This is a summary of the course Quantitative Methods in Media and Comm. (CM2005) with contents from pre recorded lectures of all 7 weeks as well as the tutorial notes and step by step on how to do the analysis and conclusion making.
Lecture 6/9/22
• Theory: interrelated set of variables formed into hypothesis that specify the
relationship among variables with the purpose of explaining natural phenomenon
• Theoretical rationale: provides an explanation about why or how variable X
influence variable Y
o Example: how violent media (X) can influence a person’s well-being (Y)?
• Variable: characteristic that varies among the people being studied
• Independent Variable (IV): those that influence the outcome
• Dependent Variable (DV): presumed result of the influence of the IV
Two types of hypotheses:
• The goal of hypothesis testing is to reject one hypothesis and accept the other
1. Null hypothesis (H0)
a. No difference/change
b. Never stated, always implied
2. Alternative hypothesis (H1)
a. Statement of prediction
b. Actual research hypothesis
Type of H1 hypothesis:
• Directional hypothesis
o Different/effect in particular direction
o E.g., media violence has positive impact on well-being
• Non-directional hypothesis
o Difference/effect but not in particular direction; avoid when possible
o E.g., media violence has impact on well-being
Variables that influence the relationship of IV and DV
• Spurious relationship: when the independent and dependent variables seem related
but are in fact not
,• Confounding variables: variables that aren’t measured but might influence the
observed relationship (a third variable that comes into play)
• Moderating variables: a relationship might exist for one party but not the other
o E.g., gender (there may be a relationship between the IV and DV for males
but not females)
o E.g., H1: younger girls use more social media than younger boys
• Control variable: special type of IV that researchers measure as they potentially
influence the DV
o Don’t have assumption but want to make sure all possibilities
• Intervening/Mediating variable: mediate the effects of the IV on the DV
,Week 2 Overview
Pre-recorded lecture 13/9/22
• Data are only as good as the instrument that you use to collect them
• Good science also includes careful operationalization (selection of measurements)
Operationalizing concepts and using scales
• The variables in your RQ and hypothesis may be abstract concepts
o E.g., press freedom, materialism, friendship, etc.
• Abstract concepts
o Can’t be measured with one question (e.g., gender, sex, educational level,
ethnicity, nationality, age, etc.)
o How to measure? Use a set of question (scale)
Scale
• Contain multiple items (questions or statements) which participants have to react to
• E.g., Material Value Scale for children with 3 questions (MVS-c)
Material centrality (MVS-c01): “Do you think it’s important to own expensive
things?”
Material happiness (MVS-c07): “Does buying expensive things make you happy?”
Material success *MVS-c13): “Do you like children who have expensive things more
than you like other children?”
For each item of a scale, an identical number of answer options should be provided.
For the MVS-C there were 4 answer options: (1) no, not at all, (2) no, not really, (3)
yes, a little, (4) yes, a lot.
• How to calculate scale scores?
o Sum the scores on the separate items
o Average the scores on the separate items
o E.g., one participant in the sample scored a 3 on all 3 MVS-c questions then,
the sum scale score is 3+3+3=9
Average scale score would be (3+3+3)/3=3
, • Choosing whether to sum or average the scores depend on the need of the
researcher (how the final variable will be used)
• Sum scores are used to categorize the participants
o E.g., intelligence, autism, addiction, total number of hours spent with media,
etc.
• Average scale scores are used to interpret the scale scores with the original answer
options
o E.g., level of materialism of children who chose option 3 on all the questions
hence, the child would be a little materialistic
• Some scales contain both positive and negative items. In such cases, the negative
items need to be recoded before the scale score is calculated
o E.g., using data set that contains a scale measuring “grateful disposition” with
6 items
1 = strongly disagree
2 = disagree
3 = slightly disagree
4 = neither agree nor disagree
5 = slightly agree
6 = agree
7 = strongly agree
1. I have so much in my life to be thankful for
2. If I had to list everything I felt grateful for, it would be a very long list
3. When I look at the world, I don’t see much to be grateful for
4. I am grateful to a wide variety of people
5. As I get older, I find myself more able to appreciate the people, events,
and situations that have been part of my life history
6. Long amounts of time can go by before I feel grateful to something or
someone
Item 1, 2, 4, and 5, a high score (7 = strongly agree) indicates gratefulness
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