Descriptive Designs Can Be Classified Based On The Number Of ✓ Solved

Descriptive Designs Can Be Classified Based On The Number Of Subjects

Descriptive designs can be classified based on the number of subjects, time dimension, and description of a phenomenon (Houser, 2016). Descriptive statistics are very important because if we simply presented our raw data it would be hard to visualize what the data was showing, especially if there was a lot of it. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. Descriptive statistics can be useful for two purposes: 1) to provide basic information about variables in a dataset and 2) to highlight potential relationships between variables (OPRE, 2020).

In the hypothetical data provided by Chamberlain, one of the questions is, “Do you own your residence?” with responses: Yes (61%) and No (39%). The use of measures of central tendency plays a major part in this type of research. Measures of central tendency are the most basic and often the most informative description of a population's characteristics, describing the "average" member of the population of interest. The three measures of central tendency are the mean, mode, and median (OPRE, 2020).

This question was likely asked to assess who might have or need a mortgage and how that could influence their decision to attend nursing school. For individuals who own their residence, they might leverage their home equity—possibly by taking a second mortgage—to finance their education. Those with existing mortgages would need to evaluate their financial capacity, calculate how much is needed for school expenses, and consider what sacrifices are necessary. The data also allows the research group to understand the percentage of homeowners versus renters, providing a clear, simplified view that makes the information more accessible and realistic within a broader context.

Sample Paper For Above instruction

Descriptive research plays a vital role in understanding the characteristics of a population or phenomenon, especially through the classification based on the number of subjects, time dimension, and the nature of the phenomenon itself (Houser, 2016). It is designed to systematically describe and present data to facilitate interpretation, often through statistical summaries that offer insights into the distribution, central tendencies, and relationships among variables.

One of the key tools in descriptive research is descriptive statistics, which simplify complex datasets and make their interpretation more accessible. When dealing with large datasets, raw data can be overwhelming and difficult to analyze directly. Descriptive statistics, such as measures of central tendency, are essential because they condense data into meaningful summaries—mean, median, and mode—that often reveal the core characteristics of the dataset. For instance, in a study exploring homeownership and its relation to educational pursuits like nursing school, understanding the proportion of homeowners versus non-homeowners provides meaningful context about consumer behavior and financial stability among the population.

In the hypothetical data used by Chamberlain, the question “Do you own your residence?” with responses indicating 61% ownership and 39% non-ownership offers a snapshot of the population’s housing status. Analyzing such data using measures like proportion or percentage helps researchers interpret the distribution of homeownership, which may influence financial decisions related to education. For example, homeowners may have more leverage to access home equity, possibly through second mortgages, thereby funding educational endeavors. Conversely, renters might face different financial challenges and opportunities, which could influence their ability to pursue further education or training.

Furthermore, the application of measures of central tendency provides critical insights into the data’s core tendencies. For example, calculating the mean age of homeowners versus renters could reveal age-related financial stability or variability. The median income levels in these groups could identify economic disparities influencing educational access. These statistical summaries enable researchers to make informed hypotheses about the population’s behavior and needs, guiding policy development or targeted interventions.

Descriptive designs are also vital in understanding phenomena over different periods (time dimension). Longitudinal studies, for example, track how variables such as homeownership status or educational attainment change over time, providing deeper insights into causal factors and trends. Cross-sectional descriptive studies, on the other hand, give a snapshot at a particular moment, useful for planning and resource allocation.

In conclusion, the classification and application of descriptive designs based on subjects, time, and phenomena are fundamental in research. The use of descriptive statistics, especially measures of central tendency, enhances our ability to interpret data efficiently and accurately. These methods are particularly valuable in health sciences, social sciences, and economics, where understanding the basic characteristics of a population forms the basis of further analytical or experimental research. Proper application of these descriptive approaches can ultimately guide effective decision-making and policy formulation, aligned with the needs and realities of the population studied.

References

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• Office of Planning, Research and Evaluation. (2020). DESCRIPTIVE STATISTICS. Retrieved from https://www.acf.hhs.gov/opre/resource/descriptive-statistics
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