Variables and Hypotheses

From a lens to a test

• Tuesday: theory tells you what to look for and what would count as evidence
• A lens on its own cannot be right or wrong. It has nothing to be checked against.
• Theories become testable through variables
• A variable is a measurable concept that can take on different values
• Today is the translation step, from an idea to something you can count

The independent variable

• The independent variable is the proposed cause
• In an experiment, it is what you manipulate. In a correlational study, it is what you measure as the predictor.
• In livestreaming research it might be:
  • the stream’s category type, gaming or non-gaming
  • the streamer’s tenure on the platform
  • the stream’s concurrent viewer count

The dependent variable

• The dependent variable is the outcome, the thing you think is influenced
• In livestreaming research it might be:
  • chat messages per minute
  • viewer retention across a broadcast
  • the rate of subscriptions and donations
• If you cannot say which of your two variables is doing the influencing, you do not have a hypothesis yet

A worked hypothesis

In non-gaming streams, a higher proportion of chat messages are directed at the streamer than in gaming streams.

• Independent variable: category type, gaming or non-gaming
• Dependent variable: whether a message is directed at the streamer or broadcast to the room
• Both are things you can actually observe in this dataset

What testing it actually looks like

• Classify streams by category, gaming or non-gaming
• Code a sample of messages for whether each addresses the streamer
• Run a statistical test to see whether the predicted relationship holds
• Notice how much of that is coding, not statistics
• The codebook is where the hypothesis gets its teeth. That is Chapters 7 and 8.

The same structure, a different domain

News stories framed around individual human interest will generate more social media engagement than stories framed around systemic data.

• Independent variable: frame type
• Dependent variable: engagement
• Different topic, identical logic
• This is why the structure is worth learning once, carefully

Mediators: how and why

• A mediator explains how an independent variable affects a dependent one
• It is the mechanism sitting in between
• If category affects the rate of directed messages, the mediator might be the perceived pace of the stream
• Non-gaming streams may feel more conversational, and that conversational feel may be what invites viewers to address the streamer

Moderators: under what conditions

• A moderator changes the strength or direction of a relationship
• It answers the question “under what conditions?”
• The category-to-directed-message relationship might be moderated by audience size: holding on small streams, weakening on large ones
• A moderator is not a nuisance to control away. It is often the finding.

From relates-to toward mechanism

• “X relates to Y” is where a first project starts
• “X relates to Y, through this mechanism” adds a mediator
• “X relates to Y, through this mechanism, under these conditions” adds a moderator
• You do not need all three for a good White Paper
• Knowing the ladder exists keeps you from overclaiming on the first rung

Choosing a theory, first three steps

1. Identify your research question, specifically. “Livestreaming and community” is too broad. “Does the proportion of chat messages directed at the streamer differ between gaming and non-gaming streams?” is actionable.
2. Map the question to a theoretical domain. Group processes point toward social identity or uses and gratifications. Perception effects point toward cultivation, agenda setting, or framing.
3. Review how others studied similar questions. Convergence on a theory signals it is productive for that domain.

Choosing a theory, last two steps

4. Derive hypotheses or research questions. Let the theory generate the prediction, rather than picking a theory to decorate a prediction you already had.
5. Design measures that operationalize the concepts. Turn abstractions like identity or salience into variables you can measure.

Step 5 is operationalization, and it is the work of Chapters 7 and 8.

Hypothesis or research question?

• State a hypothesis when your theory is precise enough to predict a direction: more, less, higher, lower
• Ask a research question when the theory supports an expectation of difference but not of direction
• A research question is not a weaker hypothesis. It is an honest one.
• What you must not do is write a question, look at the data, and then report it as though it had been a directional prediction

Why the hypothesis comes first

• HARKing: Hypothesizing After the Results are Known
• You examine data, notice a pattern, then write as though you predicted it
• The p-value still prints, but it no longer means what it claims, because the hypothesis was generated by the data rather than before it
• Writing your prediction down before you look is a five-minute act that protects every inference that follows

Before next time

• The Annotated Manuscript is due this week, 25 points
• CITI Ethics Certification is due this week, 25 points. If you have not started, do it tonight.
• Write your working hypothesis or research question into your project repository with its independent and dependent variables named
• Read Chapter 6 before Tuesday: the prospectus, where the literature, the theory, and the method become one document