stat10010_1

STAT10010

Introductory Statistics

Dr. Patrick Murphy

School of Mathematical Sciences

TextbookSeeing Through Statistics

by

Jessica Utts

OR

STATISTICS

by

UTTS AND HECKARD

DISCOUNT PRICE IN CAMPUS BOOKSHOP

Course Website available on Monday

WWW.UCD.IE/mathsci

Click on “classpages”

Then Click on STAT10010

Other Requirements

New Cambridge Statistical Tables

Calculator with Statistics Mode

IMPORTANT

MY PART OF THIS COURSE

DOES NOT USE

BLACKBOARD for NOTES

“I couldn’t find the notes on Blackboard”

Assessment

60% Final Exam

10% Laboratory

20% In Class Tests

10% Participation in Lectures/Labs/Tutorials

Participation

Arriving Late

Leaving Early

Talking

Eating

Texting

Not Paying Attention

Sleeping

Whole Class Loses Marks

Individuals May Gain Marks

LECTURES 2 per week

Tutorials 1 per week

Tutorials Start in Week 4

Computer Labs Start in Week 4

What do you know about statistics?

It’s boring…

There are three kinds of lies:

• Lies

• Damned Lies

• and

• Statistics

• - Benjamin Disraeli

Simpsons’ episode:• Homer is questioned about his newly formed

vigilante group

Newscaster: Since your group started up, petty crime is down 20%, but other crimes are up.

Such as heavy sack beating which is up 800%. So you’re actually increasing crime.

Homer: You can make up statistics to prove anything.

43% of people know that.

Misuse of Statistics

INTRO STATS

PART 1

Section A : DATA COLLECTION

1. Introduction and Terminology

2. Seven Critical Components of a Study

3. Questionnaire Design

4. Survey Design

5. Design of Experiments and Observational Studies.

Chapter 1:Terminology

Statistics is the science of data. This involves collecting, analysing and interpreting information.

STATISTICS is the Science of Variability.

Descriptive Statistics uses graphical and numerical techniques to summarise and display the information contained in a dataset.

Inferential Statistics uses sample data to make decisions or predictions about a larger population of data

Chapter 1The Beginning

Sample Survey

Observational Study

Designed Experiment

DATA

More Definitions

Population: The entire collection of individuals or objects about which information is desired.

Sample: A part (subset) of the population selected in some prescribed manner.

Variable: A characteristic or property of an individual unit in the population.

Representative Sample: A selection of data chosen from the target population which exhibits characteristics typical of the population.

Representative samples should give unbiased estimates Chapter 2

CHAPTER 2:7 Critical Components: To believe or not to believe the results of a study.

• The importance of “not always believing what we read in the papers” cannot be overstated.

• This lesson applies not just to newspapers but to academic research papers in journals also.

• In fact it applies everywhere someone presents us with a conclusion based on a study.

• When we are presented with a study we should examine 7 components of the study:

1. The source of the research and of the funding.

2. The researchers who had contact with the participants.

3. The individuals or objects studied and how they were selected.

4. The exact nature of the measurements made or the questions asked.

5. The setting in which the measurements were asked.

6. The extraneous differences in groups being compared.

7. The magnitude of any claimed effects or differences.

1. The source of the research and of the funding.Research costs money, researchers need to be paid, equipment has to be bought, subjects need to be found.

We should always be aware of this fact when we examine the claims made by researchers.

We should look differently on research conducted by• Independent agencies e.g. CSO, Eurostat, ESRI?• Academics (who funds them?)• Companies trying to convince consumers to buy their

product instead of a competitors. • Journalists

2. The researchers who had contact with the participants.When a study is conducted, the responses of an individual

may depend on who asks the questions.

Question: How much money do you earn?

Response to a Revenue Commissioner will probably be different than to a friend or date whom you are trying to impress.

Would you trust a study on the use of illegal drugs which was carried out by Gardai knocking on peoples’ doors.


• THE INDIVIDUALS STUDIEDCan we apply the results of a study conducted on men to women?Does a study conducted on Americans apply to Irish people?

• HOW THEY WERE SELECTEDMany studies rely on volunteers, is this wise?Is there not a difference between the kind of people who volunteer for a study and those who don’t.Consider what would happen if someone came up to you in the street with a questionnaire.


You should be aware that the wording and the ordering of questions can influence responses.

What is your opinion on the plight of refugees forced by war in Syria to flee their homes and come to Ireland?

How do you feel about all those foreigners coming over here taking our jobs?

