Basic concepts Population Is the entire aggregation of

Basic concepts • Population: Is the entire aggregation of cases that meet a designated set of criteria. Examples: 1. All the women in Gaza strip who gave birth to a live baby during the past decay. 2. All the women older than age 60 whom are under psychological care. 3. All the children in Gaza strip with cystic fibrosis. Population may be human subjects and might consist of hospital records, all of the blood samples taken from clients

Basic concepts • Accessible population: Is the population of subjects available for particular study. "it is the aggregate of cases that conform to the designated criteria and that are accessible to the research as a pool of subjects of a study”. The sample is obtained from the accessible population, and findings are generalized first to the accessible population and then, to the target population.

Basic concepts • The target population: Is the total group of subjects about whom the investigator is interested and whom the results could be reasonably generalized.

Basic concepts • The target population: Example 1: all RNs currently employed in the Gaza Strip is the target population, but the more modest accessible population is RNs working in Gaza city. Example 2: A target population might consist of all diabetic people in the Gaza Strip, but the accessible population might consist of all diabetic people who are members of a UNRWA clinic.

Sampling • Sampling - refers to process of selecting a portion of the population to represent the entire population • A sample consists of a subset of the units that compose the population. Population Sample

Sampling • Sampling determines who will be participants in the study. Aim • A representative sample – A sample which accurately reflects its population • Avoiding bias

Why Use A Sample? • Ideally the whole population should be used in a study. • Why would this be impractical in most studies? • When would it be feasible to use the whole population?

Representative Sample • It is that sample whose key characteristics are highly similar to those in the population from it is drawn • It is important that the sample not be biased. • A representative sample is a sample which is a true cross-section of the population you are measuring.

How Do We Generalize? Model I: Sampling Population Sample draw sample

How Do We Generalize? Model I: Sampling generalize back Population Sample

Generalization • If the sample is representative of the population then it is possible to generalize the findings from the study. • If you can generalize you can say that the results hold true not only for the sample that you studied but the entire population from which the sample was derived.

Basic terminology • Population - the entire group of objects about which information is wanted • Target Population - the group that is the focus of your research • Accessible Population - members of the population that you can reach • Unit - any individual member of the population • Sample - a part or subset of the population used to gain information about the whole • Sampling frame - the list of units from which the sample is chosen • Variable - a characteristic of a unit, to be measured for those units in the sample

General population Target Population Sample Statistic Gen Sample era liza tion Accessible population.

Step 1: Identify the Population • The units of analysis about whom or which you want to know – Define the population concretely • Example – All registered staff who were working at Shifa Hospital at the time of the study.

2. Specify the eligibility criteria • Inclusion criteria – Registered staff who were working at Shifa Hospital at the time of the study. • Exclusion criteria – Not formally staff as (pocket money) or staff who were in maternity leave. – Registered staff who moved outside Gaza for long period such as vacation, education, and training purposes.

3. Specify the Sampling plan Once the accessible population has been identified, you must decide: 1 - The method of drawing the sample. 2 - How large it will be?

4 - Recruit the sample • Institutional or Agency permission. • Try to know the subjects cooperation.

Probability (Random) Sampling • Every element in the population has a known chance of being selected. • No subject can be selected more than once in a single sample. • There are four major types of probability sampling: – Simple Random Sampling. – Systematic Random Sampling. – Stratified Random Sampling. – Cluster Sampling.

How to choose The nature of the research problem Money Availability of a sampling frame Desired level of accuracy Data collection method

Sampling Strategies--Decision Flow Study Population Entire Population Sample “ 100% Sample” or single case study? Probability (Random) Sample Simple Random Sample Systematic Random Sample Stratified Random Sample Determine sample size Cluster Sampling Quota Sample Non-probability Sample Purposive Sample Snowball Sample Convenience Sample

Simple random sampling • Is the most basic probability sampling design, because the more complex probability sampling design incorporate features of simple random sampling. • Obtain a complete sampling frame • Give each case a unique number starting with one • Decide on the required sample size • Select that many numbers from a table of random numbers • Select the cases which correspond to the randomly chosen numbers

Simple random sampling – Advantages • High probability of achieving a representative sample • Meets assumptions of many statistical procedures. • Easy to analyze data and computer errors. – Disadvantages • Identification of all members of the population can be difficult • Contacting all members of the sample can be difficult • Simple random is a time consuming chores especially if the population is large. • Expensive.

