PRR475 Stynes Exercises Page Exercise B5: Evaluation study proposal. Each group will prepare an evaluation study plan/proposal. The study plan should follow the guidelines below. You will likely need to begin by narrowing your problem and presenting a concise statement of the program you are evaluating and the study objectives. Some of your background work that is relevant to the study you propose may go into the "Lit Review" - you may call it "background" if you wish. The new information you will be adding is mostly in "Methods" section, but you will need to rework other material to put it into a concise and precise proposal format. Final paper should be roughly 8-12 pages double spaced, not counting attachments. The methods section should be at least half of the proposal. DUE - Last day of class. December 10.

Evaluation Proposal or Study Plan

A. PROBLEM : Work from broad problem down to the focus of the proposed study. Provide background on the problem or program and show where the proposed study fits into a program of research or evaluation studies. Use objective language - to test vs to prove, to assess or evaluate vs to show or demonstrate that the program is effective. Keep in mind the client for study. Who will use results for what kinds of decisions?

Evaluation - Define the program to be evaluated by reviewing written documents (program proposals, plans, budget, organization chart, program history,...) , talking with people (program administrators, managers, participants, & others), and observing (observe various parts of the program in operation). You did this in first 8 weeks.

B. OBJECTIVES: A concise, specific listing. Usually two or three clear objectives is sufficient. The objectives guide the rest of the proposal and your final report, as methods and results should be indicated for each objective. DO NOT list methods or procedures as objectives ( e.g. to conduct a survey of visitors).

Evaluation - Study objectives should indicate the criteria by which the program will be evaluated (prioritize these). Suchman's categories are a useful guide to general evaluation criteria ( effort, performance, adequacy, efficiency, process, equity). E.g. assess the benefits and costs of program; assess effectiveness, performance, adequacy, etc; identify who benefits, who pays for program.

C. LITERATURE REVIEW or DISCUSSION. This is the place for more extended discussion of literature and background for the study. It is more common in research studies than evaluations. "Literature" can include research articles, previous studies, internal documents and plans, as well as more popular magazine and newspaper articles. This section of the proposal demonstrates you have done your homework and are familiar enough with the topic/program to study it. Key here is to review related research and theory, not everything. In program evaluations, this is good place to provide a more extensive history of the program, to review previous evaluation studies of this or related programs, and to discuss key concepts, theories or research/evaluation approaches that are relevant.

D. METHODS: Describe the procedures to be carried out to achieve each of the study objectives. Procedures cover what you intend to do to whom, when, where and how. Also cover why by linking procedures to your objectives. In longer proposals you may also justify your procedures by discussing the advantages and disadvantages of alternative approachess. Typical sub-sections for procedures:

· Study Population· Sampling Procedures· Definition of key concepts· Measurement procedures· Overall study design, controlling for error· Field Procedures· Data Processing· Analysis· Reporting of results· Limitations and potential sources of error

E. ATTACHMENTS. Budget, Timeline, Questionnaires, Maps, Program documents, References

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PRR475 Stynes Exercises Page

PRR 475, Exercise B1 : Questionnaire Design

Design a one page questionnaire for a survey of one of the following populations: 1) Visitors to a particular park (Yellowstone National Park, Potter Park zoo etc.)2) A resident population to be served ( MSU students, residents of Lansing etc.)3) A population of managers or administrators4) A population of tourists to an area (e.g. Tailgaters at MSU football games,

overnight visitors to the Lansing area staying in motels, etc)5) People participating in a recreation program (you name it) 6) Or any other population of people that you define.

First clarify/define the population you have chosen, as necessary. In all cases assume you are developing a self-administered questionnaire - either mailed or distributed to individuals and completed in a written form.

(1). First formulate some specific objectives, questions, or hypotheses about the selected population. Put yourself in a management, planning or evaluation position to help identify useful questions. Then identify the information you will need to gather in your survey to answer the questions or test the hypotheses. Consider background variables, socio-economic information, participation and trip characteristics, and other cognitive (what do they know?), affective (how do they feel about...?), and behavioral (what do they do?) information. Your "mini-study" should include:

a. A descriptive component - pick at least three characteristics of the population that you will describe, i.e., report frequencies, percentages, averages, etc. example - determine the percentage of visitors who are male (be a bit more creative than this please), average number of visits per year,….

b. An explanatory component - formulate at least one hypothesis about a relationship between two (or more) variables. State your hypothesis and identify the variables you must measure to test it. Note that you may use some of the same variables from (a). example - hypothesize that males are more likely than females to participate in a given activity, or approve of a particular policy. Try to think up some meaningful relationships to study.