5. The setting in which measurements were taken.

When and where was the study conducted.

Studies conducted at certain times of the day may exclude certain elements of the target population. @3.00pm many employed people are at work, so a study conducted on Grafton St. at that time will probably not be representative.

Phone surveys conducted during the day will also probably under-represent the views of workers.

How you would reply to certain questions posed in a police interrogation room would probably differ to how you would answer those same questions in a pub. Capilano Suspension Bridge

6. The extraneous differences in groups being compared-Confounding.

CONFOUNDING FACTORSA study shows that exam scores among marijuana smokers are lower than among non-smokers.A conclusion is drawn that marijuana, impairs exam performance.

We should however consider that the type of person who smokes dope may be the kind of unmotivated slacker who doesn’t do enough study for their exams irrespective of whether they smoked or not.


Newspapers seldom say how large the effects of a statistical study are. Are the results STATISTICALLY SIGNIFICANT?Is Statistically Significant the same as Meaningfully Significant?UK & IRISH General Elections.

Taking aspirin reduces heart attacks.

We really should be told that the reduction is from 17 per 1000 to 9.4 per 1000.

But we should also be told that this increases strokes and stomach ulcers.

Review: Seven Critical Components of a Study

1. The source of the research and of the funding.

2. The researchers who had contact with the participants.



5. The setting in which the measurements were asked.

6. The extraneous differences in groups being compared.


Chapter 3

Chapter 3Questionnaire Design: How to ask a question (plus some statistical terms).

• We saw in the previous chapter that deciding exactly what to measure and what questions to ask is extremely important.

Remember the 4th component

• 4. The exact nature of the measurements made or the questions asked.

• In this chapter we will examine this component in detail.

Section 3.1 Questions

• A study was conducted in the US in 1974 where two researchers showed college students a film of a car accident.

• After viewing the film the students were asked one of two questions.

• Group 1 was asked the question:

“About how fast were the cars going when they contacted each other”– The average of the responses for Group 1 was 31.8

Miles per Hour

• Group 2 was asked the question:

“About how fast were the cars going when they collided with each other”

– The average of the responses for Group 2 was 40.8 Miles per Hour

Both groups had seen exactly the same film. The only difference was the use of the word collided instead of contacted.

• Simply using the word collided increased peoples estimates of the speed of the accident by 9 mph or 28%.

There are many problems associated with asking questions we will examine seven of them

• Deliberate Bias

• Unintentional Bias

• Desire to please

• Asking the uninformed

• Unnecessary complexity

• Ordering of questions

• Confidentiality and anonymity

• Deliberate Bias

Sometimes when a survey is conducted, the questions are worded in a leading manner to illicit a favourable response.

Recall the questions on refugees in Ireland.

• The responses to questions that begin “Do you agree that…..” should be treated with caution. This does not invalidate such questions just be careful to see if there is deliberate bias.

• “Asked whether they felt New Improved Persil was better at cleaning clothes than ordinary Persil, 90% of people said yes.”– Who wouldn’t say yes to such a leading question.

• Deliberate Bias

• “Do you agree with the continued destruction of trees on this campus for the construction of new buildings?”

• “Do you agree that during the construction of new buildings to alleviate the overcrowding on Belfield campus that it is okay to knock down a few trees?”

• Unintentional Bias

Besides the deliberate bias caused by leading questions, sometimes the questions are worded badly unintentionally and are misinterpreted by many respondents.

• “What was the most important date in your life?”

People may respond differently to this question.

• Some may interpret the word date as calendar date and may reply for instance

- “The day I was born”

- “The day I passed my final exams”

• Some may interpret the word date as “dinner and a movie”.

• And some may think …

• that a shrivelled fruit is being referred to.

• Desire to please

Many respondents like to please the questioner.

Recall the sketch from “The Fast Show”

Respondents do not like to admit to certain socially undesirable habits

• Surveys on the prevalence of cigarette smoking based on surveys of individuals disagree with data from cigarette sales.

In Dublin’s fair city…

• Asking the uninformed– Nobody likes appearing ignorant when asked a

question.

• The day that Articles 2 & 3 of our constitution were changed TV3 sent a reporter out to Grafton Street to ask Dubliners: – Do you know what important thing happened today in the

North?”– Most people replied yes.

• But the reporter then asked the people:– “OK, so what happened?”

– Many people got embarrassed and said that they didn’t know after all.

• Unnecessary Complexity

Questions should be kept simple, otherwise people may get confused.

