Please sit in your assigned seat INTEGRATED LEARNING CENTER

Please sit in your assigned seat INTEGRATED LEARNING CENTER
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BNAD 276: Statistical Inference in Management Spring 2016
Welcome Green sheets

By the end of lecture today 1/28/16
Use this as your study guide By the end of lecture today 1/28/16 Questionnaire Design 5 Principles of Questionnaire Construction Likert Scales versus Likert Items

writing assignment forms notebook and clickers to each lecture
Remember bring your writing assignment forms notebook and clickers to each lecture A note on doodling Remember to register your clicker soon

Homework Assignment #3 & 4 (Has 2 parts)
Questionnaire construction using Likert scales instructions Important additional materials to help with homework assignments 3 & 4 How to report findings in a formal memorandum Example of formal memorandum for homework assignments 3 & 4 Rubric for homework assignments 3 & 4 Due: Thursday February 4th (both handed in together)

Schedule of readings Before next exam: February 18th Please read
Chapters in OpenStax Supplemental reading (Appendix D) Supplemental reading (Appendix E) Supplemental reading (Appendix F) Please read Chapters 1, 5, 6 and 13 in Plous Chapter 1: Selective Perception Chapter 5: Plasticity Chapter 6: Effects of Question Wording and Framing Chapter 13: Anchoring and Adjustment

Likert Scale is always a “summated scale” with multiple items.
All items are measuring the same construct. The score reflects the sum of responses on a series of items. - miniquiz (like Cosmo - ask several questions then sum responses) - For example, several questions on political views (coded so that larger numbers mean “more liberal”) 1. Lower taxes and a smaller government will improve the standard of living for all. agree disagree 2. Marriage should be between one man and one woman agree disagree 3. Evolution of species has no place in public education agree disagree

Likert Scale is always a “summated scale” with multiple items.
All items are measuring the same construct. The score reflects the sum of responses on a series of items.

I prefer rap music to classical music Agree 1---2---3---4---5 Disagree
Likert Scale is always a “summated scale” with multiple items. All items are measuring the same construct. The score reflects the sum of responses on a series of items. Anchored rating scales: a written description somewhere on the scale I prefer rap music to classical music Agree Disagree Fully anchored rating scales: a written description for each point on the scale I prefer rap music to classical music Strongly Disagree Strongly Agree Agree Neutral Disagree

5 Principles of questionnaire construction
1. Make sure items match research objectives & Identify what constructs you are trying to understand (Be explicit in identifying your constructs) 2. Responders have the answers to our questions We are tapping into their attitudes/beliefs/ knowledge Understand your research participants “think like” the responders / consider their sensibilities use appropriate, natural and familiar language (for them) 3. Use appropriate, natural and familiar language

3. Assessment should feel easy and clear, unthreatening Be clear, precise and concise (short questions) Minimize use of contingency questions Start with most friendly (least threatening) questions first then at the end “now a couple questions about you” (foot in the door phenomenon) Avoid double negatives For example: Agree or disagree? Teachers shouldn’t have less contact with parents 4. Avoid ambiguity and bias in your items Avoid “double-barreled” questions - Difficult to interpret answers Avoid leading or loaded questions - Can introduce bias Consider problem of acquiescence – Ask question in different ways (careful with coding)

5. Consider lots of different formats for responses Consider open-ended vs closed-ended questions - pros and cons of each - can often modify a question into a closed question Consider complementing your questionnaire with other forms of data collection (focus group or direct observation) Pilot – feedback – fix - pilot – analyze – fix - pilot – etc Respect process of empirical approach

Questionnaires use self-report items for measuring constructs.
Constructs are operationally defined by content of items. Questionnaire is a set of fixed-format, self-report items completed without supervision or time-constraint Response rate and power of random sampling Number of responders versus percentage of responders Really important regarding bias! Wording, order, balance can all affect results

Constructs are operationally defined by content of items. Questionnaire is a set of fixed-format, self-report items completed without supervision or time-constraint Response rate and power of random sampling Number of responders versus percentage of responders Really important regarding bias! Thank you so much for your question, this is an important topic practically, and an interesting topic theoretically. It is one of my favorites, partly because it is not at all obvious at first why the response rate matters so much (and actually that it matters in some ways more than sample size does). There are some famous really fun examples of how large samples that were non-representative were the cause of high profile errors like the headlines "Dewey Defeats Truman" in 1948. Another famous example of this happened in 1936 when it was predicted that Alf Landon would beat Franklin Roosevelt by ~14 percentage points. The sample size in that one was very respectable, over 2 million (not bad for ’36). But the problem was that the sample, while very large was not a random sample and was not representative. They sampled using telephone directories, and rosters from various clubs for a total mailing list of over 10 million. The problem was (as I suspect you are guessing) that it was the more affluent folks who even had telephones or were likely to be on the lists. So the sampling frame was biased, but this was compounded by the fact that only 20 percent of the people on the list responded which created an even larger bias (some types of folks are more likely than others to respond and to send in their mock ballots). That same year Gallop used a smaller representative sample (about 50,000 or so) and those results better predicted the outcome. There are many ways that bias can enter into sampling; it’s trickier than it seems at first. It can be tricky to get a truly random sample. And larger samples, while superior in some ways, cannot overcome a biased sample, in fact may even exaggerate the bias. Interesting huh? So if we have limited resources, and who doesn’t? It is better to have a smaller sample, say 100 that have been randomly sampled and hunt down every one of those 100, so you have a 100% response rate than it is to have a much larger sample of 1,000, or 100,000 that represents only 10% or only 50% of your target sample. When you hear people say that this is a “non-scientific sample”, that’s what they mean, that the sample is not random and therefore probably not representative. So, a poll through a magazine or on the Today Show, or using CNN or MSNBC or Fox viewers will almost certainly be biased and not a representative sample. I participated in a study one time where we measured the efficacy of a risky procedure on very premature infants and followed them for five years. The study budgeted quite a lot for the heroic follow-up protocols. We flew around the world to find and measure our sample and were able to report over a 95% success rate (even higher actually, some of the babies passed away) because it matters, if you want to get it right. It is definitely counter-intuitive at first how much it matters, but it is kind of interesting. Wording, order, balance can all affect results

