Can I combine multi-wave survey data into one longitudinal dataset?

Yes. Upload all wave exports at once and enable 'Include source file name.' If your files are named with the wave identifier (e.g. 'survey-wave-3.xlsx'), every row is tagged with its wave, giving you a longitudinal dataset ready for mixed-models or repeated-measures analysis.

Does the combined output work with R's read.xlsx function?

Yes. The output is a standard single-sheet .xlsx file that loads cleanly with readxl::read_excel() or openxlsx::read.xlsx() in R, as well as pandas.read_excel() in Python.

What if different raters' files have slightly different column names?

Deliteful outputs the union of all column names as-is. If rater files have inconsistent column names for the same variable, those appear as separate columns in the output with empty cells for the other rater. Standardize column names across rater files before uploading for cleanest results.

Can I combine instrument data files with different measurement columns across experimental conditions?

Yes. The column union approach means condition-specific columns appear in the output for all rows, with empty cells for conditions that don't have that measurement. This is a valid sparse structure for many analysis workflows.

Consolidate Survey and Lab Data From Multiple Excel Sheets for Analysis

Research datasets rarely arrive pre-consolidated. Survey platforms export one file per wave, lab instruments produce one sheet per run, and coded qualitative data lives in separate files by rater. Before any statistical analysis in R, SPSS, or Python, all of that needs to be in one place. Deliteful's Excel Combine Sheets tool handles the merge step so you can move straight to analysis.

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A typical multi-wave survey study might produce 4–6 separate Excel exports from Qualtrics or SurveyMonkey — each with identical or near-identical column structures, but stored as separate files by collection period. Stacking these manually means either copying rows (error-prone) or writing a script (time-consuming for a one-off task). Coding reliability studies face the same problem: two raters' files need to be combined before computing Cohen's kappa or percentage agreement.

Deliteful reads first-row headers from every sheet across all uploaded files and outputs the column union in one worksheet. For studies with consistent column structures across waves, the output is immediately ready for import into R (read.xlsx), SPSS, or pandas. Enable the 'Include source file name' option to add a wave or rater identifier column automatically — a critical variable for longitudinal analysis or inter-rater reliability calculations.

How it works

1
Export your data files
Download .xlsx exports from your survey platform, instrument software, or coded data files from each rater or wave.
2
Upload all files to Deliteful
Drop all files in one job; multiple files and multiple sheets per file are combined together.
3
Add wave or rater identifier
Enable 'Include source file name' to automatically tag each row with its originating file — essential for wave or rater identification.
4
Import into your analysis software
Load the combined .xlsx directly into R, SPSS, Python, or JASP for your statistical analysis.

Frequently asked questions

Can I combine multi-wave survey data into one longitudinal dataset?: Yes. Upload all wave exports at once and enable 'Include source file name.' If your files are named with the wave identifier (e.g. 'survey-wave-3.xlsx'), every row is tagged with its wave, giving you a longitudinal dataset ready for mixed-models or repeated-measures analysis.
Does the combined output work with R's read.xlsx function?: Yes. The output is a standard single-sheet .xlsx file that loads cleanly with readxl::read_excel() or openxlsx::read.xlsx() in R, as well as pandas.read_excel() in Python.
What if different raters' files have slightly different column names?: Deliteful outputs the union of all column names as-is. If rater files have inconsistent column names for the same variable, those appear as separate columns in the output with empty cells for the other rater. Standardize column names across rater files before uploading for cleanest results.
Can I combine instrument data files with different measurement columns across experimental conditions?: Yes. The column union approach means condition-specific columns appear in the output for all rows, with empty cells for conditions that don't have that measurement. This is a valid sparse structure for many analysis workflows.

Create your free Deliteful account with Google and consolidate your multi-wave or multi-rater data into one analysis-ready Excel file.