Good evening,
For anyone who attends the IC Stand-up comedy event on Thursday at 7 p.m. in Emerson Suites, +3 extra credit points will be awarded.
The show features our own Chris Ptak opening up for Ben Weger (also a TV-R major).
Thanks.
Jack
Tuesday, April 21, 2015
Monday, April 20, 2015
Wednesday, April 15, 2015
Links to surveys
https://www.surveymonkey.com/collect/?collector_id=67087551
https://www.surveymonkey.com/collect/list?SurveyID=63501540
https://www.surveymonkey.com/collect/list?
https://www.surveymonkey.com/collect/list?SurveyID=63509299
https://www.surveymonkey.com/collect/list?SurveyID=63509510
https://www.surveymonkey.com/collect/list?SurveyID=63509505
https://www.surveymonkey.com/collect/list?SurveyID=63509652
https://www.surveymonkey.com/collect/list?SurveyID=63509884
https://www.surveymonkey.com/collect/list?SurveyID=63502958
https://www.surveymonkey.com/collect/list?SurveyID=63501540
https://www.surveymonkey.com/collect/list?
https://www.surveymonkey.com/collect/list?SurveyID=63509299
https://www.surveymonkey.com/collect/list?SurveyID=63509510
https://www.surveymonkey.com/collect/list?SurveyID=63509505
https://www.surveymonkey.com/collect/list?SurveyID=63509652
https://www.surveymonkey.com/collect/list?SurveyID=63509884
https://www.surveymonkey.com/collect/list?SurveyID=63502958
Wednesday, April 1, 2015
MMRM Exam #2 Review Sheet
Surveys
Two major types of surveys (descriptive and analytical)
Advantages and disadvantages
Data collection techniques in surveys (know one advantage and disadvantage of each)
How can we (perhaps) increase response rate?
Obstacles (some) of survey research
CATI system
Placement order of questions (general to specific, sensitive issues at the end, demographic info typically near the end etc.)
Double-barreled questions, filter questions
Content Analysis
Definition of content analysis
Characteristics (objective, systematic, empirical, quantitative)
Manifest vs. latent content
Importance of inter-coder reliability
Codebook and code sheets
Composite week
Purposes of content analysis
Unit of analysis in content analysis
Can we make conclusions about media effects based on content analysis?
Experiments
Advantages and disadvantages
Typical steps that a laboratory experimenter takes
Problem of confounding variables
Importance of randomization
Experimental designs—pretest-posttest-control group design
Solomon four-group design (pretest-treatment-posttest; pretest-posttest; treatment-posttest; posttest only)
Validity and reliability in experiments
Double-blind experiments
Qualitative Research
Four criteria used to evaluate qualitative research (article posted on blog):
naturalistic observation
contextualization
maximized comparisons
sensitized concepts
Positivist Paradigm vs. Interpretive Paradigm. Which is associated with Quantitative Techniques? Which is associated with Qualitative Techniques?
Positivist Paradigm vs. Interpretive Paradigm. Which is associated with Quantitative Techniques? Which is associated with Qualitative Techniques?
Major types of qualitative data collection techniques:
In-depth interviews
Focus Groups
Participant Observation
Case Studies
Understanding "Sense-Making"
In-depth interviews
Focus Groups
Participant Observation
Case Studies
Understanding "Sense-Making"
Putting together the qualitative report (what are the steps?)
Make sure you know the following:
NOM IV + NOM DV = chi-square
NOM IV + I/R DV = t-test/ANOVA
I/R IV + I/R DV = correlation
Make sure you know the following:
NOM IV + NOM DV = chi-square
NOM IV + I/R DV = t-test/ANOVA
I/R IV + I/R DV = correlation
Statistics
Definition
Central tendency vs. dispersion
Mean, mode, median
Frequencies
Type I vs. Type II error and the “null hypothesis”
Test-statistics—
Know when to use, how to solve, and how to interpret chi-square
Know when to use, how to solve, and how to interpret cross-tabulation
Know when to use, how to solve, and how to interpret t-test
Know when to use and how to interpret correlation
Degrees of freedom
The exam will feature at least one one chi-square problem, one cross-tabulation problem, one t-test problem, and one correlation interpretation problem.