Constructs are operationally defined by content of items. As “consumers” of questionnaire data – what should we ask? Number of responders versus percentage of responders Methodology of sampling Operational definitions of constructs Wording As “composers” of questionnaire data – how should we ask? - pilot – fix - pilot – analyze – fix - pilot – all the way through your design

The importance of the iterative process in design:
Iterative process and peer review is important skill in nearly all areas of business and science. Goal is to provide productive, useful and kind feedback

Preview of Questionnaire Homework
There are five parts: Statement of Objectives Questionnaire itself (which is the operational definitions of the objectives) Data collection and creation of database Creation of graphs representing results Generate a formal memorandum describing results

Preview of Questionnaire Homework

Questionnaire Homework

Average of these three scores
Questionnaire Homework Average of these three scores

Average of these two scores
Questionnaire Homework Average of these two scores

Variable label and scale values
Questionnaire Homework Variable label and scale values Variable label and scale values

Average of these three scores
Questionnaire Homework Average of these three scores

Average of these two scores
Questionnaire Homework Average of these two scores

Variable label and scale values
Questionnaire Homework Variable label and scale values Variable label and scale values

Questionnaire Homework

Preview of Questionnaire Homework
There are five parts: Statement of Objectives Questionnaire itself (which is the operational definitions of the objectives) Data collection and creation of database Creation of graphs representing results Generate a formal memorandum describing results

You’ve gathered your data…what’s the best way to display it??

Describing Data Visually
Lists of numbers too hard to see patterns Describing Data Visually Organizing numbers helps Graphical representation even more clear This is a dot plot

Describing Data Visually
Measuring the “frequency of occurrence” We’ve got to put these data into groups (“bins”) Then figure “frequency of occurrence” for the bins

Frequency distributions
Frequency distributions an organized list of observations and their frequency of occurrence How many kids are in your family? What is the most common family size?

Another example: How many kids in your family?
Number of kids in family 1 3 1 4 2 4 2 8 2 14 14 4 2 1 4 2 2 3 1 8

Number of kids in family 1 3 1 4 2 4 2 8 2 14 How many kids are in your family? What is the most common family size? Frequency distributions Crucial guidelines for constructing frequency distributions: 1. Classes should be mutually exclusive: Each observation should be represented only once (no overlap between classes) Wrong 0 - 5 5 - 10 Correct 0 - 4 5 - 9 Correct 0 - under 5 5 - under 10 10 - under 15 2. Set of classes should be exhaustive: Should include all possible data values (no data points should fall outside range) Wrong 0 - 3 4 - 7 8 - 11 Correct 0 - 3 4 - 7 No place for our family of 14!

Number of kids in family 1 3 1 4 2 4 2 8 2 14 How many kids are in your family? What is the most common family size? Frequency distributions Crucial guidelines for constructing frequency distributions: 3. All classes should have equal intervals (even if the frequency for that class is zero) Wrong 0 - 4 8 - 12 Correct 0 - 4 5 - 9 Correct 0 - under 5 5 - under 10 10 - under 15 missing space for families of 5, 6, or 7

4. Selecting number of classes is subjective
4. Selecting number of classes is subjective Generally will often work How about 6 classes? (“bins”) How about 16 classes? (“bins”) How about 8 classes? (“bins”)

Lower boundary can be multiple of interval size
5. Class width should be round (easy) numbers Remember: This is all about helping readers understand quickly and clearly. Lower boundary can be multiple of interval size Clear & Easy 8 - 11 Round numbers: 5, 10, 15, 20 etc or 3, 6, 9, 12 etc 6. Try to avoid open ended classes For example 10 and above Greater than 100 Less than 50

Let’s do one Step 1: List scores Step 2: List scores in order
Scores on an exam Let’s do one 53 58 60 61 64 69 70 72 73 75 76 78 80 82 84 87 88 89 91 93 94 95 99 Step 1: List scores Step 2: List scores in order Step 3: Decide whether grouped or ungrouped If less than 10 groups, “ungrouped” is fine If more than 10 groups, “grouped” might be better How to figure how many values Largest number - smallest number + 1 = 47 Step 4: Generate number and size of intervals (or size of bins) If we have 6 bins – we’d have intervals of 8 Sample size (n) 10 – 16 17 – 32 33 – 64 65 – 128 256 – 511 512 – 1,024 Number of classes 5 6 7 8 9 10 11 Let’s just try it and see which we prefer… Whaddya think? Would intervals of 5 be easier to read?

