Specification error refers to a critical issue in survey research where the chosen model or framework used to analyze data does not accurately capture the underlying relationships or characteristics of the phenomenon being studied. It occurs when the specification of the model deviates from the true data-generating process, which causes bias or unreliable results. 

In this blog post, we will explore the concept of specification error, and discuss its importance in survey research.

Understanding Specification Error

Specification error is defined as an error that occurs in statistical modeling when the functional form or variables included in the model do not accurately represent the true relationship between the dependent and independent variables. It arises when there is a mismatch between the model specification and the underlying data-generating process.

Hence, specification error happens when the chosen model fails to capture the accurate relationship between the variables being analyzed. This error can lead to biased parameter estimation, misleading inferences, and unreliable predictions.

Furthermore, there are many reasons why specification errors can occur such as omitting relevant variables, including irrelevant variables, using an incorrect functional form, or assuming incorrect distributional assumptions. It is important to carefully consider the theoretical and empirical evidence when specifying a statistical model to minimize specification error.

Also, to address specification errors you need to refine the model by adding or removing variables. You should also consider alternative functional forms, and test different assumptions. This is because detecting and correcting specification errors is crucial for obtaining accurate and meaningful results in statistical analysis.

Types of Specification Error

  1. Omitted Variables: Omitted variables occur when important variables that should be included in the model are left out. This omission leads to biased estimates and incorrect inferences, as the unaccounted variables can influence the relationships among the included variables.
  2. Misspecified Functional Form: Misspecification of the functional form arises when the assumed relationship between variables does not align with the actual functional form present in the data. For example, assuming a linear relationship when the true relationship is nonlinear can result in erroneous conclusions.
  3. Measurement Error: Measurement error refers to inaccuracies or imprecisions in the measurement of variables. It can arise from various factors, such as faulty instruments, respondent misunderstanding, or human error during data collection. Measurement error can lead to biased or imprecise parameter estimates and affect the validity of the results.

Related – Systematic Errors in Research: Definition, Examples

Causes of Specification Error

Specification errors can occur due to several reasons:

  • Knowledge or understanding of the phenomenon being studied may result in missing essential variables or choosing an appropriate functional form.
  • Time and resource constraints might lead to rushed model specification, overlooking potential variables, or assuming simplified relationships.
  • Assumptions made during the model specification process may not hold in reality, leading to specification errors.
  • Inadequate data quality checks or insufficient consideration of measurement errors can contribute to specification errors.

Consequences of Specification Error

Specification errors in survey research can have significant consequences that undermine the validity and reliability of the findings. Here are some key consequences of specification errors:

  1. Biased Parameter Estimates: Specification errors can lead to biased parameter estimates, where the estimated relationships between variables differ from their true values. What this mean is that omitted variables, misspecified functional forms, and measurement errors can all contribute to biased estimates. Hence, biased parameter estimates can distort the understanding of the relationships among variables and potentially lead to incorrect conclusions.
  2. Invalid Hypothesis Testing: When specification error is present, hypothesis tests based on the estimated model may be invalid. The incorrect model specification can affect the statistical properties of the estimators, such as their distribution and efficiency. As a result, hypothesis tests may produce misleading p-values, leading to incorrect acceptance or rejection of hypotheses. This undermines the validity of the statistical inference and compromises the reliability of the research findings.

Read – Hypothesis Testing: Definition, Uses, Limitations + Examples

  1. Inaccurate Forecasts and Predictions: Specification errors can also impact the accuracy of forecasts and predictions based on the estimated model. If the model does not accurately capture the true relationships or functional form, the forecasts or predictions derived from it may be unreliable. This can have practical implications in various fields, such as economics, finance, and marketing, where accurate predictions are crucial for decision-making.
  2. Incorrect Policy Recommendations: Specification errors can have serious implications for policy recommendations based on survey research. If the estimated model is plagued by specification error, the policy implications drawn from it may be flawed. Biased estimates or incorrect relationships among variables can lead to misguided policy recommendations. And this may not effectively address the underlying issues. This can have real-world consequences in areas such as public policy, healthcare, and social sciences.

