Artificial Intelligence in Research
Generative AI can write Stata do-files for data cleaning, statistical testing, regression, and reproducible analysis. However, valid Stata commands may still use inappropriate samples, models, or assumptions. Learn how to verify AI-generated Stata code and avoid errors that could undermine your research findings.
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Artificial Intelligence in Research
Generative AI can generate SAS programs for data management, statistical analysis, regression, and research reporting. However, code that runs successfully may still alter the analytical sample or implement the wrong statistical model. Learn how to verify AI-generated SAS code and avoid errors that could compromise research findings.
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Artificial Intelligence in Research
AI can generate statistical code that researchers cannot write themselves, but should they use code they do not understand? Learn why successful execution is insufficient, what level of understanding is necessary, and how to determine when AI assistance requires independent methodological review.
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Artificial Intelligence in Research
AI-generated statistical code can run successfully while producing incorrect results. Learn how researchers can verify data transformations, statistical methods, model assumptions, and numerical output using systematic testing and independent validation.
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Artificial Intelligence in Research
A statistical script can execute successfully and produce convincing results while implementing the wrong method. Learn how AI-generated code can confuse paired and independent tests, ignore clustering, mishandle categorical variables, or estimate the wrong statistical quantity.
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Artificial Intelligence in Research
Generative AI may produce statistical code that relies on deprecated functions, obsolete packages, or assumptions from older software versions. Learn why code can fail or change behavior after updates and how researchers can verify compatibility without compromising statistical reproducibility.
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Artificial Intelligence in Research
Generative AI can invent convincing statistical function names, packages, and command arguments that do not exist. Learn how these coding hallucinations arise, why some are difficult to detect, and how researchers can verify software references before using AI-generated research code.
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Artificial Intelligence in Research
AI-generated statistical code may rely on default settings that researchers never explicitly approved. Learn how variance assumptions, missing-data handling, reference categories, and model options can silently change statistical results, and how to verify them before publication.
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Artificial Intelligence in Research
AI-generated statistical code may appear correct while concealing methodological or computational errors. Learn when independent review is warranted, what a qualified reviewer should examine, and how code review can strengthen research reliability without replacing author accountability.
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Artificial Intelligence in Research
When AI-generated research code contains an error, responsibility does not automatically belong to the AI provider. Learn how accountability applies to researchers, coauthors, and statistical collaborators, and what should happen when coding mistakes affect published findings.
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Artificial Intelligence in Research
Generative AI can apply predefined qualitative codes to interview transcripts and other textual data, but reliable classification depends on clear definitions and appropriate validation. Learn how to prepare a codebook for AI-assisted coding, evaluate ambiguous assignments, detect coding drift, and preserve methodological consistency.
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Artificial Intelligence in Research
Generative AI can compare themes across participant groups and help organize similarities, differences, and contrasting experiences. However, apparent group differences may reflect sampling, interview conditions, or incomplete evidence rather than meaningful distinctions. Learn how to conduct evidence-linked comparisons without stereotyping participants or overstating qualitative findings.
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Artificial Intelligence in Research
Generative AI can produce reflective statements, identify possible biases, and suggest questions about qualitative interpretation. But generating reflexive language is not necessarily the same as exercising methodological reflexivity. Learn why this distinction matters, what AI can contribute to reflexive practice, and why researchers must not confuse simulated reflection with their own analytical engagement.
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Artificial Intelligence in Research
AI-assisted coding does not automatically change a qualitative study's epistemology, but how researchers use AI may introduce assumptions that conflict with their methodological commitments. Learn how automated classification, coding reliability, and delegated interpretation relate to interpretivist, constructivist, and other qualitative approaches, and how to evaluate methodological congruence.
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Artificial Intelligence in Research
Generative AI can produce qualitative codes, themes, and interpretations, but generating findings is not necessarily equivalent to undertaking the researcher's interpretive role. Explore what human interpretation contributes, when AI can perform structured analytical tasks, and why methodological accountability cannot simply be delegated to a model.
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Artificial Intelligence in Research
Reporting that generative AI was used in qualitative analysis is not enough to explain its methodological contribution. Researchers should distinguish transcription, coding, theme development, and interpretation; document how outputs were verified; and disclose relevant data protection arrangements. Learn what to report, where to report it, and why transparent disclosure does not automatically establish methodological validity.
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Artificial Intelligence in Research
Generative AI can help researchers compare quantitative results with qualitative findings, organize integrated evidence, and explore possible interpretations. Its usefulness depends on preserving methodological context, examining contradictions, and independently verifying the conclusions it proposes.
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Artificial Intelligence in Research
Generative AI can help researchers construct mixed-methods joint displays that connect quantitative findings with qualitative themes. Learn how to organize the evidence, distinguish integration from simple juxtaposition, and verify AI-generated interpretations.
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Artificial Intelligence in Research
Generative AI can identify apparent agreement between quantitative and qualitative findings, but similarity is not necessarily convergence. Learn how to evaluate construct alignment, distinguish agreement from complementarity, and verify AI-assisted conclusions.
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Artificial Intelligence in Research
Generative AI can help researchers identify apparent contradictions between quantitative and qualitative findings. However, determining whether those differences represent genuine divergence, methodological variation, or unresolved uncertainty requires careful examination of the underlying evidence.
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Artificial Intelligence in Research
Generative AI can propose meta-inferences by connecting quantitative and qualitative findings, but a plausible integrated conclusion is not necessarily a defensible one. Learn how to distinguish genuine integration from restatement, assess inferential quality, and verify AI-generated interpretations.
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Artificial Intelligence in Research
Generative AI may turn genuinely contradictory quantitative and qualitative findings into a seemingly coherent explanation without sufficient evidence. Learn how to recognize overresolution, distinguish legitimate reconciliation from unsupported interpretation, and preserve meaningful uncertainty in mixed-methods research.
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Artificial Intelligence in Research
Generative AI can turn inconsistent mixed-methods findings into a smooth, persuasive narrative that conceals important contradictions. Learn how narrative smoothing occurs, why coherence is not evidence of validity, and how to preserve disagreement when writing integrated research findings.
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Artificial Intelligence in Research
AI-assisted mixed-methods integration requires more than checking statistics and quotations. Learn how to verify relationships between quantitative and qualitative findings, audit joint displays, evaluate meta-inferences, and document the reasoning behind integrated conclusions.
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Artificial Intelligence in Research
Generative AI can create illustrations, conceptual visuals, experimental stimuli, and synthetic images for research. However, researchers must distinguish generated content from observed evidence, evaluate its scientific accuracy, and consider disclosure, copyright, and research integrity requirements.
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