In no more than one page, define the population, state your objectives, your hypothesis, and identify the variables you will need to measure. After completing the questionnaire (see part 2 below) , identify which questions (by number) are measuring which variables, and indicate the scale or level of measurement for each (nominal, ordinal, interval, ratio).

(2). Design a one page questionnaire to gather the above information. Assume this will be a self-administered questionnaire distributed to a random sample of your population. Prepare the one page questionnaire as if this were the instrument to be used in the survey. That is, pay attention to instructions, wording, question types, formatting, question sequencing, layout, and overall design of your questionnaire. Assume the cover letter will be drafted by someone else.

TURN IN TWO PAGES. First page covering part 1 above (complete the form below), second page the actual survey instrument. Keep in mind your study population, topic, and type of survey (self-administered instrument) in designing the questions. Hand in two copies of the completed exercise (both pages): one for the instructor, another to be exchanged with a fellow student. DUE. November 3

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PRR475 Stynes Exercises Page

NAME: ____________________________PAGE 1 for Questionnaire Design Exercise

STUDY POPULATION :

RESEARCH OBJECTIVES (at least one descriptive, one a hypothesis/relationship)

1.

2.

3.

VARIABLES TO MEASURE

VARIABLE Question No (s) Measurement Scale

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PRR475 Stynes Exercises Page

PRR 475 Sampling Exercise - Exercise B2, Lab Nov 4.

Yogi Bear park has a single entrance gate that is not normally staffed. The management would like an estimate of how many users the park receives during the month of October 1998. Use will be measured by having someone count the number of visitors entering the park during a sample of hours. The department has funds to pay for 24 hours of observation. Propose a sampling plan to identify which days/hours the counter will be stationed at the entrance. Assume the population is the 372 daylight hours in October - 12 daylight hours from 8 am to 8 pm during the 31 days in October. You will choose a sample of 24 hours from this population.

This is a typical problem involving sampling over time. Note that the sampling unit here is the hour (not the individual visitor). Your assignment, (should you accept it), is to determine which 24 one hour time periods to make observations in order to have a representative sample of time periods in October. Use a calendar to indicate the days and times you would schedule observations.

USE THE FOLLOWING THREE APPROACHES TO GENERATE A SAMPLE.

a. SIMPLE RANDOM SAMPLE (SRS): First generate a simple random sample using any random process for choosing hours. (e.g. put 31 numbered slips of paper in one envelope for the day of the month and 12 numbered slips for the hour. Choose one slip from each envelope to get the day and time for each period to be sampled. Sample with replacement so you may sample more than one hour on a given day. Map your sample on a calendar and evaluate its “representativeness”.

b. JUDGEMENT SAMPLE: Next use your judgement to choose 24 time periods that will be representative of the 372 daylight hours in May. Briefly explain how you chose this sample.

c. STRATIFIED SAMPLE: Use some of the factors you considered in step b to stratify the population into at most six “homogeneous “ subgroups . Decide what percentage of the 24 hours in your sample that you want from each subgroup and then use a random process to choose the hours in each strata.

For each case:

1. Prepare a calendar with the sampling dates and times indicated .2. Describe how you generated the sample.3. Try to work out how you would estimate use for the month from the observations you have proposed. This is an

exercise in working out the analysis in advance so you are sure that you have gathered appropriate and sufficient data and will know what to do with the data when you get it.

4. Compare the three sampling approaches (SRS, judgement, stratified) - which works best in this case? When would you use each approach?

Other Sampling Practice: For each of the following problems define a suitable study population and propose a sampling scheme. What alternatives would you consider to generate a representative sample? Discuss the pros and cons of possible sampling frames and designs.

a. The state wants to measure the impacts of Michigan seasonal home owners and users on the economy and demands for local services including recreation.

b. Michigan State Parks wants a general profile of state park visitors.c. Traverse City wants a study of its tourism market.d. The Michigan Department of Natural Resources wishes to estimate use of boating facilities

throughout the state.e. The PRR Department Chair wants to know how faculty feel about a set of policy issues.f. Korea wants to understand their international tourism market.g. The City of East Lansing wants to assess the recreation needs of residents.

DUE- Nex week in lab, Nov 11.

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PRR475 Stynes Exercises Page

PRR 475: SPSS FOR WINDOWS - BASICS, LAB Nov 11-18.

