III Flashcards
Data gathering parts
D I C D P I
Data objectives
Identify Data Source
Choose data collection methods
Develop data collection tools
Pilot testing
Implemented data collection
Data gathering, Before embarking on the data gathering process, it is essential to clearly define the objectives of your
research. Take the time to articulate the goals you aim to achieve and the specific information required to
meet these objectives. This initial step provides a roadmap for your research, guiding subsequent
decisions on data collection methods and sources.
Define objectives
Data gathering, Once objectives are established, identify the sources of data that will contribute to achieving those goals.
Distinguish between primary and secondary sources, recognizing that primary data involves firsthand
information collected specifically for your study, while secondary data relies on existing sources.
Consider a diverse range of sources, including academic literature, surveys, interviews, observations,
and official records
Identify data source
Data gathering, Selecting appropriate data collection methods is crucial to obtaining accurate and relevant information.
Different research questions may require varied approaches such as surveys, interviews, experiments, or
observational studies. Carefully evaluate each method’s suitability in capturing the data needed to
address your research objectives, taking into account factors like time, budget, and ethical
considerations.
Choose data collection method
Data gathering, Before launching the full-scale data collection, conduct a ——– to identify and rectify any issues with your data collection tools. This small-scale trial allows you to assess the clarity, comprehensibility, and effectiveness of your instruments. Use feedback from the pilot test to refine your tools, ensuring they are well-suited for the broader data gathering process.
pilot testing
Data gathering, After determining the data collection methods, develop tools that align with your research objectives.
Whether designing surveys, questionnaires, interview guides, or experimental protocols, ensure that these tools are structured to capture the necessary information. Pay close attention to the clarity and neutrality of questions to avoid bias and to facilitate reliable data collection.
Develop data collection tools
Data gathering, With a well-defined plan in place and refined tools, proceed to implement the data collection process systematically. Adhere to ethical standards, maintaining consistency and accuracy in recording and organizing data. This phase demands attention to detail and careful execution to ensure the quality and integrity of the collected information.
By conscientiously following these steps, the data gathering process becomes a methodical and purpose-driven endeavor, laying the groundwork for robust analysis and meaningful insights in subsequent stages of your research.
Implemented data collection
Data analysis
is a crucial phase in the research process, where collected data is transformed into meaningful insights. The type of analysis you choose depends on your research design —quantitative or qualitative.
Quantitative data analysis types
D I C F T
Descriptive
Inferential
Correlational and regression
Factor analysis
Time series Analysis
Qualitative data analysis
T G C C E
Thematic
Grounded theory
Content analysis
Case study Analysis
Ethnographic
Quantitative Data analysis, are fundamental in quantitative research, summarizing and organizing data to provide a clear overview. Measures such as mean, median, mode, and standard deviation offer insights into the central tendencies and variability of the data. Visual aids like histograms and pie charts enhance the presentation of numerical information.
Descriptive statistics
Quantitative data analysis, are employed to draw conclusions about a population based on a sample of data.
This includes hypothesis testing, regression analysis, and analysis of variance (ANOVA). These methods help researchers make predictions and generalizations, extending findings from the sample to the broader population.
Inferential statistics
Quantitative data analysis, explores relationships between variables, indicating the strength and direction of connections. Regression analysis, on the other hand, models the relationships between variables, allowing for predictions and understanding the impact of one variable on another.
Correlation and regression
Quantitative data analysis, is useful when dealing with a large number of variables. It identifies underlying factors or dimensions that explain the observed patterns in the data. This method aids in simplifying complex datasets and understanding the underlying structure.
factor analysis
Quantitative data analysis, For research involving time-dependent data, time series analysis is employed. This method examines patterns, trends, and seasonality within the data over time, facilitating predictions and forecasting future values.
time series analysis
Qualitative data analysis, In qualitative research, —– involves identifying and analyzing recurring themes or patterns within the data. Researchers immerse themselves in the data, coding segments, and organizing them into themes to uncover underlying meanings.
thematic analysis
Qualitataive data analysis, —– aims to generate theories from the data itself. Researchers begin with open coding, categorizing data into concepts and categories, and gradually develop a theory that explains the relationship between these concepts
grounded theory
Qualitative analysis, is used to systematically analyze textual, visual, or audio content. Researchers identify and code specific themes, words, or phrases, enabling a quantitative interpretation of qualitative data. This method is common in media studies and communication research.
Content analysis
Qualitative data analysis, —- is employed in studies where researchers immerse themselves in the culture or context of the subjects. It involves participant observation, interviews, and document analysis to understand social phenomena from an insider’s perspective.
ethnographic analysis
is a critical step in the research process, enabling researchers to communicate the essence of their study to a broader audience. Effectively presenting your results involves a combination of clear, concise written summaries and visually appealing presentations
summarizing findings
Summary of findings points
HOUPD
Highlight Key findings
Organize information logically
Use clear and concise language
Proved context
Discuss unexpected results
Visual representation and findings
CTIH
Charts and graphs
Tables
Infographics
Heat maps
Visual representation of findings, Utilize – to visually represent your findings. Bar graphs, line charts, and pie charts can effectively illustrate patterns, trends, and relationships within your data. Choose the type of visualization that best fits your specific data and research objectives.
charts and graphs
Presenting data in allows for a detailed and organized display of numerical information. Ensure that tables are well-labeled, and use footnotes to provide additional context or explanations for specific values.What Visual representation of findings
tables
Create — to condense complex information into visually appealing and easily digestible formats. Infographics can include icons, charts, and short annotations to convey key findings in a visually engaging manner. What representation of findings
infographic
When dealing with large datasets, consider using – to highlight patterns and variations. This visual representation uses color gradients to convey the intensity of relationships or differences in the data. What Representation of findings
heat maps
—- is a crucial phase in the research process where researchers analyze and extract meaning from their findings. This involves going beyond the descriptive statistics and uncovering the implications, applications, and broader significance of the results.
interpreting results
Key steps in interpretation
PECCA
Provide content
Examine patterns and trends
Compare and contrast
Consider practical implications
Address Limitations
are critical steps that follow the interpretation of results. These steps involve summarizing the findings, making broader statements about the implications, and suggesting actionable steps for future endeavors.
Drawing conclusions and making recommendations
is the process of synthesizing the results, interpretations, and key findings to answer the research questions or hypotheses. It is a reflection on the extent to which the collected data and analyses support or refute the initial research objectives.
Drawing conclusion
Key elements
S A R
summarize key findings
Address hypothesis
Reflect on objectives
involves suggesting practical actions based on the research findings and conclusions. It aims to guide future decisions, policies, or practices related to the studied phenomenon.
Making reccomendations
making recommended actions key elements
I O C
Identify areas for improvement
Offer actionable suggestions
Consider implications
