What Should You Do When Different Theories Explain the Same Phenomenon?

What If More Than One Theory Seems to Fit?

You search for a theory to explain your research problem and encounter an unexpected difficulty: you find too many plausible options rather than too few.

One theory explains the phenomenon through individual beliefs. Another emphasizes social relationships. A third focuses on institutional conditions. Two theories seem to predict similar outcomes but offer different reasons for them.

Which one is correct?

There may not be a simple answer. Multiple theories and models routinely coexist within scientific fields, sometimes because they genuinely compete and sometimes because they explain different aspects or levels of the same phenomenon. Scientific pluralism recognizes that heterogeneous explanations can coexist without implying that every explanation is equally useful.

Your task is therefore not necessarily to identify the one theory that has defeated all others. It is to determine what kind of relationship exists among the candidate theories and which theoretical strategy best serves the research question.

First Determine Whether the Theories Really Compete

Several Theories Can Be Relevant Without Saying the Same Thing

Multiple explanations are normal in science

Researchers sometimes assume that mature science should eventually produce one theory for each phenomenon.

Scientific practice is considerably less tidy.

Different models and explanations coexist in many fields. The Stanford Encyclopedia of Philosophy's discussion of scientific pluralism notes examples across physics, economics, climate science, and biology where multiple representations of the same or related phenomena remain scientifically useful.

Psychology provides another prominent example. Van Zomeren describes the field as theoretically diverse and argues that many research questions have different and competing theories available to answer them. His ACES framework consequently begins not with automatic theory selection but with analyzing, comparing, evaluating, and potentially synthesizing alternatives.

Finding several plausible theories is therefore not evidence that you have done something wrong. It may mean you have reached the genuinely theoretical part of the research problem.

Two theories can address the same phenomenon at different levels

Suppose you want to explain why university instructors adopt generative AI.

One theory may focus on individual beliefs and intentions. Another may explain how colleagues and professional communities shape behavior. A third may focus on organizational rules and resources.

All three concern adoption, but they are not necessarily competing directly.

They may answer different versions of the question:

• Why does this individual intend to use the technology?
• How do social relationships shape the individual's practice?
• Why do adoption patterns differ across organizations?

Before choosing among theories, identify the level of analysis required by your own question.

Theories can explain different mechanisms at the same level

Even when theories operate at the same level, they may emphasize different mechanisms.

One explanation of student persistence might emphasize motivation. Another might emphasize identity. Another could foreground social belonging or perceived opportunity.

The theories concern the same outcome but tell different causal or interpretive stories about how it comes about.

Those differences matter because theory is not merely a collection of predictors. It provides an account of why relationships should exist.

This is one reason theory selection should consider conceptual matching rather than relying solely on previous use. Recent theory-selection guidance explicitly treats the match between the phenomenon or theoretical entity and the chosen theory as central to good selection.

Theories genuinely compete when their claims cannot all be right under the same conditions

Some theories do offer rival explanations.

The strongest form of competition occurs when theories generate incompatible empirical implications under comparable conditions.

Suppose Theory A predicts that increasing autonomy should increase adoption because autonomy facilitates intrinsic motivation. Theory B predicts that under the same specified conditions greater autonomy should decrease adoption because strong external structure is required for coordinated behavior.

If both propositions concern the same population, outcome, conditions, and level of analysis, evidence can potentially discriminate between them.

Recent work on comparative theory testing similarly argues that useful comparison requires competing theories to generate logically coherent and empirically distinguishable propositions about a phenomenon.

Apparently competing theories may not actually contradict each other

The situation becomes more complicated when two theories appear to disagree but actually parse the phenomenon differently.

One theory may emphasize a mechanism operating early in a process, while another explains what happens later. One may describe average tendencies while another concerns behavior under particular conditions. One may focus on individual decision-making while another concerns group-level outcomes.

Philosophical work on scientific disagreement likewise notes that theories appearing to compete at a coarse level may emphasize different questions, evidence, or explanatory priorities when examined more closely.

Before designing a horse race between theories, make sure they are actually running the same race.

Different theories may make the same prediction for different reasons

This creates an important challenge for theory testing.

Suppose both Theory A and Theory B predict that greater perceived competence will be associated with stronger persistence. You observe exactly that relationship.

Which theory did the evidence support?

Potentially both. The result cannot discriminate between theories if both predict it.

A stronger comparative study identifies conditions under which the theories diverge. Perhaps they predict different mediators, different moderators, different temporal sequences, or different outcomes when competence is high but social support is low.

The most informative evidence is often not where theories agree but where their observable implications separate.

Do not choose by citation count

A highly cited theory may be highly cited because it is useful, historically influential, easy to operationalize, widely taught, or already embedded in a large research tradition.

None of those facts alone establishes that it is the best explanation for your research question.

Theory selection should consider explanatory fit, assumptions, scope, applicability, conceptual rigor, parsimony, empirical support, and whether the theory can produce meaningful insight for the particular problem.

Recent guidance such as the IMPACT framework explicitly treats theory selection as a multidimensional decision rather than a popularity contest.

Do not choose solely because validated instruments already exist

Measurement availability is a practical advantage, but it should not become the theoretical rationale.