SPSS stands for Statistical Package for the Social Sciences. Other popular statistical software includes SAS, SYSTAT and MINITAB. SPSS is well suited to analysis of social science/survey data. Like all statistical packages it works with a table of data with cases as rows and variables as columns (just like an Excel Table - in fact you can import Excel tables directly to SPSS and vice versa). For survey data, each case is a respondent or questionnaire and each variable is usually a numeric coding of the response to a single question on the survey instrument. Statistical packages prefer to analyze data in numeric form so one codes variables like GENDER as something like 1=male, 2=female ( 1=male, 0=female is better). We will be analyzing data from the 1994 Huron Clinton Metropark visitor survey. The HCMA survey dataset includes over 3,000 cases and more than a hundred variables. The file you will work with is a random sample of 1,000 of these cases. A few of the messier variables have been dropped for this exercise.

You will need copies of the HCMA475.SAV file to complete this exercise. You may retrieve directly in SPSS in microlabs from the Course AFS space. – msu/course/prr/475/Stynes/hcma475.sav.

1. Loading SPSS-PC . Run SPSS by selecting the SPSS program from the START menu or double clicking the HCMA475.SAV file name in course AFS space.

When SPSS opens you will see the Data Window, a spreadsheet for entering data with SPSS menus and toolbar at top of screen. Maximize the window. Run some of the tutorials if you wish -- Click HELP on menu bar and choose Tutorials (if available in labs). Follow instructions.

2. Retrieve a data file. To open file in SPSS, from menu select FILE, OPEN and complete the Dialog Box. If you loaded SPSS by double clicking an *.SAV file, it will automatically be loaded when program opens.

In Drives section, select u drive and point to the PRR 475 Course AFS space. MSU/COURSE/PRR/475/Stynes subdirectory. Select one of the .SAV files (HCMA475.SAV in this case) and Click OK to open it. (OR double click the file name - the SAV extension is the default for SPSS).

When the file is loaded, you will see the data in the data window in spreadsheet format. Variable names are at top of columns. See HCMA codebook and questionnaire to match variables with items on the questionnaire. To see codes as Values rather than numbers, choose View and check Value Labels. To see information about any variable, right click the variable name at top and select "Define Variable". You are now ready to run statistical analysis.

3. To run Statistical Procedures choose the STATISTICS option on menu and then the statistical procedure you wish to run. We will work mostly with the first three items

SUMMARIZE

FREQUENCIES frequencies for nominal & ordinal variables

DESCRIPTIVES means etc. for interval scale variables

EXPLORE Exploratory data analysis procedures to see distributions

CROSSTABS Tables for nominal or ordinal (few categories) variables, Chi square test

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File Edit Transform New Open

Transform Statistics SUMMARIZECustom TablesCompare meansGeneral LinearCorrelateRegressionLogLinearClasssifyData ReductionScaleNonparametric testsSurvivalMultiple Response

SummarizeFrequenciesDescriptivesExploreCrosstabs

PRR475 Stynes Exercises Page COMPARE MEANS Interval dependent variable, nominal or limited category independent variable

Means Compare subgroup means, Options ANOVA for stat test

One Sample T-Test Test H0 : Mean of variable = some constant

Indep. Samples T-Test Two groups, Test H0 : Mean for

group 1 = Mean for group 2

Paired samples T-Test Paired variables - applies in pre-test, post-test situation

One Way ANOVA Compare means for more than two groups

CUSTOM TABLES: The Custom Tables procedures let you run descriptive statistics on groups of variables and assembles the results in tables, giving you some control over formatting and labeling. It produces what are sometimes called "banner tables" summarizing a number of variables in a single table. Use "Basic Tables" for descriptive statistics, "General Tables" for crosstabulations, and "Tables of Frequencies" for frequency distributions. Check out this procedure after you have mastered those in SUMMARIZE section.

4. General Steps for Running Procedures. a. First choose a procedure from Statistics menu. Note the appropriate procedure depends on measurement

levels of your variables and nature of the intended analysis. See 5 below for details.b. Choose variables : Select from list at left, click arrow to move into Variable Box at right. Note that you can

choose several variables at a time - move one at a time by selecting and clicking arrow or by double clicking on variable name. Hold CTRL key down while clicking to select several variables and move to Variable Box as a group. To Unselect a variable, click on it in the Variable Box on right, arrow switches direction, click it to move back.

c. Select Buttons at bottom for special Options, Statistics, etc. - complete dialog boxes, CONTINUEd. Click OK to run the proceduree. Results appear in the OUTPUT Window. Scroll around in this window to view results. SPSS automatically

switches to output window when you run a procedure. To return to Data window click HCMA475 button on application bar at bottom or choose HCAM475 from Window menu item.

