Ssci Ch 7 Quiz

Drawing a judgmental sample allows researchers to use their prior knowledge about the population. This means that researchers can select individuals for the sample based on their expertise and understanding of the population being studied. It allows them to choose participants who they believe will provide valuable insights and information relevant to their research objectives. By drawing a judgmental sample, researchers can leverage their knowledge and expertise to ensure that the sample represents the characteristics and diversity of the population accurately.

If a sampling interval of 5 was used to select a sample from a population of 1000, it means that every 5th element from the population was selected. To find the number of elements in the sample, we divide the total population size (1000) by the sampling interval (5). Therefore, the sample will consist of 200 elements.

Snowball sampling is a method where participants are initially identified through a few key informants, and then these participants are asked to refer other potential participants. This method is useful when studying hard-to-reach populations or when little information is known about the target group. In this scenario, the field researcher starts by interviewing a recent recruit and asks about the person who introduced them to the organization. Then, the researcher interviews the person named and continues the process, creating a snowball effect. This allows the researcher to gain insights into the organization's pattern of recruitment over time.

Stratified sampling requires prior knowledge about the elements in the population before drawing the sample. This means that researchers need to have information about the population and its characteristics in order to divide it into strata. This can be a disadvantage because it may not always be possible to have accurate or complete prior knowledge about the population. In some cases, this requirement may limit the use of stratified sampling and make it less feasible compared to other sampling methods.

Stratified sampling would be an appropriate strategy in this case because it allows for the selection of cases from each of the personnel categories in the hospital. By dividing the population into different categories (orderlies, technicians, nurses, and doctors) and then randomly selecting individuals from each category, the researcher can ensure that the sample represents the diversity of personnel in the hospital. This method increases the likelihood of obtaining a representative sample and allows for more accurate generalizations about the entire population of hospital personnel.

Quota sampling would be the recommended sampling scheme in this case. This method allows the researcher to ensure that the sample includes individuals from different age, racial, and sex categories. With quota sampling, the researcher sets specific quotas for each category and then selects participants who meet those quotas until they have a representative sample. This approach helps to ensure diversity and representation within the sample, making it suitable for Professor Hall's field study of hitchhikers.

Dr. Chang is using stratified sampling as her sampling strategy. Stratified sampling involves dividing the population into different subgroups or strata based on certain characteristics (in this case, grade level) and then randomly selecting participants from each subgroup. By ensuring an equal number of students from each grade level, Dr. Chang is able to obtain a representative sample that accurately reflects the overall population of undergraduate students at her college.

The correct answer is "element." In research or data analysis, an element refers to the individual unit or object from which data is collected. It could be a person, a group, an organization, or any other entity that is being studied or analyzed. The element provides the basis for analysis because it is the specific unit about which information is collected and on which statistical measures are calculated.

The statement "Telephone directories are an excellent listing of a city’s population" would mislead Tammy because telephone directories do not include cell phone numbers, which means they do not represent the entire population accurately.

The given statement that "The terms informant and respondent are interchangeable" is false. While informants and respondents are both used in research, they refer to different roles. An informant is a member of the group being studied who provides information and insights to the researcher. On the other hand, a respondent is someone who responds to specific questions or surveys in a research study. These terms cannot be used interchangeably as they represent distinct roles in the research process.

Systematic sampling is a sampling technique where every nth element is selected from a population. This method is used when the population has a natural order or sequence. By selecting elements in a systematic manner, systematic sampling ensures that the sample is representative of the entire population. Periodicity, which refers to the presence of a repeating pattern or cycle, can introduce bias in systematic sampling if it aligns with the sampling interval. Therefore, understanding periodicity is crucial in systematic sampling to avoid any skewness in the sample and ensure accurate representation of the population.

The calculated standard error of 3 percent indicates the level of uncertainty in the estimate. In statistics, a standard error represents the variability or margin of error around the estimated value. A standard error of 3 percent means that the true value is likely to fall within 3 percent of the estimated value. Since the estimated value is 40 percent, we can be 68% certain that the true value falls within the range of 37% to 43%. Therefore, the answer is "you are 68% certain that between 37% and 43% of the employees believe the president is doing a good job."

Simple random sampling is a method of selecting a sample from a population in which every individual has an equal chance of being chosen. This method ensures that the sample is representative of the population and reduces the risk of bias. In statistical computations, simple random sampling is often assumed because it provides a reliable and unbiased estimate of population parameters. Other sampling methods, such as systematic sampling, cluster sampling, and stratified sampling, may also be used in specific situations, but the assumption of simple random sampling is generally made in statistical computations.

The standard error is valuable because it indicates the extent to which the sample estimates will be distributed around the population parameter. This means that it provides an understanding of the precision of the sample estimates and how closely they reflect the true population parameter. By knowing the standard error, researchers can assess the reliability of their findings and make more accurate inferences about the population based on the sample data.

Disproportionate sampling is used by researchers to ensure a sufficient number of cases from strata of varying sizes. This sampling method allows for intentionally oversampling smaller strata and undersampling larger strata, which helps to obtain a more accurate representation of the population. By deliberately selecting more cases from smaller strata, researchers can ensure that these groups are adequately represented in the sample, even if they are underrepresented in the population. This approach allows for a more comprehensive analysis and understanding of the population under study.