Data Analysis - Quantitative
Why is quantitative data analysis important, and how does it differ from qualitative analysis?
Quantitative data analysis is important because it enables systematic testing of theoretical hypotheses using statistical techniques. Unlike qualitative analysis, which focuses on interpretation and meaning, quantitative analysis emphasizes measurement, comparison, and inference. Its goal is to assess relationships between variables, estimate effect sizes, and evaluate theoretical predictions with precision.
What should be done when implementing an existing scale?
Researchers should conduct confirmatory scale validation using confirmatory factor analysis and assess reliability through measures such as Cronbach’s alpha to ensure the scale functions as intended in the new context.
What is the difference between exploratory and confirmatory analysis?
Exploratory analysis is used to discover patterns and generate hypotheses, while confirmatory analysis tests predefined hypotheses derived from theory.
What is the purpose of power analysis?
The purpose of power analysis is to determine the required number of participants needed to detect a statistically significant effect of a given size with a specified level of confidence. It helps researchers ensure that a study is neither underpowered, which increases the risk of failing to detect real effects, nor unnecessarily large, which would waste resources.
What is the main advantage of using multiple qualitative methods?
The main advantage of using multiple qualitative methods is that it provides a more comprehensive and nuanced understanding of social phenomena. Different methods capture different dimensions of reality, and their combination allows researchers to triangulate findings, strengthen credibility, and uncover insights that might be missed by relying on a single method.
What are prerequisites for quantitative data analysis?
Prerequisites include clearly defined hypotheses, reliable and valid measures, appropriate sample size, clean and prepared data, and an analytical strategy aligned with the research question and level of measurement.
What is data preparation and why is it important?
Data preparation involves cleaning, screening, and managing missing data before analysis. Missing data may arise from non-response or survey design issues and can be handled through methods such as listwise deletion, imputation, or model-based approaches. Proper preparation is essential to avoid biased or invalid results.
What are t-tests, ANOVA, and post-hoc tests?
A t-test compares means between two groups, ANOVA tests mean differences across three or more groups, and post-hoc tests identify which specific groups differ after a significant ANOVA result.
When is qualitative research particularly appropriate?
Qualitative research is particularly appropriate when little is known about a phenomenon, when the aim is to study people and practices in their natural context, and when the research requires depth, richness, and detailed understanding rather than measurement or prediction. It is especially useful for exploring meanings, experiences, and processes that cannot easily be captured through numerical data.
What is thematic analysis?
Thematic analysis is a qualitative method used to identify, analyze, and report patterns or themes within qualitative data. It provides a systematic way of organizing rich empirical material and interpreting recurring meanings across data, while remaining flexible and applicable across different qualitative research designs.
How are confidence intervals used in quantitative data analysis?
Confidence intervals are used to estimate the range within which the true population parameter is likely to fall. They provide more information than point estimates by conveying both the magnitude and precision of an effect.
Why are attention and manipulation checks important in data analysis?
Attention and manipulation checks ensure that respondents understood the task and engaged meaningfully with the study. Failed checks can introduce noise or bias, and high failure rates signal potential problems with survey or experimental design that must be addressed transparently.
What is the main difference between reflective and formative constructs?
In reflective constructs, the construct causes the indicators, meaning indicators are interchangeable and removing one does not change the construct. In formative constructs, indicators form the construct, and removing an indicator changes the meaning of the construct itself.
What are the three core ethical principles in qualitative research?
The three core ethical principles in qualitative research are consent, confidentiality, and trust. Researchers must ensure that participants voluntarily agree to take part based on informed consent, that their identities and data are protected through confidentiality, and that the research relationship is built on trust, with respect for participants and responsible use of their contributions.
What are the six phases of thematic analysis?
The six phases of thematic analysis begin with familiarizing oneself with the data to gain an overall understanding, followed by generating initial codes. Researchers then search for themes by grouping related codes, review and refine these themes to ensure coherence, define and name the final themes, and finally produce the report by linking themes back to the research question and theory.
What are scales in quantitative data analysis, and why are they useful?
Scales are multi-item measures used to capture latent constructs such as attitudes or values. They reduce measurement error, increase reliability, and allow abstract concepts to be analyzed quantitatively. Common types include nominal, ordinal, interval, and ratio scales.
What are outliers, and how are they handled?
Outliers are extreme values that deviate substantially from the rest of the data and may result from errors, rare events, or unusual behavior. They are detected using statistical diagnostics and handled carefully to balance robustness with theoretical relevance.
When thematically coding an interview, which variable type do you get?
When thematically coding an interview, all four variable types can in principle be obtained: nominal, ordinal, discrete, and continuous, depending on how the qualitative data are coded and transformed. At the most basic level, themes function as nominal categories. However, researchers may introduce ordinal structure by ranking themes, discrete variables by counting occurrences or coded segments, and continuous variables by transforming frequencies or intensities into scaled measures. Thus, while thematic coding originates in qualitative categorization, it can generate multiple variable types once the data are structured for analysis.
What distinguishes structured, semi-structured, and unstructured interviews?
Structured interviews are characterized by standardized questions asked in the same way to all participants, which enhances comparability but limits flexibility. Semi-structured interviews follow an interview guide with predefined topics while allowing flexibility to probe and explore emerging issues. Unstructured interviews are largely participant-led and narrative, enabling rich, in-depth accounts but offering less consistency across interviews.
What makes focus group data unique compared to individual interviews?
Focus group data are unique because they capture interaction between participants, allowing researchers to observe social dynamics, collective sense-making, and how meanings are co-constructed through discussion. Unlike individual interviews, focus groups reveal agreement, disagreement, and the influence of group norms on expressed views.
How are scale values analyzed or interpreted?
Scale values are typically analyzed by summarizing responses across items, often through averaging, to create a composite score representing the underlying construct.
Why does correlation not imply causation?
Correlation does not imply causation because observed associations may be driven by confounding variables, reverse causality, spurious relationships, or measurement artifacts. Establishing causation requires strong research design and theoretical justification.
When collecting data through surveys, which variable types can you get?
When collecting data through surveys, all major variable types are possible, depending on how questions are designed. Surveys can produce nominal variables through categorical choices, ordinal variables through ranked responses, interval variables through scaled responses such as Likert scales, and ratio variables through numerical measures with a true zero. Open-ended survey questions may also generate qualitative data that can later be coded.
What is the difference between mixed-methods and multimethod qualitative research?
Mixed-methods research combines qualitative and quantitative approaches within the same study to address a research question from different methodological perspectives. In contrast, multimethod qualitative research uses only qualitative methods, such as interviews, observations, and document analysis, to examine a phenomenon from multiple qualitative angles.
What is the difference between content analysis and ethnographic or interaction analysis in focus groups?
Content analysis in focus groups concentrates on what is said, focusing on topics, statements, and their frequency. Ethnographic or interaction analysis, in contrast, focuses on how things are said, examining interaction patterns, turn-taking, and the ways knowledge and meaning are socially constructed within the group.