AguilarT PSY 7103-9

docx

School

Capella University *

*We aren’t endorsed by this school

Course

7103

Subject

Psychology

Date

Oct 30, 2023

Type

docx

Pages

Uploaded by MagistrateLapwing4354

Specify Method-Appropriate Data Collection and Data Analysis Tanya Aguilar Assignment 9 Research Methods – PSY 7103 Northcentral University June 2023

FAQ What are some common data collection strategies for qualitative research and quantitative research? Qualitative Research:  Interviews: Conducting in-depth interviews with individuals or small groups to gather rich, detailed information about their experiences, perspectives, and opinions.  Focus Groups: Bringing together a group of participants to discuss a specific topic or issue, allowing for interaction and exploration of diverse viewpoints.  Observations: Directly observing and documenting behaviors, interactions, and phenomena in natural settings to gain an understanding of context and meaning.  Case Studies: Conducting in-depth investigations of a particular individual, group, or organization to explore complex phenomena within a specific context.  Document Analysis: Examining and analyzing written, visual, or audio materials such as texts, photographs, videos, or archival records to extract relevant information and insights. Quantitative Research:  Surveys: Administering structured questionnaires or surveys to collect numerical data from a large sample of respondents, allowing for statistical analysis and generalization of findings.  Experiments: Designing controlled experiments to manipulate variables and measure their effects, often involving random assignment of participants to different conditions.  Observational Studies: Systematically observing and measuring phenomena in their natural settings while minimizing researcher interference, such as through systematic sampling or structured observations.  Secondary Data Analysis: Analyzing existing datasets or previously collected data, such as government records, organizational databases, or publicly available datasets, to answer research questions.  Psychometric Testing: Administering standardized tests or assessments to measure psychological constructs, such as intelligence, personality traits, or attitudes, often yielding numerical scores. It's important to note that these strategies are not exclusive to either qualitative or quantitative research and can often be combined in mixed-methods research approaches, where both qualitative and quantitative data are collected and analyzed to provide a more comprehensive understanding of a research problem. (Busetto et al., 2020)

What are some factors that influence the decision of data collection strategy for any research study? Several factors can influence the decision of data collection strategy for a research study. Here are some key factors to consider:  Research Objectives: The specific goals and objectives of the study will shape the choice of data collection strategy. For example, if the aim is to explore individuals' experiences and subjective perspectives, qualitative methods like interviews or focus groups may be more suitable. If the goal is to measure relationships or test hypotheses, quantitative methods such as surveys or experiments may be preferred.  Research Questions: The nature of the research questions and the type of information required will impact the data collection strategy. Questions that seek to understand "how" and "why" are often better addressed through qualitative approaches, while questions focused on "what" and "how much" can be addressed using quantitative methods.  Sample Characteristics: The characteristics of the sample population, such as size, diversity, accessibility, and willingness to participate, can influence the choice of data collection strategy. Some methods may be more appropriate for large-scale surveys with a diverse population, while others may be better suited for small, homogeneous groups.  Resources and Time Constraints: The availability of resources, including budget, time, and personnel, will affect the feasibility and practicality of different data collection methods. Some methods may require specialized training, equipment, or extensive fieldwork, which should be taken into account during the decision-making process.  Ethical Considerations: Ethical concerns related to participant privacy, confidentiality, and well-being can impact the choice of data collection strategy. Researchers must ensure that their chosen methods align with ethical guidelines and regulations and minimize any potential harm or discomfort to participants.  Researcher's Expertise: The researcher's own skills, experience, and familiarity with specific data collection methods may influence the choice of strategy. It is essential to select methods that align with the researcher's proficiency to ensure the collection of high-quality data.  Nature of the Phenomena: The characteristics of the phenomena under investigation, such as their complexity, context-dependency, or sensitivity, may guide the selection of appropriate data collection methods. Certain phenomena may require direct observation, while others may necessitate the collection of subjective experiences and interpretations. Considering these factors and evaluating the strengths and limitations of various data collection strategies will help researchers make informed decisions when selecting the most suitable approach for their study. (Hesse et al., 2018) What are some common quantitative analyses and how to identify appropriate inferential analyses for quantitative RQs/hypotheses?

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Common quantitative analyses include:  Descriptive Statistics: Summarizing and describing the main characteristics of a dataset, such as measures of central tendency (mean, median, mode) and measures of variability (standard deviation, range).  Correlation Analysis: Examining the strength and direction of the relationship between two variables using correlation coefficients (e.g., Pearson's correlation).  Regression Analysis: Assessing the relationship between a dependent variable and one or more independent variables to determine the extent of their association and make predictions.  T-tests: Comparing means between two groups to determine if there is a statistically significant difference. There are different types of t-tests depending on the characteristics of the data and research design, such as independent samples t-test or paired samples t- test.  Analysis of Variance (ANOVA): Comparing means across more than two groups to determine if there are statistically significant differences. ANOVA is used when there are categorical independent variables and a continuous dependent variable.  Chi-square Test: Analyzing the association between two categorical variables to determine if there is a significant relationship. It is often used to compare observed and expected frequencies in contingency tables.  Factor Analysis: Exploring the underlying structure of a set of variables and identifying latent factors that explain the patterns of correlations among variables. When identifying appropriate inferential analyses for quantitative research questions (RQs) or hypotheses, consider the following guidelines:  Identify the type of data: Determine whether your data is continuous (interval or ratio) or categorical (nominal or ordinal). This will help you choose appropriate statistical tests.  Determine the nature of your research question: Is it focused on comparing groups, examining associations between variables, predicting outcomes, or exploring latent factors? The research question will guide your choice of analysis.  Consider the research design: Different analyses are suited for different research designs. For example, t-tests are commonly used for two-group comparisons, while ANOVA is appropriate for three or more groups.  Assess the assumptions: Ensure that the statistical test you choose aligns with the assumptions of your data, such as normality of distribution, independence, and homogeneity of variance. Violations of assumptions may require alternative analyses or data transformations.  Consult statistical resources: Refer to statistical textbooks, research methods literature, or consult with experts in the field to identify appropriate inferential analyses for your specific research question and data type. It is important to note that these guidelines provide a general framework, but the specific analysis choice will depend on the unique characteristics of your research study and data. Seeking

