When conducting research, researchers make certain assumptions that guide their work. Assumptions are necessary to frame a research study and its potential outcomes, but researchers must be cognizant of their assumptions and how they can impact results. Some common assumptions made in research samples include:
Representativeness of the sample: One key assumption is that the sample selected for a study is representative of the target population the researcher intends to generalize findings to. For example, if studying college students’ stress levels, a researcher may administer surveys to a sample of students at one university. The researcher assumes this sample of students is representative of the larger population of all college students. There are many factors like university selectivity, location, programs offered, student demographics that could make one university’s students not truly representative. Researchers should aim to randomly select a diverse sample from multiple settings to strengthen representativeness claims.
Volunteer bias: When using a convenience sample of volunteers, researchers assume those who volunteer are similar to non-volunteers. Volunteers often differ in important ways like personality traits, beliefs, motivation levels. Findings may only generalize to others similarly motivated to volunteer and not the broader population. Strategies like randomly selecting from an eligibility list rather than only using volunteers mitigates this bias.
Normal distribution of variables: Parametric tests like t-tests and ANOVAs assume variables are normally distributed in the population. Variables like test scores, personality traits frequently do not meet strict assumptions of normality. Researchers assume deviations from normality do not unduly impact results, but this can be tested using nonparametric equivalents as well.
Independence of observations: Many statistical tests assume observations or scores on the variables of interest are independent of each other. With repeated measures designs, subject effects, or cluster sampling, observations may be related. Researchers assume dependence does not bias results significantly without directly testing dependence. Multilevel modeling is a technique to account for dependence.
Reliability and validity of measures: Researchers assume measures like surveys and tests are assessing the intended constructs reliably and validly. Even widely used instruments have limitations in certain populations and contexts. Thorough piloting, reporting of psychometrics and acknowledging limitations of generalizing instruments is prudent rather than assuming perfect reliability and validity.
Control of confounding variables: It’s assumed that any differences observed are due to the manipulated independent variable rather than some third variable. In non-experimental studies, many confounding variables cannot be controlled. Statistical techniques like matching, covariate adjustment, and propensity scores can help address but not perfectly rule out confounding variable biases.
Homogeneity of groups: Experiments assume random assignment results in groups equivalent on all variables except the independent variable. In reality, group differences always exist to some extent. Large sample sizes help minimize but do not eliminate this assumption of homogeneity between groups.
Linear relationships: Many statistical techniques assume linear relationships between variables. Relationships are frequently curvilinear in nature. Transforming variables, examining interaction effects or using nonparametric techniques provide alternatives when linearity cannot be reasonably assumed.
Stability of effects: It is assumed that the size or direction of effects observed in a study apply consistently across time, populations and settings. As contexts change, prior effects may diminish or even reverse due to other moderating factors. Replication across diverse samples strengthens assurances of stability versus context-specific results.
In sum, assumptions are a necessary component of research design but must be evaluated carefully. Researchers should acknowledge key assumptions of their study and qualifications to generalizations rather than presenting findings as proven facts. When assumptions clearly do not hold, alternative statistical techniques or mixed methods studies that employ both qualitative and quantitative components can provide more nuanced understandings beyond what a single quantitative study assuming a strict research paradigm can reveal.