Collection of DataCollection of Data Chapter 4Chapter 4

Three Types of Three Types of StudiesStudies

Survey Survey

Observational Study Observational Study

Controlled ExperimentControlled Experiment

SurveySurvey

A study in which a A study in which a researcher gathers data researcher gathers data by asking responses from by asking responses from subjects subjects

Observational Study Observational Study

A study in which the researcher A study in which the researcher observes behaviors of the subjects observes behaviors of the subjects

Controlled Controlled ExperimentExperiment

A study in which the A study in which the researcher imposes researcher imposes treatments on the treatments on the subjects subjects

Methods of Data Methods of Data CollectionCollection

Census:Census:

Study ALL subjects of the Study ALL subjects of the population of interest population of interest

Sample:Sample: Studying a proper subset of Studying a proper subset of the subjects from the the subjects from the population of interest population of interest

Issues with Issues with SamplingSampling

The purpose of sampling is to generate The purpose of sampling is to generate a proper subset of the population that a proper subset of the population that is representative of the populationis representative of the population

The major concern with sampling isThe major concern with sampling is

BIASBIAS Bias is a systematic effect that Bias is a systematic effect that skews all of the data values in a skews all of the data values in a sample sample

Types of Sampling: Types of Sampling: INVALIDINVALID

Convenience SamplingConvenience Sampling

Choosing the subjects in the sample Choosing the subjects in the sample by convenience by convenience

Voluntary Response Voluntary Response SamplingSampling

Subjects are included in the sample Subjects are included in the sample on the basis of their volunteering to on the basis of their volunteering to be included be included

ValidValid Types of Types of SamplingSampling

Simple Random SampleSimple Random Sample 1.1. Make a list of all units in Make a list of all units in

population population

2.2. Number each unit in the list Number each unit in the list

3.3. Use a random generator or Use a random generator or random digit table to select random digit table to select units, one at a time, until units, one at a time, until you have the desired number you have the desired number you want you want

ValidValid types of types of SamplingSampling

Stratified Random Stratified Random SampleSample

1.1. Divide the units of the sampling Divide the units of the sampling frame into non-overlapping subgroupsframe into non-overlapping subgroups

2.2. Take a simple random sample from each Take a simple random sample from each subgroup subgroup

ValidValid types of types of SamplingSampling

Cluster SamplingCluster Sampling 1.1. Create a numbered list of all the Create a numbered list of all the

clusters in your population clusters in your population

2.2. Take a SRS of each Cluster Take a SRS of each Cluster

3.3. Obtain data on each unit in each Obtain data on each unit in each cluster of your SRS cluster of your SRS

ValidValid types of types of SamplingSampling

Two-stage Cluster SampleTwo-stage Cluster Sample 1.1. Create a numbered list of all the Create a numbered list of all the

clusters in your population, and then clusters in your population, and then take an SRS of clusters take an SRS of clusters

2.2. Create a numbered list of all the Create a numbered list of all the units in each cluster already units in each cluster already selected, and then take an SRS from selected, and then take an SRS from each cluster each cluster

Valid Types of Valid Types of Sampling Sampling

Systematic SamplingSystematic Sampling 1.1. By a method such as counting off, By a method such as counting off,

divide your population into groups of divide your population into groups of the size you want for your sample the size you want for your sample

2.2. Use a chance method to choose one of Use a chance method to choose one of the groups for your sample the groups for your sample

The Key to The Key to ValidValid SamplingSampling

Subjects are chosen by the application Subjects are chosen by the application of a probability rule; that is, based of a probability rule; that is, based on RANDOM SELECTION on RANDOM SELECTION

Controlled Controlled ExperimentsExperiments

Experiment Experiment

A study in which the researcher A study in which the researcher imposes treatment(s) on the subjects. imposes treatment(s) on the subjects.

Controlled Experiment:Controlled Experiment:

A study in which the groups receive A study in which the groups receive different treatments whose effects are different treatments whose effects are compared compared

Units Units

The subjects who participate in the The subjects who participate in the studystudy

Controlled Controlled Experiments Experiments Vocabulary Vocabulary ContinuedContinued Subjects:Subjects:

The term applied to human unitsThe term applied to human units

Control Group Control Group

the group who receives either no the group who receives either no treatment or a placebo treatment, a treatment or a placebo treatment, a treatment that causes no effecttreatment that causes no effect

Treatment GroupsTreatment Groups

The Group(s) who receives the The Group(s) who receives the treatment(s)treatment(s)

Controlled Controlled Experiments Experiments

Vocabulary ContinuedVocabulary Continued

Explanatory Variable:Explanatory Variable: The variable to which the researcher The variable to which the researcher assigns values in the study: the assigns values in the study: the independent variable independent variable