5. Guidance on individual procedures - basic statisticsa. FREQUENCIES - run this on variables at nominal or ordinal scale with a small number of categories.b. DECRIPTIVES - run for interval scale, choose S.E. Mean in Statistics Dialog BOX to compute

confidence intervals.c. CROSSTABS - choose a row and column variable (use variable with fewer categories for columns). In

Statistics select Chi square, in Cells choose Row Pct and Column Pct.d. COMPARE MEANS - dependent variable should be interval scale, independent variable forms subgroups

(should be nominal or small number of categories).

6. Other Procedures you May Need- RECODING AND COMPUTING NEW VARIABLES are in TRANSFORM menu. Use RECODE to change coding of a variable (maybe to collapse into fewer groups or reassign missing codes) and COMPUTE to compute new variables (e.g. simple sum of other variables).

a. RECODE : In RECODE you change coding on a variable. First choose whether you want to put new codes in same variable or a different (new) one . The latter preserves old codes and sets up new variable with new codes. To preserve the original coding on the file, choose recode "into new variable". Then you must add

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Transform StatisticsSummarizeCustom TablesCOMPARE MEANSANOVA ModelsCorrelateRegressionLogLinearClasssifyData ReductionScaleNonparametric testsSurvivalMultiple Response

Compare MeansMeansOne Sample t-TestInd. Samples T-Paired Samples TOne Way ANOVA

PRR475 Stynes Exercises Page name for new variable. In either case, specify coding changes as follows. Select variable you want to change codes for and complete the Dialog Box to indicate how codes should be changed. Press ADD button to add each coding change to the recode box. Repeat procedure for as many codes as you wish to change. Then press OK to execute the changes.

For example to change code 4 on the FIRST variable to group "within the past 5 years" (3) and "more than 5 years ago" (4) together, select recode into same variable, choose FIRST variable, choose OLD AND NEW VALUES button enter a 4 in box for old value and a 3 for new value at right. Then click the ADD button and a line 4 3 will appear in box. Click CONTINUE, then Click OK to perform the recoding. If you look in DATA Window under FIRST column all the 4’s should now be 3’s. When you run a FREQ on FIRST, 3’s and 4’s will be grouped and show up as 3’s. Careful as any value labeling won't be automatically corrected.

b. COMPUTE: To compute new variables from old. Enter a name for new variable in the Target Variable Box. Then enter a mathematical expression in the larger box after the = sign indicating how new variable is computed. Press OK to execute the procedure. Your new variable is added as a column at the end of file in DATA window. You may now use this variable in any procedure (refer to it by the name you assigned).

e.g. to compute a variable equal to length of time each party stayed in the park. Enter HOURS as a name in Target Variable Box. In numeric expression box enter LEAVE - ARRIVE. Press OK. Be careful to spell variable names correctly. You can paste variables into box by double clicking on them in the list of variables at left and then adding (or pasting from calculator pad) math expressions in between. You can edit inside box to correct mistakes. SPSS will add the new variable to the file - check it at far right in data window. You can now use the new HOURS variable like any other in a statistical procedure. It won't be kept when you exit SPSS unless you save file (probably no need to save file, but if you do, you'll have to put it in your own AFS space).

7. OTHER TIPSa. Variables may be easier to find for pasting into procedures if they appear in the same order as on the

questionnaire and codebook rather than alphabetical. SPSS may be set up to show variables in either alphabetic or file order. To get “File” order of variables, choose EDIT, OPTIONS in main SPSS menu and change Variable order from Alpha to File order (push radio button). You must do this BEFORE retrieving the file. Re-retrieve the file if you already loaded it for this change to take effect.

b. SPSS throws up lots of WINDOWS, often not maximized. Use MAXIMIZE buttons at top right of windows to expand display to full screen. Use WINDOW command on menu bar to choose between the Output or Data Windows or choose them from Application bar at bottom.

c. You may print results as you generate them from the output WINDOW or you may save all of your output before you exit, read this into a wordprocessor (as an ASCII file) and print all or parts you want. To save output, when you exit SPSS (By File Exit command), answer YES to the question about saving your output. Enter a path and filename, e.g. A:SPSS.LST to put it on your floppy or enter path to your AFS space. You don’t need to save the data (respond NO to this question when exiting). The SPSS.LST file can be read as a text or ASCII file by most wordprocessors including Notepad or WordPad. You can edit and print at home. You can also copy and paste SPSS output to WORD or EXCEL by opening both SPSS and these applications. The Output window is a simple text editor - you can add your own notations and delete items you don't want. Outline at left is handy for finding a procedure you ran or deleting it.