• “Shouldn’t students not be allowed to repeat their exams if they fail at the first attempt.”

• This sentence actually contains a double negative .Is it therefore equivalent to the question:– “Should students be allowed to repeat their exams if

they fail at the first attempt.”

• Ordering of Questions.

If two questions are asked of a respondent but one question causes the respondent to think about something they may not have thought of otherwise then the order of the questions will be important.

• Example– Name the five most popular types of television

programme.– Do you watch hospital dramas on TV such as Grey’s

Anatomy?

• Confidentiality and Anonymity

• Anonymity

Some questions may only be answered if the respondent feels that they are anonymous.

• Confidentiality

If a follow up study is necessary then respondents cannot remain anonymous and so confidentiality of responses must be ensured.

• Questions on sexual behaviour and financial dealings are usually only responded to if either Anonymity or Confidentiality can be ensured.

Section 3.2 Choices

• When asking a question should we present the respondent with a choice of possible answers.

• Should we ask open questions or closed questions?

• Most opinion polls are conducted using closed questions i.e. the respondent is asked to chose between a group of answers. This allows easy compilation of the results of the survey compared to an open question format.

• Closed Questions.

We’ve already mentioned that opinion polls often use the closed question format, in which the respondent is presented with a choice of answers. This form of question can often lead to some very strange results.

• The textbook refers to a study conducted in 1987 in the US to examine the difference between Open Questions and Closed Questions. The study asked the following Question:

• “What do you think is the most important problem facing this country today?”

• Half of the sample were given this as an open question, the top four responses were:– 17% Unemployment– 17% General Economic Problems– 12% Threat of Nuclear War– 10% Foreign Affairs

• The other half of the sample were given this as a closed Question to pick between the following choices:– The Energy Shortage– The Quality of Public Schools– Legalised Abortion– Pollution– If you prefer you may name a different problem as

most important.

• The responses to this closed question were:– 5.6% The Energy Shortage– 32% The Quality of Public Schools– 8.4% Legalised Abortion– 14% Pollution

• So even though the respondents were allowed to choose an alternative to these 4 choices, 60% saw these four as being the most important problems.

• But in the open question format these problems were only listed by 2.4% of respondents.

• Something is wrong here!

• WHAT IS HAPPENING?

• Open Questions.

• We mentioned one problem with the open question format, that it is hard to compile results from possibly thousands of different responses.

• There is another major problem with the open question format, this was highlighted in the same 1987 study referred to earlier.

• A group of respondents were asked to “name one or two of the most important national or world events or changes during the past 50 years”

• Half of this sample were given this as an open question, the top responses were:– 14.1% World War II– 6.9% Space Exploration– 4.6% JFK Assassination– 10.1% Vietnam War– 10.6% Don’t know– 53.7% All Other Responses

• These responses were then given as a closed Question together with another choice “The invention of the computer” - this had been mentioned by only 1.4% of respondents in the Open Question format.

• The responses to this closed question were:– 22.9% World War II– 15.8% Space Exploration– 11.6% JFK Assassination– 14.1% Vietnam War– 29.9% The Invention of the Computer– 0.3% Don’t know– 5.4% All Other Responses

• The problem here was the wording of the question, people concentrated on the word events rather than changes. When it was shown to them they realised that the invention of the computer was indeed one of the most important changes during the past 50 years.

• To summarise:

Perhaps the best way to ask a question is to conduct a small trial Open Question format survey. Then use the responses from this trial survey as the choices in a Closed Question survey together with any other answers that may not immediately spring to mind.

Section 3.3 Defining what’s being measured

• Before we use the results of a survey we should be fully aware of what was actually measured by the survey.

• Unemployment in Ireland.

• Live Register 166,142

• QNHS 86,700

• GROWTH RATES

• Q3 2002

• GDP 6.9%

• GNP -0.3%

Section 3.4 Some Statistical Terms• Measurement/Numerical Data: Data we measure in the

form of numbers.

• Examples:– Percentage you will get on the summer exam for this

course.– Number of lectures that you will skip. – Frequency of radio station you listen to when studying.

• Categorical Data: Data which can be placed in a category, cannot add/subtract this kind of data.

• Examples:– Grade you will receive on the summer exam for this

course.– Name of radio station you listen to.– Brand of shoes you are wearing.

Numerical/Measurement data is further distinguished as to whether it is Discrete or Continuous.

• Discrete variables take only isolated whole number values (integers) on the number line. – Example: Number of Nike runners in this class.