Response Variable:Response Variable:The variable the measures the effect The variable the measures the effect of the value of the explanatory of the value of the explanatory variable: the dependent variablevariable: the dependent variable

Confounding: the Confounding: the ProblemProblem

Two variables are Two variables are CONFOUNDEDCONFOUNDED when the when the effects of the explanatory variable effects of the explanatory variable cannot be separated among the cannot be separated among the treatment groups treatment groups

A A LURKING VARIABLELURKING VARIABLE is a variable that is a variable that is not include in the study but may be is not include in the study but may be effecting the results of the effecting the results of the experiment experiment

Confounding: the Confounding: the SolutionSolution

The effects of confounding can be The effects of confounding can be minimized by minimized by RANDOMIZATIONRANDOMIZATION

The effects of a lurking variable The effects of a lurking variable should be spread uniformly among should be spread uniformly among randomized groups randomized groups

3 Requirements of 3 Requirements of Controlled Controlled ExperimentsExperiments

Comparison Comparison

Randomization Randomization

Replication Replication

Back to the 3 Back to the 3 Requirements Requirements

Comparison Comparison Groups that are as similar as possibleGroups that are as similar as possible

RandomizationRandomizationMinimize the effect of confounding influences Minimize the effect of confounding influences and lurking variables by spreading these and lurking variables by spreading these effects equally throughout the groups effects equally throughout the groups

Replication Replication is the sample size sufficient so that is the sample size sufficient so that the differences in responses is due to the differences in responses is due to treatments and not chancetreatments and not chance

Basic Basic Experimental Experimental

DesignDesign Completely Randomized Completely Randomized Design Design

Randomized Block DesignRandomized Block Design

Completely Randomized Completely Randomized DesignDesign

Random

Assignment

Treatment 1

Treatment 2

Control

Results

Results

Results

Compare

Steps in Creating a Completely Steps in Creating a Completely Randomized Design Randomized Design

1.1. Number the available experimental Number the available experimental units from 1 to n units from 1 to n

2.2. If you have three treatments, for If you have three treatments, for example, use a random number example, use a random number generator to pick n/3 integers at generator to pick n/3 integers at random from 1 to n, discarding any random from 1 to n, discarding any repetitions. The units with those repetitions. The units with those numbers will be given the first numbers will be given the first treatment. Again pick n/3 integers, treatment. Again pick n/3 integers, discarding any repetitions. Those discarding any repetitions. Those units will be given the second units will be given the second treatment. The remaining units will treatment. The remaining units will get the third treatment. get the third treatment.

Randomized Block Randomized Block DesignDesign

Block 1

Block 2

Treatment 1

Treatment 2

Treatment 1

Treatment 2

Result

Result

Result

Result

Compare

Compare

Compare

Steps in Creating a Steps in Creating a Randomized Block Randomized Block

DesignDesign 1.1. Sort your available experimental units Sort your available experimental units

into groups (blocks) of similar units. into groups (blocks) of similar units. The units in each block should be The units in each block should be enough alike that you expect them to enough alike that you expect them to have a similar response to any have a similar response to any treatment. This is called blocking.treatment. This is called blocking.

2.2. Randomly assign a treatment to each Randomly assign a treatment to each unit in the first block. (Itunit in the first block. (It’’s s usually best if the same number of usually best if the same number of units is assigned to each treatment) units is assigned to each treatment) Then go to the second block and Then go to the second block and randomly assign a treatment to each randomly assign a treatment to each unit in this block. Repeat for each unit in this block. Repeat for each block.block.

Why Block?Why Block?

To reduce the variability due to To reduce the variability due to individual differences in the subjectsindividual differences in the subjects

Steps in Creating a Steps in Creating a Randomized paired Randomized paired

Comparison (matched pairs)Comparison (matched pairs)

1.1. Sort your available experimental units Sort your available experimental units into pairs of similar units. The two into pairs of similar units. The two units in each pair should be enough units in each pair should be enough alike that you expect them to have a alike that you expect them to have a similar response to any treatment.similar response to any treatment.

2.2. Randomly decide which unit in each Randomly decide which unit in each pair is assigned which treatment. For pair is assigned which treatment. For example, you could flip a coin, with example, you could flip a coin, with heads meaning the first unit gets the heads meaning the first unit gets the first treatment and tails meaning the first treatment and tails meaning the first unit gets the second treatment. first unit gets the second treatment. Then other unit in the pair gets the Then other unit in the pair gets the other treatment. other treatment.

Blind and Double Blind and Double BlindBlind

BlindBlindWhen the subjects are not aware of what When the subjects are not aware of what treatment they are receiving treatment they are receiving

Double BlindDouble Blind When the patient and the doctors who When the patient and the doctors who evaluate them do not know what evaluate them do not know what treatment was given.treatment was given.