precision (how close estimates from different sames are to each other)
• Margin of error (max. expected difference between the sample estimate to population)
• Sampling frame (complete list of the members in the target population)
• Strata (mutually exclusive segment)

Question 11

Type of sampling P.232

Answer 11

• Random
• Stratified
• Systematic
• Block

Question 12

Random sampling P.233

Answer 12

Equal probability of being chosen. Selected independently of every other member.

Question 13

Stratified sampling P.233

Answer 13

Members are assigned to a unique stratum that are mutually exclusive and collectively exhaustive. Stratification variables should create a heterogeneous set of strata.

Question 14

Systematic sampling P.234

Answer 14

Sampling from an ordered population at a specified sampling interval, i. (interval sampling)

i = N/n, N (population) n (sample size)
k, k+i, k+2i…, k+(n-1)i.

Question 15

Block sampling P.235

Answer 15

Non-probability sampling or judgement block sampling. The balance of a defined block are automatically chosen.

Question 16

Factors influence Sample Size P.236

Answer 16

• Precision and accuracy required
• Resources available
• Nature of the study (time, geographical)
• Sampling design adopted
• Size and characteristics of the populaion
• Confidence and power required
• Methods of obtaining the sample

Question 17

Determining sample size P.236

Answer 17

-As many as possible
-For correlational, experimental design, and causal-comparative studies: 30 per group.
<100: sample entire population
~500: sample 50% population
~1500: sample 20% population
>5000: sample 400 of the population

Question 18

Sample size calculation when population is a known qty P. 237

Answer 18

n= N / 1+Ne^2

N= Population
n= Sample size
e= Margin of error 1/ √n

Question 19

Good sampling design P.237

Answer 19

• Samples may be drawn randomly
• Sample is representative of the population
• Design is economical and efficient
• Sufficiently easy to conduct
• Standard error is minimal
• Bias is minimal
• Statistical assumptions are not violated
• Inferences can be made from the sample with the desired level of confidence
• Selection criteria are objective.

Question 20

Data Collection: Operational Definition P.239

Answer 20

1. A clear, concise, and unambiguous statement that provides a unified understanding of the data for all involved before the data are collected or the metric is developed.
2. Defined the formula that will be used and each term used in the formula.
3. Provides an interpretation of the metric, such as up is good.

Question 21

Common cause of poor data accuracy P.241

Answer 21

• Multiple points of entry
• Limited use of data validation
• Batching input versus real-time input/ synchronization
• Multiple means of data correction access to upstream systems
• Unclear directions
• Lack of training
• Ambiguous terminology
• Manual versus automated means of entry
• Rounding
• Order of calculations
• Calculation errors
• Inadequate measurement systems
• Units of measure not defined
• Failing to use the proper measurement system
• Field inconsistencies/ truncation
• Similarities of characters (0/O)
• Copying errors

Question 22

Useful data collection techniques P.243

Answer 22

• Walk the process first
• Make the data collection process simple
• Define collection points
• Use check sheets and checklists
• Bridge computer gaps
• Minimize “other” (category)
• Limit options
• Establish the rules
• Address timing and sequencing
• Time-stamp data
• Red-Tag a unit of product or service
• Chart data accuracy

Question 23

Common data collection points

Answer 23

• End of process
• In process
• Points of convergence
• Points of divergence
• Across functional boundaries (hand-off points)

Question 24

Data cleaning P.245

Answer 24

The process of detecting and correction and possibly removing inaccurate data form a set of data (usually through human means).

Question 25

Coding the data P.256

Answer 25

• Helpful to code data to simplify the recording process

- Sometimes useful to code data by using an algebraic transformation

Question 26

Collecting seasonal data P.247

Answer 26

-Understand the nuances of the data before undertaking a major data collection effort.

Question 27

Data collection strategies P.249

Answer 27

1. Observational studies
2. Monitoring techniques
3. Process capability
4. Measurement assessment
5. Sampling
6. Design of experiments
7. Complementary data and information