Coding Likert Scale Agreement With Ordinal Logistic Regression Two Recommendations

In survey research, Likert scales are frequently used to measure attitudes, opinions, and perceptions. These scales typically present respondents with a statement and ask them to indicate their level of agreement or disagreement on a symmetrical agree-disagree scale. Analyzing Likert scale data can be challenging because the data is ordinal, meaning the categories have a meaningful order but the intervals between them are not necessarily equal. This article will discuss how to code Likert-scale agreement data when two different recommendations are being compared, focusing on the application of ordinal logistic regression. Ordinal logistic regression is a statistical method appropriate for analyzing ordinal data and is particularly useful when dealing with Likert scales. Understanding the nuances of coding and analyzing Likert-scale data is crucial for researchers aiming to draw accurate and meaningful conclusions from their surveys. The specific scenario we will address involves a survey conducted in a region in America where a particular food is consumed, with two distinct recommendations circulating about its consumption. This situation presents a unique challenge in data analysis, requiring careful consideration of how to best represent the agreement levels with each recommendation and how to compare them statistically. This comprehensive guide aims to provide researchers with a clear methodology for handling such data, ensuring that the results are both valid and insightful.

Likert scales, named after psychologist Rensis Likert, are a popular psychometric scale used in questionnaires. A typical Likert scale presents a statement and asks respondents to indicate their level of agreement, usually on a 5- or 7-point scale. The response options often range from “Strongly Disagree” to “Strongly Agree,” with intermediate options such as “Disagree,” “Neutral,” and “Agree.” The ordinal nature of Likert scale data means that the order of the categories is meaningful (e.g., “Agree” is higher than “Neutral”), but the distance between categories is not necessarily equal (the difference between “Strongly Agree” and “Agree” might not be the same as the difference between “Neutral” and “Agree”). Ordinal logistic regression is a statistical technique designed to analyze ordinal dependent variables. Unlike linear regression, which assumes the dependent variable is continuous and normally distributed, ordinal logistic regression is specifically tailored for ordinal data. It models the relationship between the independent variables and the cumulative probabilities of the ordinal outcomes. In other words, it estimates the probability of a response falling at or below a certain category, given the values of the predictors. The model coefficients in ordinal logistic regression represent the change in the log-odds of being at or below a certain category for a one-unit change in the predictor variable. This makes it a powerful tool for understanding how different factors influence agreement levels on Likert scales. When dealing with two different recommendations, as in the scenario presented, ordinal logistic regression allows for a nuanced comparison of agreement levels with each recommendation, taking into account the ordinal nature of the data. This approach provides a more accurate and insightful analysis compared to methods that treat Likert scale data as continuous or nominal.

Imagine a research scenario where a survey is conducted in a specific region in America concerning the consumption of a particular food. In this region, there are two prominent, yet different, recommendations regarding the consumption of this food. One recommendation might advocate for increased consumption due to its perceived health benefits, while the other might advise moderation or even avoidance due to potential risks. This situation creates a unique context for studying consumer attitudes and behaviors. To understand how individuals perceive these conflicting recommendations, a survey is designed using Likert scales. Respondents are presented with statements reflecting each recommendation and are asked to indicate their level of agreement. For example, statements might include “I believe this food is beneficial for my health” (reflecting the first recommendation) and “I am concerned about the potential risks of consuming this food” (reflecting the second recommendation). The survey responses, collected on a Likert scale (e.g., 1 = Strongly Disagree, 5 = Strongly Agree), provide valuable data on the extent to which individuals align with each recommendation. Analyzing this data requires careful consideration of the ordinal nature of the Likert scales and the potential for complex relationships between agreement levels and other variables. The primary research question in this scenario is how to effectively compare the agreement levels with these two different recommendations. This comparison is crucial for understanding the impact of each recommendation on consumer behavior and for informing public health messaging. Ordinal logistic regression provides a suitable method for this analysis, allowing researchers to model the relationship between recommendation agreement and various demographic or behavioral factors. By using ordinal logistic regression, researchers can gain insights into which factors predict agreement with each recommendation and how the two recommendations compare in terms of overall acceptance.

When coding Likert-scale data for two different recommendations, it is essential to create variables that accurately reflect the respondents' agreement levels with each recommendation. The first step involves assigning numerical values to each response category on the Likert scale. For a typical 5-point scale (Strongly Disagree to Strongly Agree), the values 1 to 5 are commonly used. Once the numerical values are assigned, two separate variables should be created: one for agreement with the first recommendation and another for agreement with the second recommendation. Each variable will contain the numerical responses corresponding to the statements related to that particular recommendation. For instance, if the survey includes three statements related to the first recommendation, the variable representing agreement with that recommendation might be calculated as the mean or sum of the responses to those three statements. Similarly, a separate variable would be created for the second recommendation. It is crucial to ensure that the statements are worded in a consistent direction (either positively or negatively) before calculating these aggregate scores. If some statements are negatively worded, they should be reverse-coded (e.g., 1 becomes 5, 2 becomes 4, etc.) to maintain consistency. In addition to creating separate variables for each recommendation, it may also be useful to create a composite variable that represents the difference or ratio between the agreement levels with the two recommendations. This composite variable can provide insights into the relative preference for one recommendation over the other. For example, a difference score could be calculated by subtracting the agreement score for the second recommendation from the agreement score for the first recommendation. This score would indicate the extent to which an individual agrees more with one recommendation than the other. Proper coding is crucial for accurate analysis and interpretation of Likert-scale data. By creating clear and well-defined variables, researchers can effectively use ordinal logistic regression to compare agreement levels with the two recommendations and identify factors that influence these agreements.

