SOCI 211 CHAPTER 16 â THE ELABORATION MODEL Elaboration ...
SOCI 211 CHAPTER 16 – THE ELABORATION MODEL Elaboration model: a logical approach to understanding the relationship between 2 variables thru the simultaneous introduction of a 3rd variable, usually referred to as a control/test variable. Though developed primarily thru the medium of contingency tables, it may be used with other statistical techniques. The various outcomes of an elaboration analysis include replication, specification, explanation, & interpretation. It aims to elaborate on an empirical relationship among variables in order to interpret that relationship The central logic begins w/ an observed relationship between 2 variables & the possibility that one variable may be causing the other. Sometimes the analysis reveals the mechanisms thru which the causal relationship occurs. Other times an elaboration analysis disproves the existence of a causal relationship altogether.
Having observed an empirical relationship between 2 variables (level of education & acceptance of induction), we seek to understand the nature of that relationship thru the effect produced by introducing other variables (such as having friends who were deferred). We do this by 1st dividing our sample into subsets on the basis of the test variable (control variable). Test variable: a variable that is held constant in an attempt to clarify further the relationship between 2 other variables. EX: Having discovered a relationship between education & prejudice, we might hold gender constant by examining the relationship between education & prejudice among men only & then among women only. In this ex, gender is the test variable.
The relationship between the original 2 variables (acceptance of induction & level of education) is then recomputed separately for each of the subsamples. The tables produced in this manner are called the partial tables, & the relationships found in the partial tables are called the partial relationships. Partial relationships: the relationship between 2 variables when examined in a subset of cases defined by a 3rd (test) variable. EX: beginning w/ a zero-ordered relationship between income & attitudes toward gender equality. We want to see whether the relationship holds true among ♂ & ♀ (that is, controlling for gender). The relationship found among ♂ & the relationship found among ♀ would be the partial relationships, aka the partials.
The partial relationships are then compared w/ the initial relationship discovered in the total sample, often referred to as the zero-order relationship to indicate that no test variables have been controlled for.
Zero-order relationship: the relationship between 2 variables when no test variables are being controlled for (held constant). THE ELABORATION PARADIGM To begin, we must know whether the test variable is antecedent (prior in time) to the other 2 variables or whether it is intervening (interfering) between them because these positions suggest different logical relationships in the multivariate model. If the test variable is intervening, the independent variable (educational level) affects the intervening test variable (having friends deferred or not), which in turn affects the dependent variable (accepting induction). If the test variable is antecedent, the test variable affects both the ―independent‖ & ―dependent‖ variables. Replication: refers to the empirical outcome of the persistence of the observed initial relationship between 2 variables when a control variable is held constant. This supports the idea that the original, zero-order relationship is genuine. Whenever the partial relationships are essentially the same of the original relationship, the term replication is assigned to the result, regardless of whether the test variable is antecedent or intervening. This means that the original relationship has been replicated under test conditions. Explanation: a technical term that represents the elaboration outcome in which an antecedent test variable reveals the original (zero-order) relationship between 2 variables to be spurious. In other words, the original relationship disappears—is explained away—when the antecedent test variable is introduced. EX: There’s a positive relationship between the # of fire trucks responding to a fire & the amount of damage done. If more trucks respond, more damage is done. One might assume from this fact that the fire trucks themselves cause the damage. However, an antecedent test variable—the size of the fire—explains away the original relationship. Large fires do more damage than small ones, & more fire trucks respond to large fires than to small ones. Looking only at large fires, we would see that the original relationship vanishes (or perhaps reverses itself); and the same would be true looking only at small fires. Interpretation: a technical term that represents the research outcome in which a test or control variable is discovered to be the mediating factor thru which an independent variable has its effect on a dependent variable. EX: The intervening variable, deferment of friends, merely helps to interpret the mechanism thru which the relationship occurs. An interpretation doesn’t deny the validity of the original causal relationship but simply clarifies the process thru which that relationship functions. Specification: a technical term to refer to the elaboration outcome when an initially observed relationship between 2 variables is replicated among
some subgroups created by the control variable but not among others. In other words, when the partial relationships that result from the addition of a test variable differ significantly from each other, u will have specified the conditions under which the original relationship exists—for ex, among the elderly but not among children.
Having observed an empirical relationship between 2 variables (level of education & acceptance of induction), we seek to understand the nature of that relationship thru the effect produced by introducing other variables (such as having friends who were deferred). We do this by 1st dividing our sample into subsets on the basis of the test variable (control variable). Test variable: a variable that is held constant in an attempt to clarify further the relationship between 2 other variables. EX: Having discovered a relationship between education & prejudice, we might hold gender constant by examining the relationship between education & prejudice among men only & then among women only. In this ex, gender is the test variable.
The relationship between the original 2 variables (acceptance of induction & level of education) is then recomputed separately for each of the subsamples. The tables produced in this manner are called the partial tables, & the relationships found in the partial tables are called the partial relationships. Partial relationships: the relationship between 2 variables when examined in a subset of cases defined by a 3rd (test) variable. EX: beginning w/ a zero-ordered relationship between income & attitudes toward gender equality. We want to see whether the relationship holds true among ♂ & ♀ (that is, controlling for gender). The relationship found among ♂ & the relationship found among ♀ would be the partial relationships, aka the partials.
The partial relationships are then compared w/ the initial relationship discovered in the total sample, often referred to as the zero-order relationship to indicate that no test variables have been controlled for.
Zero-order relationship: the relationship between 2 variables when no test variables are being controlled for (held constant). THE ELABORATION PARADIGM To begin, we must know whether the test variable is antecedent (prior in time) to the other 2 variables or whether it is intervening (interfering) between them because these positions suggest different logical relationships in the multivariate model. If the test variable is intervening, the independent variable (educational level) affects the intervening test variable (having friends deferred or not), which in turn affects the dependent variable (accepting induction). If the test variable is antecedent, the test variable affects both the ―independent‖ & ―dependent‖ variables. Replication: refers to the empirical outcome of the persistence of the observed initial relationship between 2 variables when a control variable is held constant. This supports the idea that the original, zero-order relationship is genuine. Whenever the partial relationships are essentially the same of the original relationship, the term replication is assigned to the result, regardless of whether the test variable is antecedent or intervening. This means that the original relationship has been replicated under test conditions. Explanation: a technical term that represents the elaboration outcome in which an antecedent test variable reveals the original (zero-order) relationship between 2 variables to be spurious. In other words, the original relationship disappears—is explained away—when the antecedent test variable is introduced. EX: There’s a positive relationship between the # of fire trucks responding to a fire & the amount of damage done. If more trucks respond, more damage is done. One might assume from this fact that the fire trucks themselves cause the damage. However, an antecedent test variable—the size of the fire—explains away the original relationship. Large fires do more damage than small ones, & more fire trucks respond to large fires than to small ones. Looking only at large fires, we would see that the original relationship vanishes (or perhaps reverses itself); and the same would be true looking only at small fires. Interpretation: a technical term that represents the research outcome in which a test or control variable is discovered to be the mediating factor thru which an independent variable has its effect on a dependent variable. EX: The intervening variable, deferment of friends, merely helps to interpret the mechanism thru which the relationship occurs. An interpretation doesn’t deny the validity of the original causal relationship but simply clarifies the process thru which that relationship functions. Specification: a technical term to refer to the elaboration outcome when an initially observed relationship between 2 variables is replicated among
some subgroups created by the control variable but not among others. In other words, when the partial relationships that result from the addition of a test variable differ significantly from each other, u will have specified the conditions under which the original relationship exists—for ex, among the elderly but not among children.
