Types of Studies

Observational Study – Observes and measures characteristics without trying to modify the subjects being studied Experiment – Impose a treatment on the subjects, then observe the response

Types of Studies

Cross-sectional Study – Data are observed, measured, and collected at one point in time .

– Example: What percentage of people own dogs?

– Most polls are cross-sectional studies

Types of Studies

Retrospective (or case control) Study – Data are collected from the past – Example: What was the average rainfall in 1994?

Prospective (or Longitudinal or Cohort) Study – Data are collected in the future from groups (cohorts) sharing similar characteristics – Example: What percentage of dogs who attend an obedience class are still well-behaved 2 years later?

Confounding

• When it’s not possible to distinguish the effects of each factor (i.e., which factor caused the outcome?) • Usually, when there are multiple differences between comparison groups • Confounding can be avoided by good study design

Examples of Confounding

Example: A middle-school implements a new math curriculum. They also encourage parent participation, and offer after-school tutoring. An improvement in performance results Example: An experiment is done to determine if students perform better on tests while listening to music. Each subject is given two similar tests; the first in silence, and the second while listening to music. Performance is higher on the second test.

Ways to control confounding

Blocks – Create groups with similar characteristics – Ideally identical in every way except factor being compared Blinding – Subjects don’t know if they’re receiving a treatment or placebo Double-blinding – Experimenters don’t know which subjects are receiving the treatment

Experiment Design

(How to create blocks) Completely randomized experimental design – Subjects are assigned to groups based on a process of random selection Rigorously controlled experimental design – Subjects are

very

carefully chosen and assigned to groups so they have similar characteristics

Sample size

Sample Size – Sample must be large enough to reveal the true nature of any effects – Large samples do not make up for bad samples; sample must be selected appropriately for results to be valid.

Random Sampling

Random Sample – Members of the population are chosen so that each

individual

has equal likelihood of being chosen Simple Random Sample – A special random sample where

every possible sample

is equally likely

Other types of sampling

Systematic sampling – Population is ordered, and every

k

th element is chosen.

– This is only random sampling if the

starting

element is randomly chosen

Other types of sampling

Stratified sampling – Population is divided into groups with similar characteristics, and a sample is chosen from each subgroup (stratum) – This is only random sampling if the sample from each subgroup is chosen randomly

Other types of sampling

Cluster sampling – Divide the population into sections, or clusters. Select a group of clusters, and use all members of those clusters.

– This is only random sampling if the clusters to be used are selected randomly

Not-so-good sampling methods

• Voluntary response sample • Convenience sample – Choosing whoever’s handy

Sources of Error

Sampling error – The difference between the sample result and the population result, caused by chance fluctuations Non-sampling error – Error caused by problems in collecting, recording, and analyzing the data (like broken tools, typos, or miscalculations), or by a biased sample (bad sample selection)

Differences in error

Sampling error is natural and unavoidable . We must consider it when analyzing our data, but we cannot eliminate it.

Non-sampling error is avoidable , and every effort should be taken to do so.

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