Lesson Two: Sampling Procedures

Population and Sampling

Population - an entire group of persons or elements that have at least one thing in common (Minnesota fourth graders, Argosy University graduate students).

Sample - a small group of persons or elements (observations) selected from the total population.

We want the sample to be representative of the population.

Descriptive and Inferential Statistics

Descriptive statistics are a way of summarizing data - letting one number stand for a group of numbers. We can also use tables and graphs to summarize data. Descriptive statistics are used to reveal patterns through the analysis of numeric data. The same statistic (number) can be either descriptive or inferential, it depends on how we are using the statistic.Inferential statistics are used to draw conclusions and make predictions based on the analysis of numeric data.


Parameter and statistic

Parameter - a parameter is a characteristic of a population.

Statistic - a statistic is a characteristic of a sample.

The mean of a sample would be a statistic. The mean of a population would be a parameter.

Sampling Methods

Sampling methods are methods for selecting a sample from the population.

Simple random sampling - In simple random sampling, there is an equal chance for each member of the population to be selected for the sample. You could do simple random sampling by throwing all the names of the population members in a hat and randomly select a sample from the hat.

Systematic sampling - Systematic sampling is the process of selecting every nth member of the population arranged in a list. For example you could take every 10th member of a list of people (the population) arranged alphabetically.

Stratified sample - A stratified sample is obtained by dividing the population into subgroups and then randomly selecting from each of the subgroups. The number of units selected from each subgroup can be proportional to the groups number in the population or can be equal-sized among the subgroups.

Cluster sampling - In cluster sampling groups are selected rather than individuals. For example select 5 elementary schools from among the 25 elementary schools in the district.
Incidental or convenience sampling - Incidental or convenience sampling is taking an intact group (e.g. your own forth grade class of pupils) and using this group to represent the population (e.g. all fourth grade students in your state, province, or country). This is not really sampling at all and there are severe problems in generalizing the results from your sample to the population in incidental or convenience sampling.

Sampling biase and sample size

Sampling biase - Sampling biase is caused by systematic errors in the sampling process. For example, you want to take one-forth of your students as a sample to use in a research study, so you send out notes to the parents requesting permission for their child to participate in the study and then select those students whose parents give permission first as the sample for the study.

Sample size - In general, the larger the sample size, the more representative it is of the population.

Gathering and coding data

When gathering and coding data (preparing data for analysis) data collection must be accurate, where tests are used, they must be scored correctly, and observations must be made systematically.

In some cases data may be coded, for example the sex of subjects might be coded with males as 1 and females as 2.An electronic spreadsheet, such as Microsoft Excel, provides an excellent place to keep the data for your study (both raw data and coded data). The spreadsheet, as you will find in later lessons, can also be used to calculate descriptive and inferential statistics on your data

Creating Rubrics

Checklists to support Project Based Learning and evaluation