• Continuous variables have values comprising entire intervals of the number line. Decimals and Fractions are allowed.– Example: The duration of this class . remember seconds

are not the smallest unit of time measurement. This class could possibly last 50.123456789 Minutes.

• Validity

A valid measurement is one which actually measures what it claims to measure.– Example: Unemployment figures are validly

measured using the Labour Force Survey not the Live Register

• Reliability

A reliable measurement is one which will give approximately the same result time after time, when taken on the same individual or object.– Example: Most physical measurements are reliable,

for example measuring your weight using a bathroom scales.

– Some measurements may be reliable but not necessarily valid.

• Are exams reliable measuring devices?

• Are exams results valid measurements of intelligence?

• Bias

Sometimes when measurements are made a systematic error is made which underestimates or overestimates the true value. Such a measurement is called a biased measurement.

• Example: Suppose your bathroom scales always overestimated your weight.

• Example: Car Speedometers are deliberately biased to overestimate a car’s real speed.

• Variability– If we try to measure a certain characteristic for

many different objects or people we will most likely not get the same answer each time. The fact that the observations vary is referred to as the variability in the dataset.

– Some datasets are more variable than others:– Example: A dataset consisting of the ages of 100

students in UCD will be less variable than a dataset consisting of the ages 100 randomly chosen Irish people.

Homework

• Design two surveys to look at some of the concepts in this chapter.

• Chose a random sample of 20 people and divide the sample in to two groups of 10.

• 1. Examine bias caused by changing words in one question.

• 2. Examine the effects of using Open Questions vs Closed Questions

• The Topics of the questions are up to you!

Chapter 4

Chapter 4How to get the Data Part 1:Survey Design

• In the first 3 Chapters of this course we spoke at length about what care we should take in conducting a study ourselves or in interpreting the results of someone else’s study.

• What we didn’t mention was how to actually conduct the study, that will be the topic of today’s lecture.

• We saw that studies can take three forms:

• Experiments

• Observational Studies

• Sample Surveys

• We saw earlier that experiments are possibly the best way to conduct studies as the researcher usually has complete control over the elements of the study. And experiments allow a determination of cause and effect.

• Since this is a human sciences/business course and not a Biology or Chemistry course we will restrict ourselves to experiments involving humans.

• No not what you may think, experiments these days rely on volunteer subjects.

Experiments

• The Experimental procedure involves manipulating something called the Explanatory Variable and seeing the effect on something called the Outcome Variable

• Example: In an experiment to test the effect of a new diet.

• The Explanatory Variable would be?

• The Outcome Variable would be?

• The experiment has to be designed to eliminate to any extraneous effects and to determine only the results of the explanatory variable on the outcome variable.

• The way that this is accomplished is that participants are randomly assigned to one of two groups:

• One group receives the treatment the other receives a placebo - ie no treatment at all.

• This random assignment to a treatment group or control group is the way most clinical trials are conducted today.

• This form of study is similar to an experiment except that the treatment occurs naturally and is not imposed on the subjects.

• It is much harder to establish a cause and effect relationship using an observational study than using an experiment because we cannot create control and treatment groups to eliminate confounding effects.

• One attempt to isolate the explanatory variable is to conduct what is called a case control study.

• We will examine this type of study in detail later.

Observational Studies

• We will concentrate for the rest of this chapter on sample surveys.

• First some definitions:– A Unit is a single individual or object to be measured.– A Population is the entire collection of Units about

which we would like information.– A Sample is the collection of Units we actually

measure.– A Sampling Frame is a list of Units from which the

sample is chosen. Ideally the sampling frame includes the whole Population.

Sample Surveys

• In a Sample Survey measurements are taken on a sample chosen from the Population

• In a Census the entire Population is surveyed.• Resources are needed to conduct a Census

• CSO Spends about €80 million to conduct the 5 year Census of Population

• Sometimes the measuring process destroys the thing being measured, e.g. if we were to test the

strength of a weld or in testing an individuals blood - who among us would be willing to donate all of our blood in a test?

• Because of the work involved in a Census it is much faster to conduct a survey, sometimes it is important to have results fast.

Why is Sampling used?

• There are accuracy advantages to be had in conducting a sample survey:– It is easier to get complete coverage of a sample

than of a population. – Easier to train a small number of interviewers for a

sample survey than to train a large number for a census.

• OK but a sample is still a sample and is bound to be inaccurate by its very nature, isn’t it???

• British General Election

Accuracy in Surveys

• We mentioned before that if a sample was chosen to be representative of the target population then it could be very accurate.How accurate?

