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Lecturer’s desk Physics- atmospheric Sciences (PAS) - Room 201 s c r e e n Row A Row B Row C Row D Row E Row F Row G Row H 131211109 87 Row A 14131211109.

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Presentation on theme: "Lecturer’s desk Physics- atmospheric Sciences (PAS) - Room 201 s c r e e n Row A Row B Row C Row D Row E Row F Row G Row H 131211109 87 Row A 14131211109."— Presentation transcript:

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2 Lecturer’s desk Physics- atmospheric Sciences (PAS) - Room 201 s c r e e n Row A Row B Row C Row D Row E Row F Row G Row H 131211109 87 Row A 14131211109 87 Row B 1514131211109 87 Row C 1514131211109 87 Row D 16 1514131211109 87 Row E 17 16 1514131211109 87 Row F 1716 1514131211109 87 Row G 1716 1514131211109 87 Row H 16 18 table Row A Row B Row C Row D Row E Row F Row G Row H 15141716 1819 16 15 18171920 17161918 2021 18172019 2122 19182120 2223 20192221 2324 18172019 2122 19182120 2223 2143 56 2143 56 2143 56 2143 56 2143 56 2143 56 2143 56 2143 56 Row J Row K Row L Row M Row N Row P 2143 5 2143 5 2143 5 2143 5 2143 5 1 5 Row J Row K Row L Row M Row N Row P 27262928 30 25242726 28 24232625 27 23222524 26 25242726 28 27262928 30 6 14 131211109 87 16151817 19 202122 614131211109 87 16 15 18 17 19 20212223 614131211109 87 16 15 18171920 2122 23 6 14 131211109 87 1624181719 20 2122 231525 6 14 131211109 87 1624181719 20 2122 231525 Row Q 2143 5 27262928 30 6 14 131211109 87 242223 21 - 15 25 37363938 40 34 3132 3335 69 87 13 table 14 18 192021

3 MGMT 276: Statistical Inference in Management Fall 2015

4 We’ll be starting this next week

5 By the end of lecture today 9/8/15 Use this as your study guide Questionnaire design and evaluation 5 Principles of questionnaire construction The importance of the iterative process in design Likert Scales for quantifying qualitative data Number of responders versus percentage of responders Time series versus cross-sectional comparisons Random sampling vs Random assignment Parameters versus statistics Descriptive or inferential Random versus non-random sampling techniques

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

7 Homework due- (Thursday, September 10 th ) On class website: please print and complete homework worksheet #2 & 3 We’ll be using this for a writing assignment on Thursday

8 Just for Fun Assignments Go to D2L - Click on “Content” Click on “Interactive Online Just-for-fun Assignments” Complete Assignments 1 – 7 Please note: These are not worth any class points and are different from the required homeworks

9 Schedule of readings Before next exam: Please read Chapters 1 - 4 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

10 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 Rev iew

11 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 Rev iew

12 Preview of Questionnaire Homework Rev iew

13 Preview of Questionnaire Homework Rev iew

14 Preview of Questionnaire Homework Rev iew

15 Preview of Questionnaire Homework Rev iew

16 QuestionnaireHomework

17 QuestionnaireHomework Average of these three scores Rev iew

18 QuestionnaireHomework Average of these two scores Rev iew

19 QuestionnaireHomework Variable label and scale values Rev iew

20 QuestionnaireHomework Average of these three scores Rev iew

21 QuestionnaireHomework Average of these two scores Rev iew

22 QuestionnaireHomework Variable label and scale values Rev iew

23 QuestionnaireHomework

24 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 Rev iew

25 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 Rev iew

26 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) 3. Use appropriate, natural and familiar language 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) Rev iew

27 5 Principles of questionnaire construction 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) Rev iew

28 Consider open-ended vs closed-ended questions 5. Consider lots of different formats for responses - can often modify a question into a closed question - pros and cons of each 5 Principles of questionnaire construction 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