Systematic random sampling • Involves taking the list of elements and choosing every n/th element on the list. • Sample interval – divide the population size by the desired sample size K= N/n – e. g sample interval = 40, 000/200 means that we select one person for every 200 in the population • The sample interval is the standard distance between the elements chosen for the sample. • The first element should be selected randomly

Systematic random sampling – Advantage • very easily done – Disadvantages • Some members of the population don’t have an equal chance of being included. If every 10 th nurse listed in nursing personnel list were a head nurse and a sampling interval was 10, then head nurses would either always or never be included in the sample.

Stratified random sampling • The elements are divided into two or more strata or subgroups • The aim of stratification is to obtain a greater degree of representativeness • The population is subdivided into homogenous subsets, e. g. age, gender, occupation … • Proportional stratified sampling: e. g. 10% of black students, 10% of Hispanic and 80% white students, then the proportional stratified sample of 100 students will be 10, 5, 85 from the respective sub-population. • Non-proportional stratified sampling (weighing): 20% + 20%+60%

stratified random sampling – Advantage • representation of subgroups in the sample – Disadvantages • Identifying members of all subgroups can be difficult • Require more labor and effort

Cluster sampling • Involves drawing several different samples – draw a sample of areas – start with large areas then progressively sample smaller areas within the larger • Divide city into districts - select Simple Random Selection (SRS) sample of districts • Divide sample of districts into blocks - select SRS sample of blocks • Draw list of households in each block - select SRS sample of households

Cluster sampling example Population: all clinics in the district provided MCH services One begins with the largest, most inclusive units (such as governorates; moving on to less inclusive units as cities then MCH clinics and then to the most basic units, e. g. pregnant women. 1 3 5 9 3 2 4 6 7 5 10 7 8 10 Sample: a random sample of clinics.

Cluster Random Sampling – Advantages • Very useful when populations are large and spread over a large geographic region • Convenient and practical – Disadvantages • Representation is likely to become an issue • Assumptions of some statistical procedures can be violated

Random Samples • Advantages – Ability to generalise from sample to population using statistical techniques • Inferential statistics – High probability that sample generally representative of the population on variables of interest

Nonprobability sampling • In this method of sampling the researcher purposively picks the elements that are information rich. • This is used for interpretive studies. Under what circumstances would you use this method? • Depends on the population • Problem and aims of the research • Existence of sampling frame

Methods of Nonprobability Sampling Quota sampling: A form of non-probability sampling It is one in which the researcher identifies "homogenous" strata of the population and determine the proportion of elements needed from the various segment of the population Purposive sampling "judgmental": When the researcher attempts to ensure that specific elements are included in the sample This approach employs a high degree of selectivity regarding the necessary characteristics of the desired sample

Methods of Nonprobability Sampling Snowball: initial participants lead to other participants. is another type of convenience sampling. This approach is sometimes used when specific traits are needed but difficult to be identified by ordinary means. Suppose that researcher is interested in studying mothers who had stopped breast feeding their infants within one month of being released from hospital Accidental or convenience : Involves the use of convenient, or available, elements in the sample. It is considered a poor approach to sampling because it provides little opportunity to control for biases. – In convenience sampling, subjects are included in the study because they happened to be in the right place at the right time.

Sample Size • There is no simple formula that indicate how large a sample is needed in a given study • Use the largest sample size possible; the larger the sample, the more representative of the population it is likely to be • The larger the sample, the smaller the sample error. The size of the sample depends on three general items: • The research approach being used. • The homogeneity, or similarity, among the different elements. • The resources available to you. What is practical?

Sampling error • Refers to differences between population values, e. g. average of population and the sample value, such as the average of the sample • A margin of error of 5% means that the actual findings could vary in either direction by as much as 5%.

Conclusion • The purpose of sampling is to select a set of elements from the population in such a way that we learn about the sample can be generalised to the population from which it was selected • The sampling method used determines the generalizability of findings Random samples Non-random sample