Population and samples Part IV Population and samples Measurement and collection of data Data-collection methods

Population: Is a complete set of persons or objects that possess some common characteristic that is of interest to the researcher. Two groups: The target population The accessible population

Target population: The entire group of people or objects to which the researcher wishes to generalize the findings of a study. Target population should meet the criteria of interest to the researcher Example: all people who were admitted to the renal unit for dialysis in Al-Basheer hospital during the period of 2004 - 2005

Accessible population: The group of people or objects that is available to the researcher for a particular study

Element: The single member of the population (population element or population member are used interchangeably Sampling frame is the listing of all elements of a population, i.e., a list of all nursing students in the university of Jordan, 2002-2006

Types of sampling methods: Probability Non probability

Probability: A. Simple Random Sampling B. Stratified Random Sampling Proportionate Disproportionate C. Cluster Random Sampling D. Systematic Random Sampling

Nonprobability sampling: the sample elements are chosen from the population by nonrandom methods. More likely to produce a biased sample than the random sampling. This restricts the generalization of the study findings. Most frequent reasons for use of nonprobability samples involve convenience and the desire to use available subjects.

Convenience sampling Quota sampling Purposive sampling Snowball sampling Quota sampling Purposive sampling

Convenience sampling (Accidental or incidental sampling): People may or may not be typical of the population, no accurate way to determine their representativeness Most frequently used in nursing research Advantages: Saves time and money

Snowball sampling: a method by which the study subjects assist in obtaining other potential subjects (networking) Useful in topics of research where the subjects are reluctant to make their identity known, Drug users, Aids patients, etc.

Quota sampling: Similar to stratified sampling in that the first step involves dividing the population into homogeneous strata and selecting sample elements from each of these strata In Stratified random sampling the sample is done by random selection (a percentage of each strata selected randomly) In quota sampling, the sample is selected by convenience (the first 50% of males and 50% of females) A mean for securing potential subjects from these strata

In a quota sampling variables of interest to the researcher (include subject attributes), such as age, gender, educational background are included in the sample

Purposive sampling (handpicking, judgmental): Subjects are chosen because they are typical or representative of the accessible population, or because they are experts (more knowledgeable) in the field of research topic. Qualitative researchers use Purposive sampling

How large should a sample Be? Sample size: How large should a sample Be? Factors to be considered in deciding the size of the sample: Homogeneity of the population The degree of precision desired by the researcher The type of sampling procedure that will be used

Large sample sizes may be needed in the following instances: 1. Many uncontrolled variables are present. i.e., inability to control for age 2. Small differences are expected in members of the population on the variable of interest 3. The population must be divided into subgroups 4. Dropout rate among subjects is expected to be high 5. Statistical test are used that require minimum sample sizes.

Sampling error and sampling bias: Sampling error: the difference between data obtained from a random sample and the data obtained that would be obtained if an entire population were measured. Error is not under the researcher’s control and caused by chance

Example of sampling error: Average pulse rates of a group of cardiac patients: 66 80 59 70 71 71 63 70 74 55 70 65 76 92 83 67 79 66 80 72 µ = 71 Random Sample #1 66 59 70 55 66 = 63 Random Sample # 2 80 92 83 79 80 = 8 3 Random Sample # 3 71 71 70 64 67 = 71

Sampling bias: is the bias that is caused by the researcher when the samples are not carefully selected (not a matter of chance) Example: selection from the telephone directory but this record has some people missing form the register for some reasons