There will also be a few questions about data interpretation. Specifically, you'll have see if a hypothesis is supported or not supported based on p < .05.
Practice problems:
The table above provides the expected and observed frequencies of IC students who drop out of school during any given year. The admissions department would like to know if their retention efforts are making a difference.
Practice problems:
Practice Statistical questions:
1. Chi-square.
Ithaca
School Year------------ Observed Freq. -----------Expected Freq.
Freshmen ------------------------15---------------------------- 27
Sophomores ---------------------20---------------------------- 35
Juniors ---------------------------10----------------------------- 20
Seniors ----------------------------15---------------------------- 25
Where o = observed frequency; e = expected frequency.
The table above provides the expected and observed frequencies of IC students who drop out of school during any given year. The admissions department would like to know if their retention efforts are making a difference.
Using the chi-square test, please tell me if there is a significant difference between the observed and expected frequencies (at the .05 level).
Are the retention efforts working? Why or why not?
2. Cross-tabulation.
I’m testing the following hypothesis:
Men are more likely than women to prefer TV sitcoms to TV dramas.
After collecting my data, I’m left with the following cross-tab:
Comedy TV
|
Drama TV
|
Total:
| |
Male
|
40
( )
|
42
( )
|
82
|
Female
|
29
( )
|
57
( )
|
86
|
Total:
|
69
|
99
|
168
|
Using chi-square, tell me whether or not the data support my hypothesis (at the .05 level). What use is this data to the ad agency representing Schick Quattro for Men (shaving products—face razors)?
T-Test
- The following are data of TV use per two weeks by gender. Using t-test (independent samples), determine the statistical significance with probability .05 between the two groups. Are these groups statistically different or not? Why?
Where the denominator is the difference between the standard error of the mean for each group, and X is the average/mean for each group.
Gender -------------------Male------------------- Female
Mean --------------------41 hour --------------- 56 hours
Participants ---------------10---------------------------20
Standard error of mean 2.01 -----------------------0.58
What is the t-value? What conclusions can you make?
Correlation
OK, so here’s the deal. I’m a TV news investigative reporter for Newswatch 16 and I’ve got a tip that a local grocery store is knowingly selling kid yogurt that contains unsafe levels of bacteria. The thing is, I’m, uh, “allergic” to numbers and I can’t make heads or tails out of this information. The tipster, a food safety scientist from Cornell, gave me the following info from his random survey of children who consumed the tainted yogurt, but I have no idea what it means. Can you help me? Do I have a story here? What do all these numbers mean? (5 points)
Correlation table of unsafe levels of bacteria in yogurt to intestinal illness among children 0-14:
Children ages 0 thru 2 .38
Children ages 3 thru 5 .17
Children ages 6 thru 8 .66
Children ages 9 thru 11 .22*
Children ages 12 thru 14 .04*
Understanding Correlation
Correlation Overview
So far, we've talked about Margin of Error, Standard Deviation, z-Score, t-Test, and Chi-square.
Remember that, depending on the type of measurement for the IV and DV, we use certain tests.
Specifically,--If the IV is nominal and the DV is nominal, we use chi-square.
If the IV is nominal and the DV is interval/ratio, we use t-test.
If the IV is interval/ratio and the DV is interval/ratio, we us correlation.
Correlation is the single most common statistical test in mass media research.
Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of people 5'7'', etc. Correlation can tell you just how much of the variation in peoples' weights is related to their heights.
Although this correlation is fairly obvious your data may contain unsuspected correlations. You may also suspect there are correlations, but don't know which are the strongest. An intelligent correlation analysis can lead to a greater understanding of your data.
Like all statistical techniques, correlation is only appropriate for certain kinds of data.
