1 Research Methods Winter 2006 Winter 2006 Chapter 4 – Samples & Populations- Choosing or what to Study Instructor: Dr. Harry Webster

2 Where Do Data Come From? Where Do Data Come From? Anything that we can put into a number is data. Good data requires valid measurements; an appropriate way to investigate the topic. Types of investigations in Social Science and Commerce that usually have data are:

3 1. Observational Study: Uses predetermined categories (target behaviors/events) and observes frequency. Seeks to describe behavior or event. Ex., observe children for helpful behavior. Have target behaviors identified before beginning observation. Define precisely helpful behavior. Ex., p. 7 textbook. Compared proximity of residence to power lines for children with leukemia to those without leukemia.

4 2. Survey Studies: Offer a series of questions usually to a large number of people. Ex., ability and attitude/opinion questionnaires. Looks at how many people gave a certain answer (frequency & percents). 2a) Sample surveys: A survey where much attention is given to securing the sample in a random manner where, as much as possible, everyone has an equal chance of being picked. Ex., public opinion polls, marketing research.

5 3. Census Tries to gather data from everyone in a country. Underestimates the homeless and some minority groups. Governments do this to establish voting districts, economic and social trends. For Canadian CensusCanadian Census 4. Experiments esearcher changes Researcher changes one thing (independent variable, IV; called treatment in textbook); keeps everything else the same, and determines if behavior (dependent variable, DV) is affected.

6 Basic design is two groups where one group gets one level of the IV and the other gets another level of the IV (this includes its absence). Since everything else kept constant, then any change in behavior (DV) is due to the change in the IV. Can make cause and effect (what causes what) statements with experiments. Cause and effect statements can only be made about groups; not individuals. They are probable causes not certain ones (remember: statistics is about probability not certainty).

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8 Ex., Does a tutor impact academic performance? Group ANo tutor in RM Group BA tutor in RM Both groups are otherwise the same. Compare their grades in RM.

9 Vocabulary Vocabulary Individual: anything that is measured; includes people, objects, animals. The ‘thing’ that provides the measurement. Ex., person Variable: any characteristic of an individual; anything that we actually measure. Ex., height, weight, schooling, singing ability.

10 Population: The entire group of individuals about which we want information. Ex, students, Canadians, Montrealers, workers, dads. Sample: The part of the population that we actually measure on a variable. Too time- consuming, often too costly, & difficult to measure the whole population. Ex., samples of 100 students, 1000 Canadians, 300 Montrealers, 500 dads are more easily measured than the entire populations.

11 Exercises Chapter 1 1. Fewer women than men vote for the PQ. A political scientist interviewed 400 male and female voters and asked how they voted in the last election. a) Is this an experiment? b) What is the population? c) What is the sample? d) What is the individual? e) What is the variable under study?

12 2. Identify the individual, variable, population, sample and type of study: Researchers investigated how many homeless people have chronic diseases by interviewing 100 homeless in the downtown Montreal area. Individual: Population: Sample: Type of Study:

13 3. As a researcher you are interested in whether students who sleep one hour more per night would improve their academic performance. Design an experiment to investigate this. Be sure to identify the independent variable, the dependent variable, the population and the sample.

14 Chapter 4 - continued Chapter 4 - continued Samples, Good and Bad Samples, Good and Bad Probability Sample Any sample that is chosen by chance. Includes Simple Random Sample (SRS) and Stratified Samples. 1. Simple Random Sample Gives each individual in the population an equal chance of being chosen.

15 Use table of random digits (handout) or computer program to choose individuals or samples. Random: every individual or sample has an equal chance of being picked. This ensures the sample is representative of the population. Therefore, we can generalize the results back to the population.

16 Choose a Simple Random Sample (SRS) Step 1. Assign a number to every individual in the population (or to every sample). Assign numbers beginning with 0 and keep going until everyone has a number. Go down columns of lists. Step 2. Use random digits to select the numbers and choose those people.

17 The first two lines of the Random Digit Table p 550: Line

18 Ex., Choose a Simple Random Sample of 4 from the following past players of the Canadiens. (number down beginning with 0). Begin at line 101. Ribeiro BriseboisMarkov SouraySundstromHainsey RyderRivetHossa ZednikDackellGratton KoivuBouillonLangdon Perreault KilgerTheodore BulisQuintalDwyer Dagenais WardHiggins JuneauBeginPlekanec GaronKomisarek

19 The sample of 4 is: Komisarek (19); Hossa (22); Perreault (05); Dackell (13).

college students, evenly divided per gender, were asked about part-time work. 850 said they worked at least 12 hours per week. a) What is the sample? b) What is the population?

21 2. A newspaper presents a survey where 500 of 1100 people coming out of the cinema said they went to see an action movie. a) What is the sample? b) What is the population?

22 Chapter 4 Chapter 4 Sample Surveys in the Real World Sample Surveys in the Real World How sample surveys go wrong: 1. Sampling errors: 1. Sampling errors: errors that occur as we secure the sample causing the sample to be different from the population. Biased samples. a) convenience sampling: a) convenience sampling: grab them where you can. ex., Malls, stores. b) voluntary/self initiated response sample: b) voluntary/self initiated response sample: interested people decide to participate given a public request. ex., Call in votes; internet polls.

23 2. Random sampling errors: 2. Random sampling errors: difference between the sample result (statistic) and the population result (parameter) caused by chance in selecting the random sample. Ex., margin of error mentioned in Box 4.1 on page 80 and pp 96 to 99 in the text.