• For surveys conducted to measure a sample proportion as an estimate for a population proportion we can define a Margin of Error.

• The sample proportion will differ from the population proportion by more than the margin of error less than 5% of the time.

• The Margin of Error for a sample of size n is 1/√n

Accuracy in Sampling

• For Example with a sample of 1600 the margin of error is 1/40 or 2.5%

• So a survey conducted using a sample of size 1600 will be accurate to within 2.5% more than 95% of the time.

Accuracy in Sampling

• We saw already that in order for the sampling procedure to work properly the sample must be representative of the target population

• There are several ways to get a representative sample:

• Simple Random Sampling

• Stratified Random Sampling

• Cluster Sampling

• Quota Sampling

• Systematic Sampling

• Random Digit Dialing

• Multi Stage Sampling

How to choose a Sample

• The simplest form of sampling procedure.

• Each group of individuals has the same chance of getting chosen.

• Therefore each individual has the same chance of being chosen.

• Use Random Number Tables or a random number generator.

• Or put names in a hat or roll a die.

Simple Random Sampling

• Polling companies don’t have a list of all adults and select from that list randomly.

• Instead they use other methods like Stratified Random Sampling

• We first divide the population into different strata, then sample randomly within those strata.

• Example: To conduct an opinion poll we might divide the population into different age groups or sexes or by County of residence.

Stratified Random Sampling

• Advantages of this method are:

• We can get individual estimates for each strata

• We can use different interviewers for each strata and train them appropriately

• If strata are different geographic regions it may be cheaper to sample them separately.

Stratified Random Sampling

• Divide the Population into similar groups or clusters. Then choose a random sample of clusters.

• The analysis of this type of survey is more complicated than for simple random sampling.

• NOTE: This is not the same as Stratified Sampling, in Cluster Sampling the Clusters are chosen so that the resemble each other as much as possible.

Cluster Sampling

• This is where a plan is used to chose the participants in the study.

• For Example: We might decide to survey every 3rd person we meet. Or to choose every 5th House to be surveyed.

• Sometimes this procedure can be very biased.

• What happens if every 5th house is an end house?

Systematic Sampling

Quota Sampling

• We know the demographic characteristics of the population of interest and we ensure that our sample contains the same distribution of demographic traits as the population.

• Over-sampling will be required as we will be forced to exclude respondents once each quota has been achieved.

• Used very much in the US

• Phone numbers in certain area codes are dialled randomly by computer, then when someone answers an interviewer asks questions

Random Digit Dialling

Multi- Stage Sampling

Used for large surveys

Involves using a combination of the methods described above.

• Here are 5 ways to make a mess of the sampling procedure:

• Use the wrong sampling frame

• Fail to reach the individuals selected

• Get no response

• Get a sample of volunteers

• Use a convenient or haphazard sample

• The last 2 of these are disastrous

What can go wrong in Sampling• 1936 US election

• Literary Digest had been extremely successful in predictions

• 1936 predicted 3-2 victory for Rep Landon over Dem. FDR

• George Gallup American Institute of Public Opinion predicted FDR correctly and also predicted what Literary Digest would predict.

• Literary Digest 10 million

• Gallup 50,000

• LD- Magazine Subscribers, Phone Directories, Car Owners - Wealthy

• Most serious though: 23% Volunteer response.

What went wrong in Sampling

Chapter 5

• The Experimental procedure involves manipulating something called the Explanatory Variable and seeing the effect on something called the Outcome Variable or the Response Variable.

• Many times it is hard to establish a clear causal connection between one variable and another, it may be that a third variable is causing both.

• There is an established correlation between the number of fillings in children’s teeth and their vocabulary.

Chapter 5Design of Experiments and Observational Studies

• Does this mean that eating Mars bars increases your vocabulary??

• There may be a third variable causing both of the variables we are studying.

• Ideally we want to create changes in the explanatory variable and then examine the effects on the response variable. This we can only really do in an Experiment.

• In an Observational Study we cannot create changes in the explanatory variable. Instead we observe differences in the explanatory variable and try to see if these are related to changes in the response variable.

• So in an Experiment we have an element of control that we do not have in an Observational Study.

• Why don’t we just do Experiments then?

• Well it may be unethical to perform certain experiments. – Eg To measure the effect of smoking during pregnancy on a

child, it would be unethical to make a random selection of mothers smoke. We could however observe the effects on the children of mothers who already are smoking during pregnancy.

• It may be that we cannot randomly assign some explanatory variables-like baldness, or handedness.