Correlation works for quantifiable data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such as gender, brands purchased, or favorite color
Rating Scales
Rating scales are a controversial middle case. The numbers in rating scales have meaning, but that meaning isn't very precise. They are not like quantities. With a quantity (such as dollars), the difference between 1 and 2 is exactly the same as between 2 and 3. With a rating scale, that isn't really the case. You can be sure that your respondents think a rating of 2 is between a rating of 1 and a rating of 3, but you cannot be sure they think it is exactly halfway between. This is especially true if you labeled the mid-points of your scale (you cannot assume "good" is exactly half way between "excellent" and "fair").
The main result of a correlation is called the correlation coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.
While correlation coefficients are normally reported as r = (a value between -1 and +1), squaring them makes then easier to understand. The square of the coefficient (or r squared) is equal to the percent of the variation in one variable that is related to the variation in the other. After squaring r, ignore the decimal point. An r of .5 means 25% of the variation is related (.5 squared =.25). An r value of .7 means 49% of the variance is related (.7 squared = .49).
If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an "inverse" correlation).
Info provided by Survey System.
So far, we've talked about Margin of Error, Standard Deviation, z-Score, t-Test, and Chi-square.
Remember that, depending on the type of measurement for the IV and DV, we use certain tests.
Specifically,--If the IV is nominal and the DV is nominal, we use chi-square.
If the IV is nominal and the DV is interval/ratio, we use t-test.
If the IV is interval/ratio and the DV is interval/ratio, we us correlation.
Correlation is the single most common statistical test in mass media research.
Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of people 5'7'', etc. Correlation can tell you just how much of the variation in peoples' weights is related to their heights.
Although this correlation is fairly obvious your data may contain unsuspected correlations. You may also suspect there are correlations, but don't know which are the strongest. An intelligent correlation analysis can lead to a greater understanding of your data.
Like all statistical techniques, correlation is only appropriate for certain kinds of data.
Correlation works for quantifiable data in which numbers are meaningful, usually quantities of some sort. It cannot be used for purely categorical data, such as gender, brands purchased, or favorite color
Rating Scales
Rating scales are a controversial middle case. The numbers in rating scales have meaning, but that meaning isn't very precise. They are not like quantities. With a quantity (such as dollars), the difference between 1 and 2 is exactly the same as between 2 and 3. With a rating scale, that isn't really the case. You can be sure that your respondents think a rating of 2 is between a rating of 1 and a rating of 3, but you cannot be sure they think it is exactly halfway between. This is especially true if you labeled the mid-points of your scale (you cannot assume "good" is exactly half way between "excellent" and "fair").
Most statisticians say you cannot use correlations with rating scales, because the mathematics of the technique assume the differences between numbers are exactly equal. Nevertheless, many survey researchers do use correlations with rating scales, because the results usually reflect the real world. Our own position is that you can use correlations with rating scales, but you should do so with care. When working with quantities, correlations provide precise measurements. When working with rating scales, correlations provide general indications.
The main result of a correlation is called the correlation coefficient (or "r"). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.
While correlation coefficients are normally reported as r = (a value between -1 and +1), squaring them makes then easier to understand. The square of the coefficient (or r squared) is equal to the percent of the variation in one variable that is related to the variation in the other. After squaring r, ignore the decimal point. An r of .5 means 25% of the variation is related (.5 squared =.25). An r value of .7 means 49% of the variance is related (.7 squared = .49).
A correlation report can also show a second result of each test - statistical significance. In this case, the significance level will tell you how likely it is that the correlations reported may be due to chance in the form of random sampling error. If you are working with small sample sizes, choose a report format that includes the significance level. This format also reports the sample size.
A key thing to remember when working with correlations is never to assume a correlation means that a change in one variable causes a change in another. Sales of personal computers and athletic shoes have both risen strongly in the last several years and there is a high correlation between them, but you cannot assume that buying computers causes people to buy athletic shoes (or vice versa).
The second caveat is that the Pearson correlation technique works best with linear relationships: as one variable gets larger, the other gets larger (or smaller) in direct proportion. It does not work well with curvilinear relationships (in which the relationship does not follow a straight line). An example of a curvilinear relationship is age and health care. They are related, but the relationship doesn't follow a straight line. Young children and older people both tend to use much more health care than teenagers or young adults.If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an "inverse" correlation).
Info provided by Survey System.
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