Adjacency Expression
The logical definition of a tie between two network members based on a specific response to an Alter Pair question. This expression takes the raw response to an evaluation of a connection between two alters and defines a dichotomous relationship tie value as “true” or “false”. For example, if an alter pair question asks how often two people interact with each other and the response options are, “never, rarely, sometimes, often” and adjacency expression can be written to define a relationship between these two alters as “true” if the two alters interacted “rarely”, “sometimes”, or “often”. Another expression can be written to define a relationship as those who interact with each other more than “rarely” (i.e. “sometimes” or “often”).
Alter
A network member named by a respondent. Can be a person, organization, role, object, etc. depending on the research questions.
Alter Prompt Question
This is the “name generator” or the prompt to the respondent to name the members of the network. These can be specific types of relationships (“We are interested in who you collaborate with. Please give me a list of research staff you have worked with on a project in the last 2 years…) or generic (“Give me a list of people that you know and who know you.”)
Alter Pair Question
A question that is pre-programmed to appear for each unique dyad on the alter list (with some options for presentation and skipping certain types of alter-alter combinations).
Ego ID Prompt
Questions filled out in the beginning of the interview that produce the unique ID for the participant. The responses to these questions are concatenated into an ID string. This string is visible on each screen of an interview in the upper right hand corner of the question page. These questions do not usually include demographic questions unless they are helpful in identifying the respondent uniquely with a code.
Multisession study
A series of EgoWeb 2.0 interviews that is intended to be completed with the same respondent/participant. These interviews are linked together with a variable on the EgoID page. This is for conducting longitudinal data collection or for repeated intervention sessions with the same participant.
Single session study
An EgoWeb 2.0 interview that is intended to only happen once with the same respondent.
Skip Logic
Logic determining whether a question should only appear contingent upon responses to previous questions. For example, a study might only ask about sex partners if the respondent had previously stated that she had sex within the past 12 months. Skip logic controls this sort of contingent display of questions.
Study (and .study)
This is the term that stands for the entire set of settings for an interview in EgoWeb 2.0 including question presentation, order, skip logic, global settings, etc. It is also the extension of the XML file for any EgoWeb 2.0 export (e.g. if an interview is called “Research_Project” the exported file will be called “Research_Project.study”.
Subject Type
This is the term for a category of question. Options include “EGO”, “NAME_GENERATOR”, “ALTER”, “ALTER_PAIR”, and “NETWORK”. Selecting the Subject Type establishes the default functions of the questions as well as the optional functions.
Values for Unanswered Questions
These are the values entered into the resulting interview data set for questions that did not receive a response during an interview, such as questions that were never answered because the interview was not completed, questions that were skipped due to the logic of the interview, or questions that the respondent could not answer and either refused or said that they did not know the answer. Values can be anything a survey designer wants them to be, but they should be easily distinguished from the values given to other response options. They are typically defined as negative numbers (-8, -9, etc.) while valid response options are typically given positive numeric responses (0, 1, 2, etc.). In typical statistical data analysis these values are treated as “missing” and, therefore, assigning them negative values allows or ease of re-coding during data processing prior to analysis.