Yes and no. They're pretty impressive by now, especially Claude, but they also can't quite be trusted yet. Great for exploring and for internal projects, but I wouldn't risk it for client projects or important analyses. Let's dig deeper. If you're not overly concerned with traceability, ease-of-use or security. If have no problem sharing your – or your client's – survey data with the AI companies, there's actually a lot you can do.
AI large language models (LLMs) cannot be fully trusted to analyze quantitative SPSS survey data files because they are not statistical engines and lack the ability to rigorously apply validated statistical methods. While LLMs can generate interpretations or summaries, they may misapply tests, overlook assumptions (e.g., normality, sample size, weighting), or produce results that appear authoritative but are statistically invalid. Unlike dedicated tools such as SPSS, R, or Python statistical libraries, LLMs do not directly calculate from raw data with reproducible methods; instead, they generate text based on patterns in training data. This makes them useful for guidance or explanation, but not reliable for producing accurate, defensible quantitative analysis.
One major challenge of using large language models (LLMs) to analyze big survey data files is the limited context window. LLMs can only process a certain amount of text or data at one time, meaning they cannot “see” an entire dataset if it’s large. When data has thousands of rows and variables, the model may miss important patterns, lose consistency across chunks, or misinterpret relationships. This makes LLMs poorly suited for handling full-scale quantitative survey analysis, where reliable results depend on examining all the data together rather than in small slices.
