COLLECTING QUANTITATIVE DATA: Sampling and Data collection

It involves the following five steps The process of collecting quantitative data consists of more than simply collecting data. It involves the following five steps determining the participants to study, obtaining permissions needed from several individuals and organizations, considering what types of information to collect from several sources available to the quantitative research, locating and selecting instruments to use that will net useful data for the study, and finally, administering the data collection process to collect data.

WHAT PARTICIPANTS WILL YOU STUDY? These decisions require that you decide on a unit of analysis, the group and individuals you will study, the procedure for selecting these individuals, and assessing the numbers of people needed for your data analysis.

Identify Your Unit of Analysis Who can supply the information that you will use to answer your quantitative research questions or hypotheses? Some possibilities might be individuals, households, organization, community, state, country etc

Specify the Population and Sample you need to consider what individuals or organization or community you will study. select those who are representative of the entire group. Representative refers to the selection of individuals from a sample of a population such that the individuals selected are typical of the population under study, enabling you to draw conclusions from the sample about the population as a whole.

A population is a group of individuals who have the same characteristic. A target population(or the sampling frame) is a group of individuals (or a group of organizations) with some common defining characteristic that the researcher can identify and study. A sample is a subgroup of the target population that the researcher plans to study for generalizing about the target population. In an ideal situation, you can select a sample of individuals who are representative of the entire population.

Probabilistic and Non probabilistic Sampling Researchers employ either probability or non probability sampling approaches. several types of both approaches are available. Researchers decide which type of sampling to use in their study based on such factors as the amount of rigor they seek for their studies, the characteristics of the target population, and the availability of participants.

probability sampling In probability sampling, the researcher selects individuals from the population who are representative of that population. This is the most rigorous form of sampling in quantitative research because the investigator can claim that the sample is representative of the population and, as such, can make generalizations to the population.

1. Simple Random Sampling The most popular and rigorous form of probability sampling from a population is simple random sampling. In simple random sampling, the researcher selects participants (or units, such as households) for the sample so that any individual has an equal probability of being selected from the population. The intent of simple random sampling is to choose individuals to be sampled who will be representative of the population. Any bias in the population will be equally distributed among the people chosen.

The typical procedure used in simple random sampling is to assign a number to each individual (or site) in the population and then use a random numbers table, available in many statistics books, to select the individuals (or sites) for the sample. For this procedure, you need a list of members in the target population and a number must be assigned to each individual.

2. Systematic Sampling A slight variation of the simple random sampling procedure is to use systematic sampling. In this procedure, you choose every nth individual or site in the population until you reach your desired sample size. This procedure is not as precise and rigorous as using the random numbers table, but it may be more convenient because individuals do not have to be numbered and it does not require a random numbers table.

3. Stratified Sampling In stratified sampling, researchers divide (stratify) the population on some specific characteristic (e.g., gender) and then, using simple random sampling, sample from each subgroup (stratum) of the population (e.g., females and males). It guarantees that the sample will include specific characteristics that the researcher wants included in the sample.

Stratification ensures that the stratum desired (females) will be represented in the sample in proportion to that existence in the population. Stratification is also used when a simple random sampling procedure would yield fewer participants in a specific category (e.g., females) than you need for rigorous statistical analysis.

The procedure for selecting a stratified random sample consists of dividing the population by the stratum (e.g., men and women) and sampling within each group in the stratum (e.g., women first and then men) so that the individuals selected are proportional to their representation in the total population.

4. Multistage Cluster Sampling In multistage cluster sampling, the researcher chooses a sample in two or more stages because either the researchers cannot easily identify the population or the population is extremely large. If this is the case, it can be difficult to obtain a complete list of the members of the population. However, getting a complete list of groups or clusters in the population might be possible

Non probability sampling It is not always possible to use probability sampling In non probability sampling researcher selects individuals because they are available, convenient, and represent some characteristic the investigator seeks to study. In some situations, you may need to involve participants who volunteer and who agree to be studied. Further, you may not be interested in generalizing findings to a population, but only in describing a small group of participants in a study. Researchers use two popular approaches in non probability sampling: convenience and snowball sampling approaches.

1. Convenience Sampling In convenience sampling the researcher selects participants because they are willing and available to be studied. In this case, the researcher cannot say with confidence that the individuals are representative of the population. However, the sample can provide useful information for answering questions and hypotheses.

2, Snowball Sampling In this case, the researcher asks participants to identify others to become members of the sample. Sample Size When selecting participants for a study, it is important to determine the size of the sample you will need. A general rule of thumb is to select as large a sample as possible from the population. The larger the sample, the less the potential error is that the sample will be different from the population. This difference between the sample estimate and the true population score is called sampling error.

END!