Quota Sampling Proportional Quota Sampling — The "proportional" in the name is because the population of interest is represented almost exactly by the percentage of each cell major demographic group in the final survey results.
Study Design and Sampling Study Design Cross-sectional studies are simple in design and are aimed at finding out the prevalence of a phenomenon, problem, attitude or issue by taking a snap-shot or cross-section of the population. This obtains an overall picture as it stands at the time of the study.
For example, a cross-sectional design would be used to assess demographic characteristics or community attitudes. These studies usually involve one contact with the study population and are relatively cheap to undertake.
Such studies are often used to measure the efficacy of a program. These studies can be seen as a variation of the cross-sectional design as they involve two sets of cross-sectional data collection on the same population to determine if a change has occurred. Retrospective studies investigate a phenomenon or issue that has occurred in the past.
Such studies most often involve secondary data collection, based upon data available from previous studies or databases. For example, a retrospective study would be needed to examine the relationship between levels of unemployment and street crime in NYC over the past years.
Prospective studies seek to estimate the likelihood of an event or problem in the future. Thus, these studies attempt to predict what the outcome of an event is to be.
General science experiments are often classified as prospective studies because the experimenter must wait until the experiment runs its course in order to examine the effects. Longitudinal studies follow study subjects over a long period of time with repeated data collection throughout.
Some longitudinal studies last several months, while others can last decades. Most are observational studies that seek to identify a correlation among various factors. Thus, longitudinal studies do not manipulate variables and are not often able to detect causal relationships.
Sample Once the researcher has chosen a hypothesis to test in a study, the next step is to select a pool of participants to be in that study. However, any research project must be able to extend the implications of the findings beyond the participants who actually participated in the study.
For obvious reasons, it is nearly impossible for a researcher to study every person in the population of interest. The researcher must put some careful forethought into exactly how and why a certain group of individuals will be studied. This is also known as random sampling. A researcher can simply use a random number generator to choose participants known as simple random samplingor every nth individual known as systematic sampling can be included.
Researchers also may break their target population into strata, and then apply these techniques within each strata to ensure that they are getting enough participants from each strata to be able to draw conclusions. For example, if there are several ethnic communities in one geographical area that a researcher wishes to study, that researcher might aim to have 30 participants from each group, selected randomly from within the groups, in order to have a good representation of all the relevant groups.
Non-Probability Sampling, or convenience sampling, refers to when researchers take whatever individuals happen to be easiest to access as participants in a study.
This is only done when the processes the researchers are testing are assumed to be so basic and universal that they can be generalized beyond such a narrow sample. Snowball sampling is not a stand-alone tool; the tool is a way of selecting participants and then using other tools, such as interviews or surveys.
Sampling Challenges Because researchers can seldom study the entire population, they must choose a subset of the population, which can result in several types of error.CONVENIENCE SAMPLING - Subjects are selected because they are easily accessible.
This is one of the weakest sampling procedures. An example might be sur. As a researcher, I absolutely love election season.
While I could say that the reason for this is that I am simply living up to my obligations as a citizen (partly true), the real reason I enjoy it so is because of all the polls that are released. 22 Chapter 8: Quantitative Sampling I.
Introduction to Sampling a. The primary goal of sampling is to get a representative sample, or a small collection of units. Module 2: Study Design and Sampling Study Design.
Cross-sectional studies are simple in design and are aimed at finding out the prevalence of a phenomenon, problem, attitude or issue by taking a snap-shot or cross-section of the barnweddingvt.com obtains an overall picture as it stands at the time of the study.
2 Ilker Etikan et al.: Comparison of Convenience Sampling and Purposive Sampling. include every subject because the population is almost finite.
This is the rationale behind using sampling techniques like convenience sampling by most researchers . The following guidelines are provided for submissions reporting case study research aimed at understanding a bounded phenomenon by examining in depth, and in a holistic manner, one or more particular instances of the phenomenon.
Case study research in TESOL and second language acquisition (SLA) has.