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# Experimental Design PowerPoint PPT Presentation

Experimental Design. Playing with variables. The nature of experiments. allow the investigator to control the research situation so that causal relationships among variables may be evaluated

## Related searches for Experimental Design

Experimental Design

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## Experimental Design

Playing with variables

The nature of experiments

• allow the investigator to control the research situation so that causal relationships among variables may be evaluated

• One variable is manipulated and its effect upon another variable is measured, while other variables are held constant

So… you’ve decided to do an experiment

• Decisions… decisions… decisions

Decision 1: Independent Variable?

• value is changed or altered independently of other variables

• hypothesized to be the causal influence

• categorical or continuous (?)

Experimental Treatments:

• alternative manipulations of the Independent Variable

Experimental and Control Groups

• Control Group

• Experimental Groups

• there can be more than one treatment level of the Independent Variable (basic or factorial)

• there can be more than one IV

Experimental Groups

Decision 2: Dependent Variable

• The criterion or standard by which the results are judged

• It is presumed that changes in the Dependent Variable are the result of changes in one or more Independent Variable

• the choice of Dependent Variable determines the type of answer that is given to the research question

Decision 3: Test units/unit of analysis

• The subjects or entities whose responses to the experimental treatment are being measured

• People are the most common test unit in business research

Decision 4: Extraneous variables

• A number of extraneous or “other” variables may affect the dependent variable and distort the results

Conditions of constancy:

• When extraneous variables cannot be eliminated we strive to hold Extraneous Variables constant for all subjects

• Problems… problems…

IMPACT OF THE RESEARCH SITUATION

Demand Characteristics: experimental design procedures that unintentionally hint to subjects about the experimenter’s hypothesis

• rumour

• instructions

• status and personality of researcher

• unintentional cues from experimenter

• experimental procedure itself

• Setting: Field versus Laboratory

### Field versus Laboratory

• Field experiments: usually used to fine-tune strategy and determine sales volume

• Laboratory: used when control over the experimental setting is more important

### Experimental Design effects….

The Hawthorne effect

• Subjects perform differently just because they know they are are experimental subjects

• Western Electric’s Hawthorne Plant 1939 study of light intensity

The Guinea Pig effect

• exhibit the behaviour that they think is expected

• Potential Solutions:

• run experiment for a longer period

• use a control group

• Deception (?)

Experimental Treatment Diffusion

• if treatment condition perceived as very desirable relative to the control condition, members of the control group may seek access to the treatment condition

• Potential Solutions:

-have control group in another site

-of course, this introduces new variables!

John Henry Effect

• legend of black railway worker

• control group overcompensates

• Potential Solutions:

• don’t do threatening experiments

• don’t set up obviously competitive situations

• don’t tell control group that they are control group

• conduct in another location somewhere else

• unfortunately, produces new variable of different location, neighbourhood, etc.!

Resentful Demoralization of Control Group

• Control group artificially demoralized if perceives experimental group receiving desirable treatment being withheld from it

• Potential Solutions?

• what about giving control group some perk to compensate?

• don’t tell them they are control group! (but what about informed consent?)… Use of Placebo… use of blinding…

Getting control….

• Design decisions

• Physical Control

• Holding the value or level of extraneous variables constant throughout the course of an experiment.

• Statistical Control

• Adjusting for the effects of confounding variables by statistically adjusting the value of the dependent variable for each treatment conditions.

• Design Control

• Use of the experimental design to control extraneous causal factors.

Blinding

• Blinding is utilized to control subjects knowledge of whether or not they have been given a particular experimental treatment

• double-blind experiment

• secrecy

• but then violate principle of informed consent

• screen out or balance number of placebo reactors in treatment & control groups

## Sampling

Who and How

And How to Screw It up

### Terms

• Sample

• Population (universe)

• Population element

• census

### Why use a sample?

• Cost

• Speed

• Sufficiently accurate (decreasing precision but maintaining accuracy)

• More accurate than a census (?)

• Destruction of test units

Stages in the Selection of a Sample

Step 7: Conduct

Fieldwork

Step 2: Select

The Sampling

Frame

Step 3: Probability

OR Non-probability?

Step 1: Define the

the target population

Step 6: Select

Sampling units

Step 5: Determine

Sample Size

Step 4: Plan

Selection of

sampling

units

### Step 1: Target Population

• The specific, complete group relevant to the research project

• Who really has the information/data you need

• How do you define your target population

• Bases for defining the population of interest include:

• Geography

• Demographics

• Use

• Awareness

### Operational Definition

• A definition that gives meaning to a concept by specifying the activities necessary to measure it.

• “The population of interest is defined as all women in the City of Lethbridge who hold the most senior position in their organization.”

• What variables need further definition?

### Step 2: Sampling Frame

• The list of elements from which a sample may be drawn.

• Also known as: working population.

• Examples?

Sampling Frame Error:

• error that occurs when certain sample elements are not listed or available and are not represented in the sampling frame.

Sampling Units:

• A single element or group of elements subject to selection in the sample.

• Primary sampling unit

• Secondary sampling unit

Error: Less than perfectly.

representative samples.

• Random sampling error.

• Difference between the result of a sample and the result of a census conducted using identical procedures; a statistical fluctuation that occurs because of chance variation in the selection of the sample.