d. Feel free to explore other parts of the SPSS program. You can generate GRAPHS in the graphs menu (be careful to save before trying graphs - labs are crash prone on graphing), and view various information about the file in UTILITIES menu. See the HELP menus for further information about using SPSS for WINDOWS. If you’d like more instruction in SPSS, the Computer Lab runs shortcourses. Also check out the SPSS site on the web at www.spss.com.

e. SPSS also lets you paste commands into a syntax window (look for the PASTE buttons on most procedures). If you prefer you can type, edit and run procedures from the syntax window if you know the syntax. This is sometimes faster than navigating thru the menus, but requires some familiarity with SPSS syntax. If you paste commands to Syntax windown, you can save the syntax file and easily rerun procedures later. This simplifies rerunning a complicated set of procedures

f. While you can run many of these simple procedures in Excel using functions or the data analysis ADD-IN, it is much simpler to perform statistical analysis with SPSS. Each type of analysis package (spreadsheet, database, statistics) is best suited for particular kinds of analyses.

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PRR475 Stynes Exercises Page SPSS PRACTICE EXERCISE

1. FREQUENCIES for nominal, ordinal variable. Describe the characteristics of park visitors - income and age. From codebook note these variables are measured in categories- i.e. ordinal scale with small number of categories. Run FREQUENCIES. In menu choose Statistics, Summarize, Frequencies. Find INCOME and AGE2 variables on list at left. Select them and click arrow to move to the variable box at right. Click OK to run frequencies.

2. DESCRIPTIVES for interval scale variable. How many female visitors on average in each party visited the Metroparks during the survey period? Find variable on codebook = TOTFEMAL, note it is interval scale. In menu Choose Statistics, Summarize, Descriptives. Complete Dialog box as above by selecting TOTFEMAL and moving it to the input box.

3. DESCRIPTIVES with STANDARD ERROR. Find a 95% confidence interval for the average in question 2. Note a 95% confidence interval = sample mean plus or minus two standard errors. To get standard error repeat step 2, but also click the OPTIONS button at bottom of dialog box. Ask for SE mean (Standard error of mean ) by clicking appropriate box. Click CONTINUE, then OK to run. To get 95% confidence interval you add and subtract two standard errors from the sample mean.

4. CROSSTABS with two nominal or ordinal variables. Crosstabs generates a table using two variables, one for rows and one for columns. Question- What is distribution of the sample by age and income? From menu, choose Statistics, Summarize, Crosstabs. Complete Dialog Box by choosing INCOME for the row variable and AGE2 for the column variable. Also click the

STATISTICS button at bottom and ask for all of them -- CONTINUECELLS button at bottom and ask for observed count, row percents, and column percents -- CONTINUE

Now click OK to run procedure.

5. COMPUTING NEW VARIABLES. What is the average number of visitors in each party? You will need to COMPUTE a new variable equal to the sum of total female and male visitors and then run DESCRIPTIVES on this new variable. (See example COMPUTE on previous sheet).

6. RECODING a variable. Suppose we want to collapse income into two categories, say above or below 50,000. Choose Transformations on menu, then RECODE. Complete dialog box to recode income into a NEW variable (See Recode command on previous page) - call it INCOM2. Then run FREQUENCY on INCOM2.

7. Hypothesis testinga. Differences in means. Are older visitors more likely to purchase annual permits? Two variables are

MVP which indicates whether people purchased daily or annual permits and AGE which gives ages of the respondents (interval scale). We want to compute means for AGE for each types of motor vehicle permits they purchased. In menu, choose Statistics, Compare Means, Means. Complete Dialog Box by choosing dependent variable - the interval scale one - = AGE, then independent or subgroup variable - nominal or ordinal scale with small number of categories = MVP- Click OK to run the procedure. For statistical test of hypotheses that all the subgroup means are equal ask for ANOVA in Options Box.

Alternatively you could perform the independent samples T-test on two groups defined by MVP (say group one has MVP =0, group 2 =1), or request the ANOVA to test all subgroups using CONTRASTS to get pairwise T-Tests of subgroups (it is useful when you have three and more subgroups).

b. Chi square - tests for relationships in a crosstab table between two nominal/ordinal variables. Is the decision of purchasing annual permits related to marital status? Note MVP and MARITAL are measured in categories (ordinal). Run the Crosstab procedure with MVP and MARITAL and ask for the Chi square test in statistics button.