Some Definitions

• A Treatment is one or more explanatory variables assigned by the experimenter.

• A Confounding Variable is one whose effect on the response variable cannot be separated from the effect of the explanatory variable.

• An Interaction occurs when the effect of one explanatory variable depends on what’s happening with another explanatory variable.

• An Experimental Unit is the smallest object to which we can assign different treatments in an experiment.

Some More Definitions

• An Observational Unit is the smallest object which we can observe in an observational study.

• When the Units are people they are called Participants or Subjects.

• Usually these participants are Volunteers.

How to design an Experiment

• Randomisation is the most important element of any experiment. We will discuss different types of randomisation in a little while.

• A Control Group which is treated identically in all respects to the group receiving the treatment except that the members of the control group do not receive the treatment.

• Placebos: There is a proven phenomenon called the placebo effect. Patients receiving Placebo tablets which have no active drug ingredient (eg a sugar tablet) may experience a certain beneficial effect.

• The way to eliminate this Placebo Effect from the experiment is to give Placebo tablets to the control group when administering a tablet to the treatment group.

• Blinding: It is not just in receiving tablets that the “power of suggestion” plays an important role. It is usually best therefore if the subject does not know whether they are receiving the treatment or not. This practice is called Blinding.

• Sometimes it is also best if the experimenter does not know which subject is receiving the treatment and which is not. This will remove any potential bias in the way the experimenter reports his findings.

• Experiments in which both the subject and the experimenter do not know who receives the treatment are called Double Blind.

• Experiments in which either the subject or the experimenter (but not both) do not know who receives the treatment are called Single Blind.

Experimental design

• The design of an experiment is very important. Experiments are designed with the purpose of isolating the effect of the treatment on the response variable and removing any confounding effects.

• One way that we have seen already of removing the effect of any confounding variables is to randomly assign subjects to the treatment or control group. This way any possible bias in the population should be evenly spread among the treatment and control groups.

• Sometimes instead of relying on randomisation to make the groups as even as possible we actually force the groups to be similar.

• Matched Pair designs: These are experimental designs in which either the same individual or two matched individuals are assigned to receive the treatment and the control. In the case where an individual receives both the treatment and the control, the order in which this happens should be random. And the experiment should be conducted as a Double Blind experiment.

• Block Designs: This is an extension of the Matched Pair design to the case of three or more treatments (one may be the control).

• If there are 4 treatments and a control then there will be 5 blocks each one designed to be as similar as possible. 4 of the blocks will each receive one of the treatments and one block will be a control.

Problems with Experiments

• Confounding variables -– These are variables connected to the explanatory variable

which may be the actual cause of the effect on the response variable.

– Cured by Randomisation– Storks

• Interacting variables– A second variable which interacts with the explanatory

variable.– Smoking/Alcohol/Exercise

• Placebo effect

• Hawthorne effect– Participants in an experiment respond differently just

because they are in an experiment.

Problems with Experiments

• Experimenter effect– The experimenter can influence the results of the

experiment. – They may record data incorrectly.– Or inadvertently let the subjects know the desired outcome.– RATS

• Ecological validity and generalisability– Results obtained in a closed experimental setting may not

be applicable in the real world


• Observational studies are not as good as Experiments at establishing causal connections.

• However since no special setting is required they usually do not suffer from the problem of Ecological Validity.

• In addition the Hawthorne and Experimenter effects are not a problem.


• Observational studies are classified as either Retrospective or Prospective Studies

• In Retrospective studies the participants are asked to recall certain past events.

• In Prospective studies the participants are followed by the researcher into the future and events are recorded.

• One particular type of observational study has become very popular

• - The Case Control Study

Case Control Studies

• To try and examine the possibility of a relationship between an explanatory variable and a response variable the researcher selects a group of participants called CASES in which the response variable is already present.

• For example in a study to determine if there is a relationship between baldness and heart attacks, a group of patients in hospital for heart attacks are chosen as the Cases.

• A group who have not had heart attacks are chosen as the Controls.

Case Control Studies

• The Control Group should be chosen to be as similar in all respects as the case group except for the response variable. Why?

• A Case Control study is much more efficient than some other forms of study. Consider first choosing two groups according to whether the explanatory variable was present or not then waiting until the response variable revealed itself. This may take a long time.

• Good at removing confounding variables if controls are chosen appropriately.

Problems with Observational Studies

• Confounding Variables

• Cases and Controls not representative of the population

• Recollections of the Past may not be accurate.

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