A theory with widely used scales can make data collection easier. That does not establish that its explanation fits the phenomenon better than alternatives.

If another theory offers a more appropriate mechanism but requires more difficult measurement, choosing the easier theory may optimize the project administratively while weakening it conceptually.

Practical feasibility matters, especially in student research, but distinguish feasibility from theoretical superiority.

Compare the theories systematically

A useful comparison asks the same questions of every candidate theory.

Van Zomeren's ACES approach similarly emphasizes analyzing, comparing, evaluating, and then deciding whether synthesis is warranted rather than selecting theories by familiarity alone.

You can choose one theory without claiming that the others are wrong

Selecting a theoretical framework for one study does not require declaring every alternative explanation invalid.

Your research question may concern the mechanism that one theory explains most directly. Your design may operate at a particular level of analysis. One theory may provide the clearest testable propositions for the evidence available.

You can justify that selection while acknowledging plausible alternatives.

This is part of choosing a theory based on fit rather than forcing one onto the problem.

You can compare theories directly

If rival theories make distinguishable predictions, the comparison itself can become the study.

Rather than asking, “Does Theory A predict the outcome?”, ask, “What evidence would look different if Theory A rather than Theory B provided the better explanation?”

This may involve comparing predicted relationships, mechanisms, temporal sequences, responses under particular conditions, or patterns across cases.

Comparative theory testing can be particularly valuable because it evaluates alternatives rather than testing one theory in isolation against a vague possibility of “not the theory.”

You can use theories complementarily

Sometimes the theories explain different necessary aspects of the phenomenon.

An individual-level theory may explain intention, while an organizational theory explains whether individuals have the opportunity to act on that intention.

If both mechanisms are central to the research question, using more than one theory may provide a more complete explanation.

The rationale should identify the unique contribution of each perspective. Simply saying that both are relevant is not enough.

You can integrate theories, but only when synthesis is defensible

If theories contain complementary or convergent concepts and their assumptions can be reconciled, researchers may attempt theoretical integration.

This goes beyond using theories side by side. Integration requires explaining how their concepts, mechanisms, or propositions connect within a common explanatory structure.

Van Zomeren's ACES framework explicitly treats synthesis as a decision that follows analysis, comparison, and evaluation rather than something researchers should assume is always possible.

The relevant question is whether combining the theories creates a stronger explanation or merely a more complicated framework.

Pluralism does not mean “anything goes”

Accepting that multiple explanations can coexist does not mean all theories are equally defensible.

Explanatory pluralism requires reasons for retaining multiple perspectives. One published discussion argues that additional approaches are justified when they provide genuinely different information about a phenomenon rather than merely duplicating what another approach already explains.

Theories can still be criticized for poor conceptual fit, weak evidence, incoherent assumptions, lack of testability where testing is relevant, or inability to explain the phenomenon under investigation.

Pluralism is an argument for careful comparison, not theoretical indecision.

Disagreement among theories can be scientifically productive

Competing explanations reveal where knowledge remains unsettled.

If two theories explain the same evidence equally well but imply different mechanisms, that ambiguity identifies a useful research problem. Researchers can design studies around observations that separate those mechanisms.

If theories emphasize different aspects of the phenomenon, comparing them may reveal that the original research question was too coarse.

Scientific disagreement can therefore generate better questions rather than simply creating a problem to be resolved before research can begin.

You do not always need to decide which theory is “the winner”

A single study may not provide enough evidence to adjudicate among broad theoretical traditions.

Your findings may show that Theory A better explains one aspect of the phenomenon while Theory B explains another. Both may perform similarly under the conditions examined. The evidence may also reveal that the theories need refinement before a meaningful comparison is possible.

That is an acceptable outcome.

Research progresses through cumulative evidence. The objective of one study is to make the theoretical landscape clearer, not necessarily to hold an elimination ceremony at the end of the discussion section.

Your research question should determine the comparison

Theory choice becomes much easier when the research question is sufficiently precise.

“Why do students use generative AI?” permits an enormous range of explanations.

“Why do students continue using generative AI after experiencing inaccurate outputs?” narrows the explanatory target.

“Why do students with similar perceptions of AI usefulness differ in continued use when institutional restrictions vary?” narrows it further and makes some theoretical perspectives more relevant than others.

Theories should therefore be compared against the question you actually intend to answer, not against the broad topic printed on the proposal cover.

How to Compare Two Plausible Explanations

The theoretical contribution comes from comparing what the explanations uniquely imply, not from discovering that two theories can both be cited in the literature review.

Common Mistakes When Several Theories Seem to Fit

Analyze the Relationship Between the Theories Before Choosing Among Them

A practical way to compare candidates is to create a theory matrix before choosing the framework. Give each theory the same rows: phenomenon explained, level of analysis, central constructs, mechanism, assumptions, boundaries, major supporting and challenging evidence, and unique implications for your question.

The purpose is not to assign arbitrary scores. It is to make theoretical differences visible enough that your eventual selection can be defended.

Before Choosing Between Different Theories

Questions About Competing and Alternative Theories

Do Not Ask Which Theory Wins Until You Know How They Differ

How to Cite This Guide

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