# HLTB15H3 Lecture Notes - Lecture 7: Sampling Frame, Population Study, Sample Size Determination

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20 Mar 2013
School
Department
Course
Professor
Cue Column:
Note-Taking Area:
Adv: saves \$ & HR & time
Loses accuracy
Based on assumption
Disadv: only find about sample, and estimate for population
Keep going while you keep finding
Basically continue sampling as long as you make new
findings
Based on subjective judgments
When no new findings, it's called saturation point
Analysis done on saturation point
In qualitative research
Concept of sampling
Group of individuals/units etc.
Shares same characteristics
Population / study sample (N)
Subset of population
Sample
# of individuals used to draw info
Representatives of population
Sample size (n)
Random, non-random, mixed designs
Sampling design/strategy
Unit is an individual
Unit of analysis
Sampling unit/sampling element
Every indi/unit in study sample
Where can sample be drawn
ID individuals
Sampling frame
Statistics ex mean, etc. from sample
Sample stats
# inferred from stats
Estimate coming from sample stats
Predict certain population mean
Population parameters/ population mean
Terminology
*true population mean - the true mean of entire
population
The difference is in relation to sample units
There is going to be diff between sample stats and population
mean
As sample size increases, more accuracy between sample
stats and true pop mean
Ex. Really young and really old in class, it's going to be
so off
The greater diff in variable, greater diff between sample stats
and pop mean
Principles of sampling
Need to be precise
It still can occur though
Sampling is non-random
Doesn't cover accurately
Sampling frame
Can occur if:
Minimize bias
Aims
Guiding principles
Undertaking research - sampling methods
HLTB15 Lecture 7
Thursday, March 14, 2013
12:16 PM
HLTB15 Page 1
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## Document Summary

Disadv: only find about sample, and estimate for population. Basically continue sampling as long as you make new findings. When no new findings, it"s called saturation point. There is going to be diff between sample stats and population mean. *true population mean - the true mean of entire population. The difference is in relation to sample units. As sample size increases, more accuracy between sample stats and true pop mean. The greater diff in variable, greater diff between sample stats and pop mean. Really young and really old in class, it"s going to be so off. Have equal and independent chance of being chosen. Randomly select certain column and row or something. Sampling error can be reduced if heterogeneity can be reduced. Proportionate: # from each stratum is proportion to total pop. More heterogeneity, more respondents needed a lot of diff, need large sample size.

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