Ethical Standards Summary Exploring Research Eighth Edition

Ethical Standards Summary Exploring Research Eighth Edition Neil J

Identify and discuss the criteria that make research questions and hypotheses good or bad. Provide examples of good and bad research questions and explain how to improve bad ones. Define key terms such as intelligence, socioeconomic status, personality, depression, happiness, and health as they relate to psychology, and discuss how different individuals might have different interpretations of these variables. Explain what research is, distinguish between experimental and nonexperimental research with examples, and describe the appropriate use of each method. Discuss the relationship between independent and dependent variables, the reasons why the null hypothesis is always a statement of equality, and the different forms a research hypothesis can take. Explore the advantages of reading peer-reviewed journal articles versus sources like Wikipedia, and analyze why good sampling is crucial in research. Examine the importance of measurement in research, including the various levels of measurement—nominal, ordinal, interval, and ratio—and how they influence data accuracy. Clarify the process of measurement, its significance, and how different levels of measurement affect research outcomes. Emphasize the importance of understanding variables such as control, extraneous, and moderator variables and their roles in research design. Summarize the purpose and process of research, the importance of hypotheses, and the necessity of rigorous measurement for valid results.

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The exploration of ethical standards within research provides a foundational understanding of designing, conducting, and evaluating scientific studies responsibly. Ethical considerations influence the formulation of research questions and hypotheses, ensuring that investigations respect participants, maintain integrity, and produce valid results. A good research question is clear, focused, feasible, and relevant, while a bad one may be vague, overly broad, or untestable. For example, asking, "Does social media affect adolescent self-esteem?" is a good question because it is specific and measurable. Conversely, a bad question would be, "Why do adolescents behave badly?" which is too vague and difficult to operationalize. Improving bad questions often involves narrowing the scope, specifying variables, or defining the population clearly (Salkind, 2012).

Research hypotheses translate questions into testable statements, often expressing relationships between variables. Good hypotheses are precise, measurable, and grounded in existing theory or prior research. They can be directional, indicating the expected direction of effect, or nondirectional, merely stating that a relationship exists. For example, "Adolescents who spend more time on social media will have lower self-esteem" is a directional hypothesis, whereas "There is a relationship between social media use and self-esteem" is nondirectional. Null hypotheses, on the other hand, posit no effect or relationship, serving as a default assumption to test against (Salkind, 2012).

The utility of peer-reviewed journal articles over sources like Wikipedia stems from the rigorous review process, ensuring accuracy, credibility, and scholarly credibility. Articles are scrutinized by experts, reducing the likelihood of errors, and often cite extensive references, which aid in verifying facts and accessing further information. Wikipedia, while frequently accurate and accessible, can be edited by anyone and thus may contain inaccuracies. However, Wikipedia's collaboration model allows for rapid updates and broad coverage of topics, making it a useful starting point for research (Ferrari & Capetti, 2017).

Sampling critically impacts research validity and generalizability. Good sampling methods aim to represent the target population accurately, reducing bias and increasing confidence in the findings. Larger samples generally provide more reliable estimates, but size must be balanced with resource constraints. A sample size that is too small may lack statistical power, while an excessively large sample may be impractical (Creswell, 2014). Determining "big enough" depends on the research design, variability within the population, and desired confidence levels.

Measurement is essential in research because it operationalizes abstract variables into quantifiable data. Measurement includes selecting appropriate instruments, scales, and procedures to accurately capture the variables of interest. Different levels of measurement—nominal, ordinal, interval, and ratio—offer varying degrees of precision. Nominal measurement categorizes data without numerical value, such as grouping participants by ethnicity. Ordinal measurement ranks data but does not specify the magnitude of differences, like classifying pain levels as mild, moderate, or severe. Interval measurements, such as temperature, have equal units, allowing for meaningful comparisons. Ratio scales have a true zero point, facilitating calculations of ratios, e.g., height or income (Salkind, 2012). Higher levels of measurement lead to more precise and valid data, crucial for accurate analysis.

Variables such as control, extraneous, and moderator variables influence research outcomes and interpretation. Control variables are held constant to eliminate confounding effects, such as controlling for intelligence when studying reading speed and comprehension. Extraneous variables are uncontrolled factors that may unpredictably impact the dependent variable, like the influence of television program content when studying academic achievement. Moderator variables change the strength or direction of the relationship between independent and dependent variables; for instance, temperature moderates the relationship between ice cream sales and crime rates (Cozby & Bates, 2015). Recognizing and managing these variables is vital for ensuring the internal validity of research (Salkind, 2012).

Research, as a systematic inquiry, seeks to generate new knowledge through empirical evidence, guided by theories that organize observations and predict future events. The process involves posing questions, formulating hypotheses, designing appropriate methods, collecting data, analyzing results, and drawing conclusions. Through replication, research ensures reliability and validity, reinforcing the credibility of findings. Hypotheses bridge the gap between questions and measurable investigation, with research hypotheses indicating expected relationships and null hypotheses serving as the baseline for testing these relationships (Babbie, 2017).

In conclusion, ethical standards underpin every aspect of research from question formulation to data interpretation. Good research questions and hypotheses are essential for producing meaningful, valid results. The careful selection of sampling strategies, precise measurement of variables at appropriate levels, and diligent control of confounding variables are critical for the integrity of research outcomes. Emphasizing the importance of credible sources, such as peer-reviewed articles, and understanding the complexities of variables involved allows researchers to contribute responsibly to the body of scientific knowledge. Adhering to these principles fosters trustworthy research that advances understanding while respecting ethical obligations to participants and the larger community.

References

• Babbie, E. (2017). The Practice of Social Research (14th ed.). Cengage Learning.
• Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research (12th ed.). McGraw-Hill Education.
• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.
• Ferrari, R., & Capetti, A. (2017). Wikipedia and the credibility of online sources: An evaluation of accuracy. Journal of Information Science, 43(6), 834-842.
• Salkind, N. J. (2012). Exploring Research (8th ed.). Pearson Education, Inc.