Let’s just try it and see which we prefer…
Scores on an exam Scores on an exam Score Frequency 80 – 84 5 Scores on an exam Score Frequency 53 58 60 61 64 69 70 72 73 75 76 78 80 82 84 87 88 89 91 93 94 95 99 Let’s just try it and see which we prefer… 6 bins Interval of 8 10 bins Interval of 5 Scores on an exam Score Frequency 80 – 84 5 Remember: This is all about helping readers understand quickly and clearly.

Let’s make a frequency histogram using 10 bins and bin width of 5!!
Scores on an exam Scores on an exam Score Frequency 80 – 84 5 Let’s make a frequency histogram using 10 bins and bin width of 5!!

Step 6: Complete the Frequency Table
Scores on an exam Step 6: Complete the Frequency Table Scores on an exam Score Frequency 80 – 84 5 Relative Cumulative Frequency 1.0000 .9285 .8214 .6428 .4642 .3213 .2142 .1785 .0714 .0357 Cumulative Frequency 28 26 23 18 13 9 6 5 2 1 Relative Frequency .0715 .1071 .1786 .1429 .0357 Just adding up the relative frequency data from the smallest to largest numbers Please note: Also just dividing cumulative frequency by total number 1/28 = .0357 2/28 = .0714 5/28 = .1786 Just adding up the frequency data from the smallest to largest numbers 6 bins Interval of 8 Just dividing each frequency by total number to get a ratio (like a percent) Please note: 1 /28 = .0357 3/ 28 = .1071 4/28 = .1429

Cumulative Frequency Data
Where are we? Scores on an exam Scores on an exam Score Frequency 80 – 84 5 Cumulative Frequency 28 26 23 18 13 9 6 5 2 1 Relative Frequency .0715 .1071 .1786 .1429 .0357 Cumulative Rel. Freq. 1.0000 .9285 .8214 .6428 .4642 .3213 .2142 .1785 .0714 .0357 Cumulative Frequency Data Cumulative Frequency Histogram

“Who is your favorite candidate?”
Simple Frequency Table – Qualitative Data Who is your favorite candidate Candidate Frequency Hillary Clinton 45 Bernie Sanders 23 Joe Biden 17 Jim Webb 1 Other/Undecided 14 Number expected to vote 9,900,000 5,060,000 3,740,000 220,000 3,080,000 We asked 100 Democrats “Who is your favorite candidate?” Relative Frequency .4500 .2300 .1700 .0100 .1400 Percent 45% 23% 17% 1% 14% If 22 million Democrats voted today how many would vote for each candidate? Just divide each frequency by total number Just multiply each relative frequency by 22 million Just multiply each relative frequency by 100 Please note: 45 /100 = .4500 23 /100 = .2300 17 /100 = .1700 1 /100 = .0100 Please note: .4500 x 22m = 9,900k .2300 x 22m = 35,060k .1700 x 22m = 23,740k .0100 x 22m= 220k Please note: .4500 x 100 = 45% .2300 x 100 = 23% .1700 x 100 = 17% .0100 x 100 = 1% Data based poll on 9/15

Step 2: List scores in order Step 3: Decide grouped
Scores on an exam Step 2: List scores in order Step 3: Decide grouped Step 4: Decide 10 for # bins (classes) 5 for bin width (interval size) Step 5: Generate frequency histogram Scores on an exam Score Frequency 80 – 84 5 Score on exam 6 5 4 3 2 1

Generate frequency polygon
Scores on an exam Generate frequency polygon Plot midpoint of histogram intervals Connect the midpoints Scores on an exam Score Frequency 80 – 84 5 Score on exam 6 5 4 3 2 1

Generate frequency ogive (“oh-jive”)
Scores on an exam Generate frequency ogive (“oh-jive”) Frequency ogive is used for cumulative data Plot midpoint of histogram intervals Connect the midpoints Scores on an exam Score 95 – 99 80 – 84 Score on exam 30 25 20 15 10 5 Cumulative Frequency 28 26 23 18 13 9 6 5 2 1

Pareto Chart: Categories are displayed in descending order of frequency

Stacked Bar Chart: Bar Height is the sum of several subtotals

Simple Line Charts: Often used for time series data (continuous data)
Simple Line Charts: Often used for time series data (continuous data) (the space between data points implies a continuous flow) Note: For multiple variables lines can be better than bar graph Note: Fewer grid lines can be more effective Note: Can use a two-scale chart with caution

Pie Charts: General idea of data that must sum to a total (these are problematic and overly used – use with much caution) Exploded 3-D pie charts look cool but a simple 2-D chart may be more clear Exploded 3-D pie charts look cool but a simple 2-D chart may be more clear Bar Charts can often be more effective

Thank you! See you next time!!

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