Once the Likert-scale data is coded appropriately, the next step is to apply ordinal logistic regression to analyze the data. This statistical technique is particularly well-suited for ordinal data, such as Likert scale responses, as it accounts for the ordered nature of the categories. The first step in applying ordinal logistic regression is to identify the dependent and independent variables. In this scenario, the dependent variables would be the agreement levels with each of the two recommendations, coded as ordinal variables. The independent variables could include demographic factors (e.g., age, gender, education), behavioral factors (e.g., consumption habits, prior knowledge), and other relevant variables that might influence agreement with the recommendations. The ordinal logistic regression model estimates the probability of a respondent falling into a particular agreement category (or below) for each recommendation, given their values on the independent variables. The model coefficients represent the change in the log-odds of being at or below a certain category for a one-unit change in the predictor variable. These coefficients can be interpreted to understand the direction and magnitude of the relationship between the independent variables and agreement levels. When comparing agreement with two different recommendations, separate ordinal logistic regression models can be fitted for each recommendation. This allows researchers to identify the factors that predict agreement with each recommendation independently. Alternatively, a single model can be used with interaction terms to directly compare the effects of the independent variables on agreement with the two recommendations. For example, an interaction term between a demographic variable (e.g., age) and a recommendation indicator variable (1 = Recommendation 1, 0 = Recommendation 2) would indicate whether the effect of age on agreement differs between the two recommendations. Interpreting the results of the ordinal logistic regression requires careful consideration of the model assumptions and limitations. It is important to assess the goodness-of-fit of the model and to check for violations of assumptions such as proportional odds. Despite these considerations, ordinal logistic regression provides a powerful and flexible approach for analyzing Likert-scale data and comparing agreement levels with different recommendations.

Interpreting the results of ordinal logistic regression involves examining the estimated coefficients, their statistical significance, and the overall fit of the model. The coefficients in ordinal logistic regression represent the change in the log-odds of being at or below a certain category for a one-unit change in the predictor variable. A positive coefficient indicates that an increase in the predictor variable is associated with a higher probability of agreeing with the recommendation, while a negative coefficient indicates the opposite. The statistical significance of the coefficients is typically assessed using p-values. A p-value less than a predetermined significance level (e.g., 0.05) indicates that the coefficient is statistically significant, meaning that the observed relationship between the predictor and the outcome is unlikely to have occurred by chance. In addition to examining the individual coefficients, it is important to assess the overall fit of the model. This can be done using various goodness-of-fit statistics, such as the likelihood ratio test or the Hosmer-Lemeshow test. A good-fitting model provides a more reliable basis for drawing conclusions. When comparing agreement levels with two different recommendations, the results of the ordinal logistic regression can provide insights into the factors that influence agreement with each recommendation and how these factors differ between the recommendations. For example, if age is found to be a significant predictor of agreement with Recommendation 1 but not with Recommendation 2, this suggests that age plays a different role in shaping attitudes towards the two recommendations. Similarly, interaction terms in the model can reveal whether the effect of a predictor variable on agreement differs significantly between the recommendations. Drawing conclusions from the analysis requires careful consideration of the research question and the context of the study. It is important to avoid over-interpreting the results and to acknowledge the limitations of the data and the analysis. The findings should be discussed in light of existing literature and theory, and implications for future research and practice should be considered. By carefully interpreting the results of ordinal logistic regression, researchers can gain a deeper understanding of the factors that influence agreement with different recommendations and inform strategies for promoting desired behaviors.

Analyzing Likert-scale data, particularly when comparing agreement with two different recommendations, requires a rigorous and thoughtful approach. Ordinal logistic regression provides a powerful tool for this type of analysis, allowing researchers to model the ordinal nature of the data and identify factors that influence agreement levels. Proper coding of the Likert-scale responses is crucial for accurate analysis, and careful interpretation of the results is essential for drawing meaningful conclusions. In the scenario presented, where two different recommendations exist regarding the consumption of a particular food, ordinal logistic regression can help to identify the factors that predict agreement with each recommendation and to compare the overall acceptance of the two recommendations. This information can be valuable for informing public health messaging and interventions. By following the steps outlined in this article, researchers can effectively analyze Likert-scale data and gain valuable insights into attitudes, opinions, and behaviors. The key takeaways include the importance of using appropriate statistical techniques for ordinal data, the need for careful coding and variable creation, and the value of interpreting results within the context of the research question and existing literature. Ultimately, a thorough and well-executed analysis of Likert-scale data can contribute to a deeper understanding of complex social phenomena and inform evidence-based decision-making.