Variables, sampling, and sample size

Overview

Variables Types of variables Sampling Types of samples Why specific sampling methods are

used

Variable

Anything which varies and can be measured Variables differ according to definition

Categories __________ Continuous _________

Can you think of any examples ?

Specifically, variables represent persons or objects that can be manipulated, controlled, or merely measured for the sake of research.

Variation: How much a variable varies. Those with little variation are called constants.

Independent Variable

Independent: influences the dependent variable

more or less controlled.

researchers manipulate these variables

Often there are many in a given study.

Dependent variable

not controlled or manipulated, but are measured

These vary in relation to the independent variables, and while results can be predicted, the data is always measured.

There can be any number of dependent variables, but usually there is one to isolate reason for variation.

Independent V. Dependent Intentionally

manipulated Controlled Vary at known rate Cause

Intentionally left alone

Measured Vary at unknown

rate Effect

Measurement of variables

Nominal (qualitative, category or categorical variable)

Two or more named categories

Dichotomous – friend/anyone else Multinominal – more than two

Quantitative variables

Numbers or values are assigned to each person or case represent increasing levels of variables

Social class – lower =1, middle = 2, upper = 3

Higher values = higher social class

Example

Students of different ages were given the same jigsaw puzzle to put together. They were timed to see how long it took to complete the puzzle.

What was the independent variable?

Ages of the students Different ages were tested by the scientist

What was the dependent variable?

The time taken to put the puzzle together The time was observed and measured by

the scientist

What was a controlled variable?

Same puzzle All of the participants were tested with the

same puzzle. It would not have been a fair test if some

had an easy 30 piece puzzle and some had a harder 500 piece puzzle.

Universalism

Is human behaviour the same ?

For all peopleFor all culturesFor all societies

Can Psychologists really make generalisations about human behaviour

Representative and Convenience samples

How big should our sample be How should we pick our sample

If done effectively both help psychologists

to generalise their findings

How and Why Do Samples Work?Sample

a small collection of units taken from a larger collection.

Population a larger collection of units from which a sample is taken.

Random sample a sample drawn in which a random process is used to select units from a population These are best to get an accurate

representation of the population But are difficult to conduct.

How and Why Do Samples Work?

Four Types Of Non-Random Samples

Convenience sampling (opportunistic) Participants are picked due to availability Does not include the whole population as

potential participants Stopping shoppers (not a true random sample!)

Quota sample Pre-set categories that are characteristics of the

population (gender / age) e.g. 20 1st years (10 male – 10 female)

Four Types Of Non-Random Samples

Purposive (Judgmental) sampling Researcher picks subjectively and tries to include a range between extremes.

Snowball (network) sampling

Based on connections in a pre-existing network i.e. contact a few vegetarians, then ask if they know other vegetarians

Four Types Of Non-Random Samples

Coming to Conclusions about Large Populations

Sampling element a case or unit of analysis of the population that can be selected for a sample.

Universe the broad group to whom you wish to generalize your theoretical results.

Population a collection of elements from which you draw a sample.

Coming to Conclusions about Large Populations

Target population the specific population that you used.

Sampling frame a specific list of sampling elements in the target population.

Population parameter any characteristic of the entire population that you estimate from a sample.

Coming to Conclusions about Large Populations

Sampling ratio the ratio of the sample size to the size of the target population.

Why Use a Random Sample? Representation.

mathematical or mechanical.Allow calculation of probability of outcomes

with great precision.

sampling ratiothe ratio of the sample size to the size of the target population.

Sampling error the degree to which a sample deviates from a population.

Coming to Conclusions about Large Populations

Coming to Conclusions about Large Populations

Types of Random Samples Simple Random Samples

random number table or computer

Sampling distribution A plot of many random samples, with a sample characteristic across the bottom and the number of samples indicated along the side.

Coming to Conclusions about Large PopulationsTypes of Random Samples

Systematic Sampling 7000/100= 70 (every 70th student on

the list) Rnd(70)= any number between 1-70 Every 70th student after that

Coming to Conclusions about Large Populations

Types of Random Samples Stratified Sampling a type of random sampling in which a

random sample is draw from multiple sampling frames, each for a part of the population.

Coming to Conclusions about Large Populations

Coming to Conclusions about Large Populations

Types of Random Samples Cluster (multi-stage) sampling

a multi-stage sampling method, in which clusters are randomly sampled, then a random sample of elements is taken from sampled clusters.

e.g.3 schools picked, 33 pupils randomly selected from each (cluster again – year groups)

Coming to Conclusions about Large Populations

Coming to Conclusions about Large Populations

Three Specialized Sampling Techniques

Random Digit DialingComputer based random sampling of telephone numbers.

Within Household Samples Random sampling from within households.

Sampling Hidden Populations Hidden Population

A group that is very difficult to locate and may not want to be found, and therefore, are difficult to sample.

Inferences from A Sample to A Population

How to Reduce Sampling Errors the larger the sample size, the smaller the

sampling error. the greater the homogeneity (or the less the

diversity), the smaller its sampling error.

How Large Should My Sample Be? the smaller the population, the bigger the

sampling ratio must be for an accurate sample. as populations increase to over 250,000,

sample size no longer needs to increase.