29 Likert Scale is always a “summated scale” with multiple items. A measure that allows for rating the level of agreement with a statement. The score reflects the sum of responses on a series of items. - miniquiz (like Cosmo - ask several questions then sum responses) 1. Lower taxes and a smaller government will improve the standard of living for all. government will improve the standard of living for all. agree 1 --- 2 --- 3 --- 4 --- 5 disagree - For example, several questions on political views (coded so that larger numbers mean “more liberal”) 2. Marriage should be between one man and one woman agree 1 --- 2 --- 3 --- 4 --- 5 disagree 3. Evolution of species has no place in public education agree 1 --- 2 --- 3 --- 4 --- 5 disagree

30 Likert Scale is always a “summated scale” with multiple items. A measure that allows for rating the level of agreement with a statement. The score reflects the sum of responses on a series of items. Anchored rating scales: a written description somewhere on the scale Agree 1---2---3---4---5 Disagree Fully anchored rating scales: a written description for each point on the scale 1---------2---------3---------4---------5 Strongly Agree Strongly Disagree AgreeDisagree Neutral I prefer rap music to classical music

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

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

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

34 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

35 Time series versus cross-sectional comparisons: Trends over time versus a snapshot comparison Trends over time versus a snapshot comparison Time series design: Each observation represents a measurement at some point in time. Repeated measurements allow us to see trends. Cross-sectional design: Each observation represents a measurement at some point in time. Comparing across groups allows us to see differences. Please note: Any one piece of data can often (not always) be used in either a time series comparison or a cross-sectional comparison. It depends how you set up your question. Traffic accidents Does Tucson or Albuquerque have more traffic accidents (they have similar population sizes)? Does Tucson have more traffic accidents as the year ends and winter approaches?

36 Time series versus cross-sectional comparisons: Trends over time versus a snapshot comparison Trends over time versus a snapshot comparison Time series design: Each observation represents a measurement at some point in time. Repeated measurements allow us to see trends. Cross-sectional design: Each observation represents a measurement at some point in time. Comparing across groups allows us to see differences. Unemployment rate Is there an increase in workers calling in sick as the summer months approach? Do more young workers call in sick than older workers? Grade point average (GPA) Does GPA tend to go up or down as students move from freshman to sophomores to juniors to seniors? Does GPA tend to go up or down when you compare Mr. Chen’s class with Mr. Frank’s Freshman English classes?

37 Random sampling vs Random assignment Random sampling of participants into experiment: Each person in the population has an equal chance of being selected to be in the sample Population: The entire group of people about whom a researcher wants to learn Sample: The subgroup of people who actually participate in a research study Random assignment of participants into groups: Any subject had an equal chance of getting assigned to either condition (related to quasi versus true experiment) We know this one Let’s explore this one

38 Sample versus population (census) How is a census different from a sample? Census measures each person in the specific population Sample measures a subset of the population and infers about the population – representative sample is good What’s better? Use of existing survey data U.S. Census Family size, fertility, occupation The General Social Survey Surveys sample of US citizens over 1,000 items Same questions asked each year

39 Parameter – Measurement or characteristic of the population Usually unknown (only estimated) Usually represented by Greek letters (µ) Population (census) versus sample Parameter versus statistic pronounced “mu ” pronounced “mew ” Statistic – Numerical value calculated from a sample Usually represented by Roman letters (x) pronounced “x bar ”

40 Descriptive statistics - organizing and summarizing data Descriptive or inferential? Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected What is the average height of the basketball team? In this class, percentage of students who support the death penalty? Based on the data collected from the students in this class we can conclude that 60% of the students at this university support the death penalty Measured all of the players and reported the average height Measured all of the students in class and reported percentage who said “yes” Measured only a sample of the players and reported the average height for team Measured only a sample of the students in class and reported percentage who said “yes” To determine this we have to consider the methodologies used in collecting the data

41 Descriptive statistics - organizing and summarizing data Descriptive or inferential? Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected Men are in general taller than women Shoe size is not a good predictor of intelligence Blondes have more fun The average age of students at the U of A is 21 Measured all of the citizens of Arizona and reported heights Measured all of the shoe sizes and IQ of students of 20 universities Asked 500 actresses to complete a happiness survey Asked all students in the fraternities and sororities their age