…Error

• Systematic or non-sampling error.

• Results from some imperfect aspect of the research design that causes response error or from a mistake in the execution of the research

• Examples: Sample bias, mistakes in recording responses, non-responses, mortality etc,.

…Error

• Non-response error.

• The statistical difference between a survey that includes only those who responded and a survey that also includes those that failed to respond.

### Step 3: Choice!

• Probability Sample:

• A sampling technique in which every member of the population will have a known, nonzero probability of being selected

### Step 3: Choice!

• Non-Probability Sample:

• Units of the sample are chosen on the basis of personal judgment or convenience

• There are no statistical techniques for measuring random sampling error in a non-probability sample. Therefore, generalizability is never statistically appropriate.

Classification of Sampling Methods

Sampling

Methods

Probability

Samples

Non-

probability

Systematic

Stratified

Convenience

Snowball

Cluster

Simple

Random

Judgment

Quota

Probability Sampling Methods

• Simple Random Sampling

• the purest form of probability sampling.

• Assures each element in the population has an equal chance of being included in the sample

• Random number generators

Sample Size

Probability of Selection =

Population Size

• minimal knowledge of population needed

• External validity high; internal validity high; statistical estimation of error

• Easy to analyze data

• High cost; low frequency of use

• Requires sampling frame

• Does not use researchers’ expertise

• Larger risk of random error than stratified

• Systematic Sampling

• An initial starting point is selected by a random process, and then every nth number on the list is selected

• n=sampling interval

• The number of population elements between the units selected for the sample

• Error: periodicity- the original list has a systematic pattern

• ?? Is the list of elements randomized??

• Moderate cost; moderate usage

• External validity high; internal validity high; statistical estimation of error

• Simple to draw sample; easy to verify

• Periodic ordering

• Requires sampling frame

• Stratified Sampling

• Sub-samples are randomly drawn from samples within different strata that are more or less equal on some characteristic

• Why?

• Can reduce random error

• More accurately reflect the population by more proportional representation

• How?

1.Identify variable(s) as an efficient basis for stratification. Must be known to be related to dependent variable. Usually a categorical variable

2.Complete list of population elements must be obtained

3.Use randomization to take a simple random sample from each stratum

• Types of Stratified Samples

• Proportional Stratified Sample:

• The number of sampling units drawn from each stratum is in proportion to the relative population size of that stratum

• Disproportional Stratified Sample:

• The number of sampling units drawn from each stratum is allocated according to analytical considerations e.g. as variability increases sample size of stratum should increase

• Types of Stratified Samples…

• Optimal allocation stratified sample:

• The number of sampling units drawn from each stratum is determined on the basis of both size and variation.

• Calculated statistically

• Assures representation of all groups in sample population needed

• Characteristics of each stratum can be estimated and comparisons made

• Reduces variability from systematic

• Requires accurate information on proportions of each stratum

• Stratified lists costly to prepare

• Cluster Sampling

• The primary sampling unit is not the individual element, but a large cluster of elements. Either the cluster is randomly selected or the elements within are randomly selected

• Why?

• Frequently used when no list of population available or because of cost

• Ask: is the cluster as heterogeneous as the population? Can we assume it is representative?

• Cluster Sampling example

• You are asked to create a sample of all Management students who are working in Lethbridge during the summer term

• There is no such list available

• Using stratified sampling, compile a list of businesses in Lethbridge to identify clusters

• Individual workers within these clusters are selected to take part in study

• Types of Cluster Samples

• Area sample:

• Primary sampling unit is a geographical area

• Multistage area sample:

• Involves a combination of two or more types of probability sampling techniques. Typically, progressively smaller geographical areas are randomly selected in a series of steps

• Low cost/high frequency of use

• Requires list of all clusters, but only of individuals within chosen clusters

• Can estimate characteristics of both cluster and population

• For multistage, has strengths of used methods

• Larger error for comparable size than other probability methods

• Multistage very expensive and validity depends on other methods used

Classification of Sampling Methods

Sampling

Methods

Probability

Samples

Non-

probability

Systematic

Stratified

Convenience

Snowball

Cluster

Simple

Random

Judgment

Quota

Non-Probability Sampling Methods

• Convenience Sample

• The sampling procedure used to obtain those units or people most conveniently available

• Why: speed and cost

• External validity?

• Internal validity

• Is it ever justified?

• Very low cost

• Extensively used/understood

• No need for list of population elements

• Variability and bias cannot be measured or controlled

• Projecting data beyond sample not justified.

• Judgment or Purposive Sample

• The sampling procedure in which an experienced research selects the sample based on some appropriate characteristic of sample members… to serve a purpose

• Moderate cost

• Commonly used/understood

• Sample will meet a specific objective

• Bias!

• Projecting data beyond sample not justified.

• Quota Sample

• The sampling procedure that ensure that a certain characteristic of a population sample will be represented to the exact extent that the investigator desires

• moderate cost

• Very extensively used/understood

• No need for list of population elements

• Introduces some elements of stratification

• Variability and bias cannot be measured or controlled (classification of subjects0

• Projecting data beyond sample not justified.

• Snowball sampling

• The sampling procedure in which the initial respondents are chosen by probability methods, and then additional respondents are obtained by information provided by the initial respondents