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Collection of Data Chapter 4

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Three Types of Studies Survey Survey Observational Study Observational Study Controlled Experiment Controlled Experiment

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

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

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

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Methods of Data Collection Census: Census: Study ALL subjects of the population of interest Study ALL subjects of the population of interest Sample: Sample: Studying a proper subset of the subjects from the population of interest Studying a proper subset of the subjects from the population of interest

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Issues with Sampling The purpose of sampling is to generate a proper subset of the population that is representative of the population The purpose of sampling is to generate a proper subset of the population that is representative of the population The major concern with sampling is The major concern with sampling isBIAS Bias is a systematic effect that skews all of the data values in a sample Bias is a systematic effect that skews all of the data values in a sample

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Types of Sampling: INVALID Convenience Sampling Convenience Sampling Choosing the subjects in the sample by convenience Choosing the subjects in the sample by convenience Voluntary Response Sampling Voluntary Response Sampling Subjects are included in the sample on the basis of their volunteering to be included Subjects are included in the sample on the basis of their volunteering to be included

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Valid Types of Sampling Simple Random Sample 1.Make a list of all units in population 2.Number each unit in the list 3.Use a random generator or random digit table to select units, one at a time, until you have the desired number you want

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Valid types of Sampling Stratified Random Sample 1.Divide the units of the sampling frame into non- overlapping subgroups 2.Take a simple random sample from each subgroup

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Valid types of Sampling Cluster Sampling 1.Create a numbered list of all the clusters in your population 2.Take a SRS of each Cluster 3.Obtain data on each unit in each cluster of your SRS

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Valid types of Sampling Two-stage Cluster Sample 1.Create a numbered list of all the clusters in your population, and then take an SRS of clusters 2.Create a numbered list of all the units in each cluster already selected, and then take an SRS from each cluster

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Valid Types of Sampling Systematic Sampling 1.By a method such as counting off, divide your population into groups of the size you want for your sample 2.Use a chance method to choose one of the groups for your sample

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The Key to Valid Sampling Subjects are chosen by the application of a probability rule; that is, based on RANDOM SELECTION Subjects are chosen by the application of a probability rule; that is, based on RANDOM SELECTION

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Controlled Experiments Experiment Experiment A study in which the researcher imposes treatment(s) on the subjects. A study in which the researcher imposes treatment(s) on the subjects. Controlled Experiment: Controlled Experiment: A study in which the groups receive different treatments whose effects are compared Units Units The subjects who participate in the study

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Controlled Experiments Vocabulary Continued Subjects: Subjects: The term applied to human units Control Group Control Group the group who receives either no treatment or a placebo treatment, a treatment that causes no effect Treatment Groups Treatment Groups The Group(s) who receives the treatment(s)

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Controlled Experiments Vocabulary Continued Explanatory Variable: Explanatory Variable: The variable to which the researcher assigns values in the study: the independent variable Response Variable: Response Variable: The variable the measures the effect of the value of the explanatory variable: the dependent variable

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Confounding: the Problem Two variables are CONFOUNDED when the effects of the explanatory variable cannot be separated among the treatment groups Two variables are CONFOUNDED when the effects of the explanatory variable cannot be separated among the treatment groups A LURKING VARIABLE is a variable that is not include in the study but may be effecting the results of the experiment A LURKING VARIABLE is a variable that is not include in the study but may be effecting the results of the experiment

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Confounding: the Solution The effects of confounding can be minimized by RANDOMIZATION The effects of confounding can be minimized by RANDOMIZATION The effects of a lurking variable should be spread uniformly among randomized groups The effects of a lurking variable should be spread uniformly among randomized groups

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3 Requirements of Controlled Experiments Comparison Comparison Randomization Randomization Replication Replication

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Back to the 3 Requirements Comparison Comparison Groups that are as similar as possible Randomization Randomization Minimize the effect of confounding influences and lurking variables by spreading these effects equally throughout the groups Replication Replication is the sample size sufficient so that the differences in responses is due to treatments and not chance

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Basic Experimental Design Completely Randomized Design Completely Randomized Design Randomized Block Design Randomized Block Design

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Completely Randomized Design Random Assignment Treatment 1 Treatment 2 Control Results Compare

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Steps in Creating a Completely Randomized Design 1.Number the available experimental units from 1 to n 2.If you have three treatments, for example, use a random number generator to pick n/3 integers at random from 1 to n, discarding any repetitions. The units with those numbers will be given the first treatment. Again pick n/3 integers, discarding any repetitions. Those units will be given the second treatment. The remaining units will get the third treatment.

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Randomized Block Design Block 1 Block 2 Treatment 1 Treatment 2 Treatment 1 Treatment 2 Result Compare

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Steps in Creating a Randomized Block Design 1.Sort your available experimental units into groups (blocks) of similar units. The units in each block should be enough alike that you expect them to have a similar response to any treatment. This is called blocking. 2.Randomly assign a treatment to each unit in the first block. (It ’ s usually best if the same number of units is assigned to each treatment) Then go to the second block and randomly assign a treatment to each unit in this block. Repeat for each block.

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Why Block? To reduce the variability due to individual differences in the subjects To reduce the variability due to individual differences in the subjects

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Steps in Creating a Randomized paired Comparison (matched pairs) 1.Sort your available experimental units into pairs of similar units. The two units in each pair should be enough alike that you expect them to have a similar response to any treatment. 2.Randomly decide which unit in each pair is assigned which treatment. For example, you could flip a coin, with heads meaning the first unit gets the first treatment and tails meaning the first unit gets the second treatment. Then other unit in the pair gets the other treatment.

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Blind and Double Blind Blind Blind When the subjects are not aware of what treatment they are receiving Double Blind Double Blind When the patient and the doctors who evaluate them do not know what treatment was given.

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