Ways to Avoid Specification Error

Avoiding specification errors is crucial to ensure the validity and reliability of survey research. Here are some effective strategies to help mitigate specification errors:

  1. Conducting Pilot Studies: Pilot studies involve conducting a smaller-scale version of the main study to identify potential specification errors. By testing the survey instruments, data collection procedures, and model specifications in a pilot study, researchers can identify and address any issues before conducting the full-scale study. Pilot studies provide valuable insights into the appropriateness of the chosen model and help refine the research design.
  2. Using Multiple Indicators: To mitigate measurement error, it is advisable to use multiple indicators for each construct or variable of interest. Multiple indicators help capture different aspects of the underlying construct and reduce the impact of individual measurement errors. Employing techniques like factor analysis or latent variable modeling can help identify and validate the latent constructs and ensure more reliable measurements. 
  3. Checking for Multicollinearity: Multicollinearity occurs when there is a high correlation between predictor variables in a regression model. It can lead to unstable parameter estimates and difficulties in interpreting the individual effects of predictors. To avoid multicollinearity, researchers should assess the correlation matrix among predictors and consider removing or combining highly correlated variables. Techniques like variance inflation factor (VIF) analysis can help identify and address multicollinearity issues. 
  4. Examining Model Fit: Evaluating the fit of the chosen model is essential to assess its adequacy in capturing the relationships in the data. Model fit assessment involves comparing the observed data with the expected data patterns based on the estimated model. Techniques such as goodness-of-fit measures, like the chi-square test, or indices like the Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA), can help assess the model fit. If the model fit is inadequate, researchers may need to reconsider the model specification and explore alternative models.
  5. Consulting with Experts: Seeking input from experts in the field can provide valuable insights and help identify potential specification errors. Experts with domain knowledge and expertise can review the research design, model specification, and data collection procedures to identify any potential issues or recommendations for improvement. Collaborating with experts ensures a more comprehensive and rigorous approach to addressing specification errors.

Employing these strategies can enhance the quality of survey research and minimize the occurrence of specification errors. Therefore, approach survey research with careful consideration of the model specification. Also, diligently address potential sources of error to ensure accurate and reliable findings.

Examples of Specification Errors in Survey Research

Specification errors can occur in various fields of survey research. Here are some examples of specification errors in different domains:

  • Market Research Studies:
  1. Omitted Variables: In a market research study analyzing consumer preferences for a product, failing to include a crucial variable such as price or brand reputation can lead to biased estimates of the determinants of consumer choice.
  2. Misspecified Functional Form: Assuming a linear relationship between advertising expenditure and sales volume when the true relationship is nonlinear can result in inaccurate estimates of the advertising effectiveness and lead to incorrect marketing strategies.
  • Economic Surveys:
  1. Measurement Error: In an economic survey measuring household income, if respondents provide inaccurate or incomplete information about their income, it can introduce measurement error and result in biased estimates of income distribution or economic inequality.
  2. Omitted Variables: When studying the determinants of economic growth, failing to account for important variables like technological innovation or institutional factors can lead to incomplete models and biased estimates of the factors influencing economic development.

  • Public Opinion Polls:
  1. Misspecified Functional Form: In a public opinion poll measuring political ideology, assuming a unidimensional left-right scale when the true relationship is more complex can result in oversimplification and biased estimates of political attitudes and preferences.
  2. Omitted Variables: Neglecting to include influential factors such as media exposure or social media engagement in a poll on voting intentions can lead to incomplete models and biased estimates of the factors shaping public opinion.

  • Social Science Research:
  1. Measurement Error: In a social science study investigating self-reported happiness levels, if respondents misinterpret the survey questions or provide socially desirable responses, it can introduce measurement error and affect the accuracy of estimates regarding factors influencing happiness.
  2. Omitted Variables: When examining factors affecting educational attainment, omitting important variables like parental involvement or school quality can result in incomplete models and biased estimates of the determinants of educational outcomes.

These examples highlight how specification errors can occur in different research domains, leading to biased or unreliable estimates and potentially affecting the validity and generalizability of survey research findings. It underscores the importance of careful model specification and addressing potential sources of specification error in survey research.


In conclusion, specification errors in survey research can lead to biased estimates, invalid inferences, inaccurate predictions, and misguided recommendations.

To avoid specification errors, researchers should carefully design their studies. They should consider multiple indicators, address multicollinearity, assess model fit and seek expert input.

Lastly, by mitigating specification errors, researchers can improve the quality and reliability of their survey research findings.


  • Olayemi Jemimah Aransiola
  • on 8 min read


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