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EXERCISE B3 DUE -- November 258. Formulate a page of your own research questions/ mini-analysis for HCMA survey, run appropriate

procedures and report the results all on at most one or two pages. Explain what you are doing and interpret the results. Refer to questionnaire and codebook to identify measurement scales of variables and how to interpret each variable.

a. First describe two or more variables - run suitable descriptive statistics.b. Then test a hypothesis about a relationship between them.

PRR475 Stynes Exercises Page SAMPLE ANALYSIS: All of this will easily fit on one page.

I hypothesized that the respondents who list price of admission as an important factor in choosing a park will have larger family sizes. The appropriate survey variables are Q7PRICE measured as a 5 item Likert scale (ordinal) and TOTFAM. TOTFAM is computed as a sum of the number of children 18 and under (HOUSEKID) and adults (HOUSEADT) that live in household.

First I computed TOTFAM=HOUSEKID + HOUSEADT. Then ran descriptives on these three variables to get means. Put these into a Table and show results. I've omitted the numbers. You should briefly describe and interpret results in a paragraph and display details in short table or figure (format tables & figures properly).

Table 1. Average family size for visitors

Category Number of People Children AdultsTotal

Report a 95% confidence interval for TOTFAM by getting DESCRIPTIVES and asking for SE Mean.

Run frequencies on Q7PRICE variable - report as percentages in a simple table.

Table 2. Rating of importance of admission price

Importance Pct extremely importantvery importantimportantsomewhat importantnot important

Based on Table 2, I split the sample into two groups: Q7PRICE = 1, 2 or 3 (extremely, very, and important) formed group one, and 4 or 5 group two. An independent samples T-Test was run to test for a difference in the average family sizes (TOTFAM) across the two subgroups (when asked to define groups, I used 4 as the Cut Point. All codes less than the cut point form one group, and all codes greater than or equal to the cut point form the other group). Show results of this in short table. Note those rating price as more important (Group 1) have somewhat larger family sizes (2.6 people compare to 2.3). The difference is statistically significant at the 95% confidence level.

Table 3. Test of Difference in Family Sizes by Importance of Admission Price

Importance Subgroup Average Family Size T-statistic SIG Group 1: Extremely, very, or important 2.6Group 2 : Somewhat or not important 2.3Test of difference in Means 3.77 .000

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PRR475 Stynes Exercises Page

1996 Huron Clinton Metroparks User Survey

BACKGROUND. The Huron Clinton Metropolitan Parks Authority (HCMA) manages a system of 13 parks in southeast Michigan. As part of HCMA’s continuing effort to meet the needs of people of Southeast Michigan, a user survey was conducted during 1995-96. The results of the survey will be used to update HCMA’s 5 year plan.

OBJECTIVES

1. Describe characteristics and patterns of use of HCMA park users

2. Identify trends in user characteristics and patterns via comparisons with previous surveys.

3. Identify and profile managerially relevant market segments

4. Evaluate visitor satisfaction with HCMA parks and measure visitor preferencesfor new facilities and programs.

METHODS: A self-administered survey of HCMA visitors was conducted between Dec 1, 1995 and November 30, 1996. Four page questionnaires were distributed to a sample of visitors in vehicles entering one of the 13 HCMA units during this period.

The sample was stratified by park, season, weekend-weekday and time of arrival at the park. Sampling was disproportionate across these strata to assure an adequate size sample for each park and season. Weights adjust the sample to the actual distribution of visits in 1995-96. Each park distributed questionnaires on 10-12 dates during each season. Dates were uniformly distributed throughout each season and divided evenly between weekends and weekdays. Gate attendants distributed surveys to each vehicle entering the park during the first 5 minutes of each hour on sampling dates. During busy periods surveys were given to every other vehicle and during slow periods sampling was conducted for the first 10 minutes of each hour. Visitors could return surveys at drop boxes located at each park exit or by return mail.

The four page questionnaire was developed from the 1990 HCMA survey instrument. Questions cover party characteristics, use of daily vs annual permits, activities in the park, importance & satisfaction with park attributes (for an I-P analysis), knowledge and use of HCMA parks, preferences for new programs and facilities, and a set of household characteristics.

You will be analyzing data covering winter, spring, summer, and fall seasons. A total of 3,086 surveys were completed over this period (overall response rate of 42%). Surveys by park range from 815 at Kensington to just over 80 at some of the more lightly used parks. A random sample of 1,000 cases will be used for our analysis.