42 Simple random sampling: each person from the population has an equal probability of being included Sample frame = how you define population Sample frame = how you define population =RANDBETWEEN(1,115) Let’s take a sample …a random sample Question: Average weight of U of A football player Sample frame population of the U of A football team Or, you can use excel to provide number for random sample Random number table – List of random numbers Random number table – List of random numbers 64 Pick 64 th name on the list (64 is just an example here) Pick 24 th name on the list

43 Systematic random sampling: A probability sampling technique that involves selecting every technique that involves selecting every kth person from a sampling frame Other examples of systematic random sampling 1) check every 2000 th light bulb 2) survey every 10 th voter You pick the number

44 Stratified sampling: sampling technique that involves dividing a sample into subgroups (or strata) and then selecting samples from each of these groups - sampling technique can maintain ratios for the different groups Average number of speeding tickets 17.7% of sample are Pre-business majors 4.6% of sample are Psychology majors 4.6% of sample are Psychology majors 2.8% of sample are Biology majors 2.8% of sample are Biology majors 2.4% of sample are Architecture majors 2.4% of sample are Architecture majors etc etc Average cost for text books for a semester 12% of sample is from California 7% of sample is from Texas 6% of sample is from Florida 6% from New York 4% from Illinois 4% from Ohio 4% from Pennsylvania 3% from Michigan etc

45 Cluster sampling: sampling technique divides a population sample into subgroups (or clusters) by region or physical space. Can either measure everyone or select samples for each cluster Textbook prices Southwest schools Southwest schools Midwest schools Midwest schools Northwest schools Northwest schools etc etc Average student income, survey by Old main area Old main area Near McClelland Around Main Gate etc Patient satisfaction for hospital 7 th floor (near maternity ward) 7 th floor (near maternity ward) 5 th floor (near physical rehab) 5 th floor (near physical rehab) 2 nd floor (near trauma center) 2 nd floor (near trauma center) etc etc

46 Snowball sampling: a non-random technique in which one or more members of a population are located and used to lead the researcher to other members of the population Used when we don’t have any other way of finding them - also vulnerable to biases Convenience sampling: sampling technique that involves sampling people nearby. A non-random sample and vulnerable to bias Judgment sampling: sampling technique that involves sampling people who an expert says would be useful. A non-random sample and vulnerable to bias Non-random sampling is vulnerable to bias

47 Does amount of sleep (4 vs 8 hours) affect class attendance? Selected 350 students from 38,000 undergraduates at U of Washington and randomly assigned students into two groups. Group 1 gets 4 hours sleep What is the independent variable? How many levels are there of the IV? Group 2 gets 8 hours sleep -Amount of sleep -2 levels (4 hours vs 8 hours) What is the dependent variable? What is population and sample? -Class attendance -Population: whole school -Sample: group of 350 students What is statistic ? -Average class attendance for 350 students Note: Parameter would be what we are guessing for the whole school based on these 350 students Quasi versus true experiment (random assignment)? -True Random sample? -Doesn’t say in the problem, so we have to assume “no”

48 Does gender of the teacher affect test scores for the students in California? Selected 150 students from Santa Monica and created two groups. Group 1 gets a female teacher What is the independent variable? How many levels are there of the IV? Group 2 gets a male teacher -Gender of teacher -2 levels (male vs female teacher) What is the dependent variable? What is population and sample? -Test Scores -Population: California -Sample: group of 150 students from Santa Monica What is statistic ? -Average test score for 150 students Quasi versus true experiment (random assignment)? -Doesn’t say in the problem, so we have to assume “no” Random sample? -No – Random sample would require that everyone in California be equally likely to be chosen.

49 Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The independent variable in this study was a. the performance of the subjects on the vision exam b. the subjects who ate the carrots c. whether or not the subjects ate the carrots d. whether or not the subjects had their vision tested

50 A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The dependent variable in this study was a. the performance of the subjects on the vision exam b. the subjects who ate the carrots c. whether or not the subjects ate the carrots d. whether or not the subjects had their vision tested Let’s try one

51 A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. This experiment was a a. within participant experiment b. between participant experiment c. mixed participant experiment d. non-participant experiment Let’s try one

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