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#OPEN STATA 13 IN R CODE#
The first line of code gives those who felt fairly or very safe a code of 1 (safe) and those who felt a bit or very unsafe a code of 0 (not safe).
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label define safe 0 “not safe” 1 “safe”.recode walkdark (1 2 = 1) (3 4 = 0), generate (safety).Clear the information from the “recode-Recode categorical variables” dialog box and repeat the process, selecting walkdark in place of age, assigning values (1 2 = 1 3 4 = 0) and calling the new variable “safety.”Īlternatively, type the following code directly into the Command window: label define agegroup 1 “16–24” 2 “25–34” 3 “35–44” 4 “45–54” 5 “55–64” 6 “65–74” 7 “75+”įor greater clarity at this initial exploratory stage, we can also dichotomise the walkdark variable into two groups, using the same process as above, into those who feel fairly or very safe and those who feel a bit or very unsafe.Once all labels are complete, press OK to return to the previous box, then click Close to return to the “Variables Manager” box.įrom the “Variables Manager” box, find the label you have created from the drop-down list in the “Value label” text box, then click Apply to attach the label to the variable.Īlternatively, you can type the following commands directly into the Stata command window: By dividing age into categories, we can make comparisons between age groups and start to explore non-linear relationships, noting that we lose information by grouping age into categories while gaining additional information through analyses that categorical variables allow for, such as cross-tabulation and regression with dummy variables.įigure 5: Using the Create Label Dialog Box in Stata. In the case of age, there are many occasions where this relationship is non-linear.
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This approach assumes a relationship between the dependent variable and age that is the same across ages. Age in its continuous form ensures a more parsimonious model in regression analyses with only one coefficient for interpretation which can identify significant trends between age and the outcome variable where they exist. Where age is provided in a dataset on a continuous scale, the researcher is provided with the option to include it as a continuous variable or divide it into groups as a categorical variable. While many demographic variables are categorical by nature, for example, religion, race, or nationality, those that are continuous by nature, such as age or income, can be recorded and managed either as continuous or categorical, both at the data collection stage or later in the data processing stage. A researcher might also want to consider whether age acts as a moderator variable, such that the relationship between an independent and dependent variable changes according to how old a respondent is.Īge can be considered as a continuous, ratio variable. A researcher might want to understand the direct relationship between age and some outcome or they might recognise that changes occur with age and, thus, they must control for age when considering other relationships.
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Measurements of age can be of particular interest to social scientists in understanding those changes in behaviours, beliefs, attitudes, and lifestyles that coincide with age. These help to identify representativeness of a particular sample, as well as describing participants and providing valuable information to aid analysis. # and 'reghdfe' for unnested fixed effects and one way clustering.1 Including Demographic Variable Age in AnalysisĪge is a key demographic variable, frequently recorded in survey data as part of a broader set of demographic variables, such as education, income, race, ethnicity, and gender. To compare the various approaches, I use the Petersen dataset.