guidance from a statistician or research methods expert can be beneficial in selecting and conducting the appropriate inferential analyses. (Tomaszewski et al., 2020) What are some common qualitative analyses and how to identify appropriate qualitative analyses? Common qualitative analyses include:  Thematic Analysis: Identifying and analyzing recurring patterns or themes in qualitative data to gain insights into participants' experiences, perspectives, or meanings associated with a particular phenomenon.  Content Analysis: Systematically categorizing and coding qualitative data, such as interview transcripts or textual documents, to identify common themes, concepts, or categories.  Narrative Analysis: Examining the structure and content of narratives or stories shared by participants to understand how individuals construct and convey their experiences or identities.  Grounded Theory: Developing theories or explanations grounded in qualitative data through iterative processes of data collection and analysis. It involves constant comparison of data to generate concepts and categories.  Discourse Analysis: Analyzing language use, social constructs, and power dynamics within texts or conversations to understand how meaning is created, negotiated, and represented.  Phenomenological Analysis: Exploring individuals' lived experiences and subjective perceptions to uncover the essence and meaning of a specific phenomenon or event.  Ethnographic Analysis: Conducting in-depth observations and interpretations of social groups or cultures to understand their behaviors, practices, and societal dynamics. To identify appropriate qualitative analyses, consider the following steps:  Familiarize yourself with different qualitative analysis methods: Gain an understanding of various qualitative analysis approaches and their underlying principles by studying research methods literature, textbooks, or attending relevant workshops or courses.  Determine the nature of your research question: Clarify the specific objectives and research questions of your study. Consider whether you aim to explore individual experiences, understand social processes, interpret cultural phenomena, or analyze textual materials.  Consider your data collection methods: Reflect on the type of qualitative data you have collected, such as interviews, focus groups, observations, or documents. Different analysis methods may be more suitable for different types of data.  Examine the characteristics of your data: Consider the volume of data, the level of detail, and the complexity of the information collected. Determine whether your data is textual, visual, or a combination of both, as this can influence the choice of analysis method.

 Seek guidance from qualitative research resources: Consult relevant literature, qualitative research textbooks, or scholarly articles that describe and discuss different qualitative analysis methods. Pay attention to studies with research questions similar to yours and explore the analysis methods they employed.  Consult with experienced researchers: Seek advice from experienced qualitative researchers, mentors, or experts in your field. They can provide insights and guidance on appropriate analysis methods based on their expertise and experience.  Consider mixed-methods approaches: Depending on the research question and the availability of data, it may be beneficial to combine qualitative and quantitative analyses in a mixed-methods approach, allowing for a more comprehensive understanding of the research problem. Remember that the choice of qualitative analysis method should align with your research goals, data characteristics, and theoretical framework. It's important to apply rigorous and transparent analysis techniques to ensure the credibility and validity of your findings. (Guetterman, 2018) Barroga, E., & Matanguihan, G. J. (2022). A practical guide to writing quantitative and qualitative research questions and hypotheses in scholarly articles. Journal of Korean medical science , 37 (16), e121. https://doi.org/10.3346/jkms.2022.37.e121 Mwita, Kelvin. (2022). Factors to consider when choosing data collection methods. International Journal of Research in Business and Social Science (2147-4478). 11. 532-538. 10.20525/ijrbs.v11i5.1842. Noyes, J., Booth, A., Moore, G., Flemming, K., Tunçalp, Ö., & Shakibazadeh, E. (2019). Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods. BMJ global health , 4 (Suppl 1), e000893. https://doi.org/10.1136/bmjgh-2018-000893 References

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Barroga, E., & Matanguihan, G. J. (2022). A practical guide to writing quantitative and qualitative research questions and hypotheses in scholarly articles. Journal of Korean medical science , 37 (16), e121. https://doi.org/10.3346/jkms.2022.37.e121 Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z Guetterman T. C. (2019). Basics of statistics for primary care research. Family and community health , 7 (2), e000067. https://doi.org/10.1136/fmch-2018-000067 Hesse, Arielle & Glenna, Leland & Hinrichs, Clare & Chiles, Robert & Sachs, Carolyn. (2018). Qualitative research ethics in the big data era. American Behavioral Scientist. 63. 000276421880580. 10.1177/0002764218805806. Mwita, Kelvin. (2022). Factors to consider when choosing data collection methods. International Journal of Research in Business and Social Science (2147-4478). 11. 532-538. 10.20525/ijrbs.v11i5.1842. Noyes, J., Booth, A., Moore, G., Flemming, K., Tunçalp, Ö., & Shakibazadeh, E. (2019). Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods. BMJ global health , 4 (Suppl 1), e000893. https://doi.org/10.1136/bmjgh-2018-000893 Tomaszewski, L. E., Zarestky, J., & Gonzalez, E. (2020). Planning qualitative research: design and decision making for new researchers. International Journal of Qualitative Methods , 19 . https://doi.org/10.1177/1609406920967174