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CMNS 260 (43)
Lecture 4

Lecture Week 4

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Department
Communication
Course
CMNS 260
Professor
Frederik Lesage
Semester
Spring

Description
• Quiz next week • KEYWORD: Causality recap: a causal explanation is “a statement in social theory about why events occur that is expressed in terms of causes and effects. They correspond to associations in the empirical world” (p.32) ◦ Acausal explanation requires: temporal, association, and that you eliminate plausible alternatives. • KEYWORD: Hypothesis:Ahypothesis is a statement from a causal explanation of a proposition that has at least one independent variable and one dependent variable, but it has yet to be empirically proven (87) ◦ 5 Characteristic of causal hypothesis At least 2 variables causal relationships between these variables expressed as a prediction or an expected future outcome logically linked to the research question (and therefore to a related theoretical framework) is falsifiable (capable of being tested against empirical evidence and shown to be true or false) ◦ Types of hypotheses Null hypothesis predicts there is no relationship if evidence support null hypothesis then?? Direct relationship (positive correlation) Indirect relationship (negative correlation) • Ways of stating causal relationships ◦ causes, ◦ x leads to y ◦ x is related to y ◦ x influence y ◦ x is associated with y ◦ if x higher, then y lower • Causal diagrams • Agood abstract will give you a sense for what is in the journal article • Example extra reading ◦ Unit of analysis is young women ◦ Variables: social networks ◦ Hypothesis 1: The primary motive for SNS use for bothAmerican and Japanese young women will be peer communication (regardless of nationality variable, both will still have as a primary motive…peer communication.) ◦ Hypothesis 2:American young women will be more likely to post photographs on Facebook while Japanese participants will be more likely to post diaries on Mixi (dependent variable: what are they posting on the sites, type of post. Independent variable: nationality) ◦ Findings 1: Hypothesis 1 was partially supported, on average for americans true, Japanese participants said it was for passing time first, communication second. ◦ Findings 2: Hyp 2 was confirmed, all of theAmerican participants used Facebook and the overwhelming majority of Japanese used Mixi. ◦ Can we generalize? No, not applied to males, or younger/older women • Dangers of not clearly defining and applying units of analysis ◦ ecological fallacy ◦ reductionism ◦ teleology ◦ tautology ◦ spuriousness • Importance of choosing appropriate unit of analysis ◦ ecological fallacy (dean made a mistake in logic) ◦ cheating and gender example-as the percentage of women in class increases, the number of cheaters increases because when there are more women, there are less men, and vice versa. There is also a negative correlation between women and cheating. ◦ However, when seeing which genders cheat in each section, the numbers of male cheaters and female cheaters are the same. • Reductionism is the opposite ◦ take a statistic (2/10 and changing it to 1/5 and then applying that logic to 5 students in the front row. • Critical analysis of mixi example • Spuriousness ◦ the connection between cell phone towers and birth rates ◦ full of shit ◦ Digging down, the science doesn’t make sense or support this. Measuring variables, reliability and validity • Measurement ◦ systematic observation can be replicated (by someone else) ◦ Measures in
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