ANALYSIS

1. Use descriptive statistics to profile parks users - see results reported on questionnaire2. Class may ignore the trend objective3. Develop segments by classifying visitors into useful subgroups and describing important differences between the

subgroups. Example - see table for annual permit holders vs those using daily permit.4. Can practice an I-P analysis using questions 7 and 15 on questionnaire.

We will be using SPSS-PC to analyze this survey. The data file HCMA475.SAV is a specially coded SPSS data file that can be retrieved from SPSS. It is available in course AFS space in Stynes subdirectory.

Preparation: Review statistics handouts, the stat reading on page 366 of Coursepack, the HCMA questionnaire and codebook.

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PRR475 Stynes Exercises Page CODEBOOK: HURON-CLINTON METROPARKS USER SURVEY

Part 1. Variables that APPEAR on the questionnaire. Refer to the questionnaire for more details.QUESTION VARIABLE NAMES CODING COMMENTQ1-Time ARRIVE, LEAVE coded in military timeQ2- N/A dropped from fileQ3-Permit MVP 0=daily, 1=annual permitQ4-Permit 95 MVP95 0=No, 1=YesQ5-Activity NATURE to PLAYGRND 0=not participate,

1=participatesee footnote for variable names & code numbers

Q6-primary activity

PRIMACT activity number from Q5 see footnote below for activity codes

Q7-Importance (characteristics))

Q7BEAUTY to Q7CROWD (see questionnaire for details)

1=extremely important to 5=not important

Q8-Importance (reasons)

Q8FAMILY to Q8NATURE (see questionnaire for details)

1=extremely important to 5=not important

Q9-Facilities Q9WATER to Q9NONEWF (see questionnaire for details)

0=not chosen, 1=like developed

Q10-Programs Q10NATURE to Q10OTHER (see questionnaire for details)

0=not chosen, 1=like developed

Q11 Aware free admission

FREEDAY1, FREEDAY2 0=no, 1=yes if yes for FREEDAY1 continue for FREEDAY2

Q12-Familiarity (column a)

META to LAKA (see questionnaire for details)

0=blank, 1= familiar

Q12-Familiarity (column b)

METB to LAKB (see questionnaire for details)

number of times visited in the past 12 months

interval

Q13- First visit FIRST 1= today, 2= within the past year, 3= within the past 5 years, 4= more than 5 years ago

Q14- Get info INFOTV to INFOOTHR 0=blank, 1= get info from itQ15- Performance Q5BEAUTY to Q5OVERAL

(see questionnaire for details)1 = excellent , 2 = very good, 3 = good, 4 = fair, 5 = poor, -8 = don’t know

Q16- Comments N/A N/AQ17Zipcode ZIPCODE 5 digit zipcodeQ18- Age AGE code age intervalQ19- Gender GENDER 1= female, 2= MaleQ20- Employment EMPLOY (see questionnaire for detail) intervalQ21- Employed EMPLFULL, EMPLPART number of people employed intervalQ22- Marital status

MARITAL (see questionnaire for detail)

Q23- Family members

HOUSEKID, HOUSEADT numbers of children (adults) at home

interval

Q24- Education EDUCATE (see questionnaire for detail)Q25- Income INCOME (see questionnaire for detail)Q26- Race ETHNIC (see questionnaire for detail)Q27- Final N/A N/A

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PRR475 Stynes Exercises Page Part 2. Created Variables. These variables were created by using recodes and computes.VARIABLE NAMES CODING COMMENTPARK 1 = Metro Beach, 2 = Wolcott Mill,

3 = Stony Creek, 4 = Indian Springs, 5 = Kensington, 6 = Huron Meadows, 7 = Hudson Mills, 8 = Dexter-Huron, 9 = Delhi, 10 = Lower Huron, 11 = Willow, 12 = Oakwoods, 13 = Lake Erie

Park where survey was distributed- determined from survey ID number

COUNTY 1 = OAKLAND, 2 = LIVINGSTON, 3 = WAYNE, 4 = WASHTENAW, 5 = MACOMB, 6 = MONROE, 7 = other

county of residence determined from zipcode

AGES 1= <5, 2= 5-12, 3= 13-17, 4= 18-35, 5= 36-59, 6= >59

Grouping of age into categories

TOTFEMAL total number of female in party Sum from question #2TOTMALE total number of male in party Sum from question #2DAY 1= Monday, 2= Tuesday etc. day of the week of visitWEEKEND 0=weekday, 1=weekend from date distributedHCMATOT Sum of total days visited parks for 1995 Sum from question #12VSTWT2 weight to use to adjust sample to population

of visits. VSTORWT2 weight to adjust sample to population of

visitorsFLC 0 = S/NC/18-35, 1 = S/C/18-35,

2 = M/NC/18-35, 3 = M/C/18-35, 4 = S/NC/36-55, 5 = S/C/>36, 6 = M/NC/36-55, 7 = M/C/36-55, 8 = S/NC/>55, 9 = M/NC/>55, 10= M/C/>55

family life cycles computed from age, marital status & children in household

SEASON 1= winter, 2= spring, 3= summer, 4= fall from date distributed

Code number, variable names, and activities for question 5 and 6.

