Chapters

1. Introduction
2. Approaches
3. Research Process
4. Ethical Consideration
5. Review of Literature
6. Hypothesis and Estimation
7. Research instruments
8. Data Collection and Analysis
9. Statistics for Nurses
10. Reporting and Communicating
11. Writing Nursing
12. Terminology
13. References

SAMPLING, DATA COLLECTION, ANALYSIS & INTERPRETATION of RESULTS

Sampling

• Sampling can be probability sampling or non-probability sampling.

Probability sampling (random sampling)

• It is a selection process that ensures each participant the same probability of being selected.
• Random sampling is the best method for ensuring that a sample is representative of the larger population.
• Random sampling can be:
  • simple random sampling
  • stratified random sampling, and
  • cluster sampling.

Nonprobability sampling

• It is the selection process in which the probability that any one individual or subject selected is not equal to the probability that another individual or subject may be chosen.
• The probability of inclusion and the degree to which the sample represents the population are unknown.
• The major problem with nonprobability sampling is that sampling bias can occur.
• Nonprobability sampling can be:
  • convenience sampling
  • purposive sampling or
  • quota sampling

Data Collection

• Data collection should be systematic and meticulous.
• In view of the statistical analysis, levels of measurement should be defined as:
  • nominal,
  • ordinal,
  • interval or
  • ratio level data.
• Sources of data can be:
  • documentary sources as primary and secondary sources,
  • field sources as subjects in person,
  • conditions, environment and events that are observable and measurable, and
  • historical data.
• The methods of collecting data include
  • surveys
  • questioning using interview schedule and questionnaires,
  • observation techniques with the help of structured or unstructured instruments, and
  • measuring with standardized instruments.
• A pilot study is done to establish the feasibility and practicability of the whole research design.

Analysis

• The purpose of analyzing data in a study is to describe the data in meaningful terms.
• Statistics help to answer important research questions and it is the answers to such questions that further our understanding of the field and provide for academic study.
• It is required the researcher to have an understanding of what tools are suitable for a particular research study.
• Depending on the kinds of variables identified (nominal, ordinal, interval, and ratio) and the design of particular study, a number of statistical techniques is available to analyze data.
• There are two approaches to the statistical analysis of data :
  • descriptive approach and
  • inferential approach.
• Descriptive statistics convert data into picture of the information that is readily understandable.
• The inferential approach helps to decide whether the outcome of the study is a result of factors planned within design of the study or determined by chance.
• The two approaches are often used sequentially.
• In that first, data are described with descriptive statistics, and then additional statistical manipulations are done to make inferences about the likelihood that the outcome was due to chance through inferential statistics.

Interpreting the results

• The result section of the research report is followed by section which focuses on interpretation of the results.
• In this task, the investigator tries to interpret the results within the given conceptual framework.
• Here the researcher draws conclusions based on the results.
• If hypotheses have been formed, this section discusses the support or lack of support hypothesis, and if hypothesis have not been formed the descriptive findings are discussed.

References