independent variable

Examples of independent variable in the following topics:

• Formulating the Hypothesis

  • In this hypothesis, the independent (causal) variable is civic engagement and the dependent variables (or effects) are the qualities of government.
  • For example, if the hypothesis is a causal explanation, it will involve at least one dependent variable and one independent variable.
  • In research, independent variables are the cause of the change.
  • In other words, the value of a dependent variable depends on the value of the independent variable.
  • If there is no relationship, then the value of the dependent variable does not depend on the value of the independent variable.

• Correlation and Causation

  • A positive correlation means that as one variable increases (e.g., ice cream consumption) the other variable also increases (e.g., crime).
  • A negative correlation is just the opposite; as one variable increases (e.g., socioeconomic status), the other variable decreases (e.g., infant mortality rates).
  • Causation refers to a relationship between two (or more) variables where one variable causes the other.
  • change in the independent variable must precede change in the dependent variable in time
  • it must be shown that a different (third) variable is not causing the change in the two variables of interest (a.k.a., spurious correlation)

• Analyzing Data and Drawing Conclusions

  • In this case, it's because of a third variable: temperature.
  • Regression analyses measure relationships between dependent and independent variables, taking the existence of unknown parameters into account.
  • More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.
  • Qualitative data can involve coding--that is, key concepts and variables are assigned a shorthand, and the data gathered is broken down into those concepts or variables .
  • It is important to remember, however, that correlation does not imply causation; in other words, just because variables change at a proportional rate, it does not follow that one variable influences the other .

• Regressing position on attributes

  • Two dummy variables have been constructed to indicate whether each donor is a member of the "capitalist" or the "worker" group.
  • Figure 18.14 shows the dialog to specify the dependent and the multiple independent vectors.
  • Note that all of the independent variables need to be entered into a single data set (with multiple columns).
  • The correlation matrix shows a very high collinearity between being in the workers group (variable 3) and participation in coalitions (variable 4).
  • We could be interested in predicting a relational attribute of actors (e.g. centrality) using a mix of relational and non-relational independent variables.

• The Scientific Method

  • If the hypothesis is a causal explanation, it will involve at least one dependent variable and one independent variable.
  • A dependent variable is a variable whose values or qualities are presumed to change as a result of the independent variable.
  • In other words, the value or quality of a dependent variable depends on the value of the independent variable.
  • If there is no relationship, then the value or quality of the dependent variable does not depend on the value of the independent variable.
  • In a study of the influence of gender (as a value) on promotion, the independent variable would be gender/sex.

• Summary

  • These tools allow us to examine hypotheses about the relational and non-relational attributes of actors, and to draw correct inferences about relations between variables when the observations (actors) are not independent.

• Introduction to explaining attributes of networked actors

  • In all of these cases we are focusing on variables that describe individual nodes.
  • These variables may be either non-relational attributes (like gender), or variables that describe some aspect of an individual's relational position (like between-ness).
  • In most cases, standard statistical tools for the analysis of variables can be applied to describe differences and associations.
  • But, standard statistical tools for the analysis of variables cannot be applied to inferential questions -- hypothesis or significance tests, because the individuals we are examining are not independent observations drawn at random from some large population.

• Introduction to describing one network

  • First, and most important, social network analysis is about relations among actors, not about relations between variables.
  • We learn about the mean of a set of scores on the variable "income."
  • We can treat Fred and Sue as independent replications.
  • These are not "independent" replications.
  • Applying them when the observations are not independent can be very misleading.

• Hypotheses about one mean or density

  • Of the various properties of the distribution of a single variable (e.g. central tendency, dispersion, skewness), we are usually most interested in central tendency.
  • In the analysis of variables, this is testing a hypothesis about a single-sample mean or proportion.
  • Using the classical formula for the standard error of a mean (s / sqr(N)) we obtain a sampling variability estimate of .0528.
  • This is because the standard formula is based on the notion that all observations (i.e. all relations) are independent.
  • In general, the standard inferential formulas for computing expected sampling variability (i.e. standard errors) give unrealistically small values for network data.

• Surveys

  • A successive-independent-samples design draws multiple random samples from a population at one or more times.
  • For successive independent samples designs to be effective, the samples must be drawn from the same population, and must be equally representative of it.
  • Unlike with a successive independent samples design, this design measures the differences in individual participants’ responses over time.
  • Furthermore, measurements will be more reliable when the factor being measured has greater variability among the individuals in the sample that are being tested.