Week 1 – Lesson 2

Background Week 1 Lesson 2 is a continuation of the week 1 discussion on the introductory

concepts to Biostatistics. Topics this Week Populations and Samples Sampling and Sampling Techniques Key Concepts Statistics and Statistic Sampling Techniques, Sample / Subjects and Population Probability Sampling Non- Probability Sampling Advantages and Disadvantages in using Probability Sampling Methods Advantages and Disadvantages in using Non -Probability Sampling

Methods Read pages 15-21 of your textbook.

Activities

Textbook Reading

Activity 1 With the assigned topic/s to your group, find at least one very good internet site or Url that you think is useful to that topic/s. Discuss in no more than 2 sentences why it is useful; what important lessons will you get from the Url site. Activity 2 Identify at least one lesson you have learned in this week’s discussion and in brief discuss its importance with your groupmates. Activity 3 Discuss your thoughts on the use of probability and non- probability sampling techniques and its implications to research.

Journal Activity

Submit your initial thoughts regarding your research journal paper in relation to the topics we have discussed so far.

Statistics and Statistic Parameter: A parameter is a characteristic of the whole population. Statistic: A statistic is a characteristic of a sample, presumably measurable.

Remember: Parameter is to Population as Statistic is to Sample.

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Week 1 – Lesson 2

e.g. Assume there are 30 patients in the CVU (Cardio Vascular Unit), with 7 who just underwent bypass surgery. Since 7 is 23% of 30, we can say 23% underwent bypass surgery. The 23% represents a parameter (not a statistic) of the patients because it is based on the entire population. If we assume this patients is representative of all patients with heart failures who underwent bypass, and we treat these 7 patients as a sample drawn from a larger population, then the 23% becomes a statistic.

Sampling Techniques, Sample / Subjects and Population Sample and population can be best described through the Venn Diagram.

S

U

The universal set is the population, which signifies the totality of all the characteristics, while subset s is the sample or the representative of the population. Samples may be called subjects, as in “subjects” of the study. Sampling Technique is the process of drawing a representative called “sample” from a group of characteristics called “population”. Sampling can be done by identifying first whether your target or required sample is probable or non – probable.

Types of Samples Some books defines probable sample as random sample while non- probable as non- random sample.

Probable/ Random Non – Probable/ Non-Random A sample is probable if every individual in a population has a known chance (or probability) of being selected as a sample. Thus, in probability sampling, statistical

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Week 1 – Lesson 2

influences can be made to the larger population. On the other hand, it is non – probable when it is not. Methods of gathering probable and non – probable samples are presented in the table below.

Probable Sampling/ Random

Non – Probable Sampling/ Non-Random

• Simple Random • Quota and Dimensional • Systematic • Convenience • Stratified • Purposive • Cluster • Multi - stage

Probability Sampling • In random sampling, every individual has an equal chance of selection. • Systematic samples are selected from every nth person. Every individual

does not have an equal chance of selection. It may create biases if individuals are clustered together as they would be by surname in a telephone directory. However, this as efficient as random sampling.

• Stratified sampling can be used to ensure adequate coverage of sub – populations, where the number of strata defends on the number of sub – populations for study. For example: gender, occupation, education and length of residence.

In stratified sampling, the number of sample selected is proportional to their number in total population. However, in some cases, disproportional numbers maybe used to ensure better coverage.

• Cluster sampling is used to save time and travel costs when population is widely scattered, difficult to interview, or where the total population is not known.

Cluster sampling can be done singly or thru multi- stage. Single stage sampling divides the population into a large number of groups or clusters, which are selected by random or systematic method.

• Multi – stage sampling is used only if there is no alternative. Among the probability approaches, this is the least reliable. Example of this would be when a number of census enumeration areas are selected at random, followed by a random selection of barangays within the census areas. Random selection within each barangay must also be done.

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Week 1 – Lesson 2

Non- Probability Sampling In non – probability sampling, we do not know the total population, and as a result we have no way of knowing how typical or valid our population is. Statistical tests of significance are therefore inappropriate.

• Quota sampling – Here, the respondents are selected according to the interest of the researcher; selected by convenience.

• Dimensional sampling – is an extension of quota sampling. Group sizes are proportional to their assumed numbers in the population.

• Convenience sampling – where the interviewer interviews whoever is convenient. It may enable the researchers to get a “feel” for the issue they are researching but it has no other valid use.

In summary, the advantages and disadvantages of using these methods are presented in the following tables.

Advantages and Disadvantages in using Probability Sampling Methods:

Method Advantages Disadvantages • Simple Random

• Requires little advance knowledge of µ.

• Easy to analyze

• Sample frame (number of sample) is needed.

• Does not use researcher’s knowledge of the µ.

• Involves high cost in the implementation.

•

Systematic • Ensures good spread of

respondents. • May allow smaller sample

size than simple random.

• Sampling interval can introduce bias if individuals or samples are clustered.

• Stratified • As for systematic • Knowledge of stratum proportions needed.

• Cluster • Easy to use. • Low Cost.

• Possibility of large sampling error if duplication or omission is done.

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Advantages and Disadvantages in using Non -Probability Sampling Methods:

Method Advantages Disadvantages • Quota and

Dimensional • No µ is required. • Some stratification of µ

may occur.

• There is possibility of bias in the classification chosen.

• Statistical tests are inappropriate.

• Convenience • Easy to use. • Low Cost. • No use for any knowledge

of µ size, or characteristics.

• No control on biases. • Statistical tests are

inappropriate.

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