16 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You often split time between desk work and meetings. Mornings might be cleaning and coding data in Excel or SPSS, checking emails in Outlook, and scheduling participants. Afternoons can include library or internet research, running interviews or supervising survey interviewers, and entering cleaned data into Word or Excel for reports.
You’ll also prepare slides in PowerPoint for presentations, track lab supplies or project expenses, and meet with the lead researcher about project plans. Days vary: some weeks focus on recruiting and screening study subjects; other weeks focus on analysis and making tables or graphs.
Expect Microsoft Excel, Word, Outlook, and PowerPoint every day: Excel for data entry, cleaning, and making tables; Word for reports; Outlook for scheduling. PowerPoint is used for presenting findings to groups.
For statistical work use IBM SPSS Statistics when you need descriptive stats or simple tests. The whole suite is often called Microsoft Office, but SPSS is the main specialized program for analyzing coded data.
According to the U.S. Bureau of Labor Statistics (BLS), about 30,640 people are employed in this role. The median annual wage is $61,990. The lowest tenth earn about $38,090, and the top tenth about $102,070 per year (BLS).
Hourly pay depends on institution and location: universities or nonprofits may pay near the median; private research firms or specialized labs can reach the top tenth.
Start by learning Excel well: sorting, filtering, pivot tables, and basic formulas. Take a free SPSS or basic statistics intro (many universities or Coursera offer one). Practice writing reports in Word and making clear slides in PowerPoint.
Volunteer on a campus project, help a history professor with literature searches and participant recruitment, or apply for entry-level research tech roles. Emphasize any experience recruiting people, doing interviews, or using Outlook for scheduling.
Use AI for proofreading reports, drafting recruitment emails, and summarizing long articles you found during internet or library research. Have the AI suggest Excel formulas or SPSS syntax, but always check outputs yourself for accuracy.
Do not let AI clean or code data without manual verification. Because you must verify data accuracy and validity, use AI as a helper, not a final decision-maker. Keep audit trails showing what the AI changed.
A Research Assistant focuses on running parts of projects: data entry, cleaning, scheduling participants, supervising interviewers, and creating tables or graphs. You follow project protocols and support social scientists in lab work.
A Historian or Graduate Researcher designs research questions, writes grant proposals, and interprets results at a deeper level. Assistants do more of the hands-on tasks—coding data, tracking supplies and expenses, and managing day-to-day project logistics.
Attention to detail: you must verify accuracy and validity of data, clean datasets, and code data for computer entry without introducing errors. Mistakes here break analyses.
Communication and organization: recruiting and scheduling participants, negotiating with service users, involving carers, and tracking supplies or expenses all need clear, polite, timely coordination. Technical skills: practical Excel and basic SPSS ability plus comfortable use of Outlook, Word, and PowerPoint for reporting and presenting.