The Rice prioritization method is an inductive prioritization technique for prioritizing variables used in regression analysis. This method is the rice framework for regression analysis that was developed by rice researchers in an effort to develop guidelines for consumer behavior research. The rice score is a simple but useful statistical measure of the strength of the relationship between two or more variables.

The rice score takes into consideration correlations among all predictor variables, not just individual effects. Grubbs’ test can be used to test whether an observation’s rice score lies significantly outside the range r – 1P(N-r) + P(N-r). If so, this indicates that the rice score is substantially greater than would be expected if there were no relationship between X and Y (i.e., one could predict Y from X with good accuracy). The rice framework provides a guide for researchers to use in organizing their thinking about psychological variables.

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For example, when studying attitudes toward rape, it might be important to know whether rape victims are perceived as having provoked their own attack. The framework would suggest examining attitudes about both rape and rapists (i.e., focal independent variables) as well as victim characteristics (i.e., focal dependent variables).

In this case, it might be hypothesized that people who think rapists are responsible for their actions would hold less favorable attitudes toward victims than those who think that rapists do not have control over their own behavior.

Alternatively, if we were interested in predicting attitudes held by men and women about rape and its causes, we could develop separate rice matrices for each gender and then examine the degree to which rice scores within genders covary as well as how rice scores between genders relate.

In addition to the rice framework’s capacity to account for measurement error using latent variable models such as confirmatory factor analysis/structural equation modeling (SEM), researchers may use rice scores as a basis for decision about statistical tests of hypotheses or mediating and moderating relationships.

For rice-based research, we only need to specify the rice matrix’s row and column labels (e.g., gender; see rice example in Figure 3) as well as any distal outcome variables of interest. In general, rice scores can be used to test for differences between groups on latent variable means or covariances by specifying a rice framework with group types specified as rows and either outcome variables or latent constructs/variables specified along the columns..

In addition, rice scores also afford researchers a data reduction technique that reduces a larger number of outcome variables into a smaller set of underlying factors that account for common variance across those outcome variables. That is, rice scores summarize many outcome variables with just a few rice scores.

For example, Satorra and Saris (1985) obtained rice scores for sixteen self-concept variables using principal component analysis with varimax rotation. Rice scores are also helpful when there is not theoretical or conceptual reason to exclude any outcome variable in the study (Saris & Satorra, 1985).

The rice framework specifies the number of groups on the rows and either latent variables/outcome variables specified on columns. The null hypothesis states that rice scores for each group are equal; thus, rice scores can be used to test whether different groups on one or more latent constructs (variables) have significantly different rice scores. For example, in three-group exploratory factor analysis with two rice-defined latent variables, the rice framework would be as follows:

The rice score for a given group is defined as the total rice from all items minus the rice from reference items. The rice score ranges from -∞ to +∞ with a positive rice bounding it on one end and an undefined rice bounding it on the other end. In practice, researchers often set a lower bound of 0 and upper bound of 1 for rice scores. Researchers can also use Rice Distribution software to compute rice scores or investigate their hypotheses using rice scores computed by others (e.g., Bentler et al., 2011).

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In general practice, values greater than 0 indicate higher values associated with health outcomes while negative values indicate poorer health outcomes. For rice prioritization, rice scores are used to rank the relative importance of rice items by computing average rice scores across all rice items or a subset of rice items. Items with larger average rice scores provide more information about the construct and should be prioritized over items with smaller average rice scores.

For example, a researcher may prioritize rice items that have a higher loading on their latent variable(s) or compute an average rice score for each item that is then used to prioritize rice items based on their importance in the number line from least important (i.e., negative rice score) to most important (i.e., positive rice score). In other words, researchers will conduct research if it increases or “uncovers” rice and deprioritizes rice items that do not contribute to rice.

In an additive rice framework, rice was computed using the weighted sum of individual item rice scores. The weights in the calculation were the sample size for each item (i.e., if a rice item was included in a study with n people, then it contributed [n/N] to rice). In other words, “the variance of a belief or preference index is equal to the average squared loadings plus N times the average inter-item correlations”.

Thus, when computing average rice score across all items, weighting is applied in order to reflect differential importance in a scale where higher rice items have more impact on their latent variable(s) than lower.