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

But, what about ___________? 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: 3don’t do threatening experiments 3don’t set up obviously competitive situations 3don’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? 3what about giving control group some perk to compensate? 3don’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 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 Blinding

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 1: Define the the target population Step 2: Select The Sampling Frame Step 3: Probability OR Non-probability? Step 4: Plan Selection of sampling units Step 5: Determine Sample Size Step 6: Select Sampling units Step 7: Conduct Fieldwork

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 Simple Random Cluster SystematicStratified Non- probability QuotaJudgment ConvenienceSnowball

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 Probability of Selection = Sample Size Population Size

Advantages minimal knowledge of population needed External validity high; internal validity high; statistical estimation of error Easy to analyze data Disadvantages 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??

Advantages Moderate cost; moderate usage External validity high; internal validity high; statistical estimation of error Simple to draw sample; easy to verify Disadvantages 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

Advantages Assures representation of all groups in sample population needed Characteristics of each stratum can be estimated and comparisons made Reduces variability from systematic Disadvantages 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

Advantages 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 Disadvantages 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 Simple Random Cluster SystematicStratified Non- probability QuotaJudgment ConvenienceSnowball

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?

Advantages Very low cost Extensively used/understood No need for list of population elements Disadvantages 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

Advantages Moderate cost Commonly used/understood Sample will meet a specific objective Disadvantages 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

Advantages moderate cost Very extensively used/understood No need for list of population elements Introduces some elements of stratification Disadvantages 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

Advantages low cost Useful in specific circumstances Useful for locating rare populations Disadvantages Bias because sampling units not independent Projecting data beyond sample not justified.