1 NATURE NATURE OBSERVATION OR PHOTOGRAPHY

2 SCENIC SCENIC DRIVE3 PICNIC PICNIC4 BIKE BICYCLE5 WALK WALK OR HIKE6 WALKPET WALK PET(S)7 RUN RUN OR JOG8 ROLLER ROLLERSKATE OR IN-LINE SKATE OR

SKI9 VISITNC VISIT NATURE CENTER10 VISITF VISIT FARM11 VISITGM VISIT GRIST MILL12 SUNBATHE SUNBATHE13 BOATNM BOAT - NON-MOTOR14 BOATM BOAT - MOTOR

15 FISHB FISH FROM BOAT16 FISHS FISH FROM SHORE17 WATERSL WATERSLIDE18 SWIMLAKE SWIM OR WADE IN LAKE19 SWIMPOOL SWIM OR WADE IN POOL

( INCLUDING WAVEPOOL)20 EVENT ATTEND A SPECIAL EVENT IN THE

PARK21 OTHERACT PARTICIPATE IN AN OTHER ACTIVITY22 GOLF GOLF23 PLAYGAME PLAY OTHER GAMES OR SPORTS

(NOT GOLF)24 WATCH WATCH GAMES OR SPORTS25 PLAYGRND USE PLAYGROUND EQUIPMENT OR

TOT LOT

(the following codes are for question 6 only)26 ICE FISH27 CROSS COUNTRY SKI28 SLED OR TOBOGGAN29 ICE SKATE30 FISHING (UNDETERMINED)31 NONE

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PRR475 Stynes Exercises Page

PRR 475 Exercise B4 - Lansing Riverfront Trail Use Estimates

PURPOSE: This exercise will provide further practice in using formulas and Pivot tables in Excel to compute some simple statistics. It also follows up on previous exercise to illustrate the procedure for expanding from a sample of observations to the population using either a simple random sample or stratified sample. The data were gathered by previous PRR 475 students using observations along the Lansing Riverfront Trail. We will use the 1996 observations.

Using the 30 observations at Aurelius and Kalamazoo access points on Lansing Riverfront Trail (in Trail96.xls file on Count96 page), answer the following questions:

1. How much use did the eastern section of the trail receive in October of 1996? 2. Where and when was use heaviest? How was use distributed across the two access sites and by time of day and

day of week?3. Who uses the trail? Report the percentage of use by type of use, user characteristics, etc.

MATERIALS:a. HINTS page suggests how to do this exercise. Try it without consulting the hints first.b. Original handout on trail summarizes purposes and study approach. Note especially the sampling strata -

only 30 of the planned observations were completed.c. Observation form(s) including observation instructions, coding of variables and how use was

operationally defined and measured. The data file has the hourly sums that were entered on the final "Summary Counts" form plus the variables in box at top of the INSTRUCTION sheet.

d. Hourly observation counts. Available in an Excel file available in the course AFS space -- msu/course/prr/475/Stynes/trail96.xls. Use the 1996 data by selecting the 1996 tab at bottom. After completing 1996, you are welcome to try other years or combining data across years. We did this in 1995 and 1993 also.

Format of your report: (Shouldn’t take more than 2 pages).

1. For each of the three questions /objectives, briefly explain how you generated the answers - call this section METHODS.

2. For each question/objective summarize the answer in a brief text, table, figure or combination of these - put these in a RESULTS section.

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PRR475 Stynes Exercises Page HINTS ON THE ANALYSIS

1. ESTIMATING OCTOBER USE. There are many ways to do this, some right some wrong, and many better or worse. Note that the sampling design suggests the recommended approach using the 6 strata.

a. Use the sample to obtain an average use per hour (note the PEOPLE column has counts of observed trail users per hour. You can get an overall average (all observations) by using the AVERAGE function to average the PEOPLE column in Excel OR get averages by strata using the Pivot table command (use STRATA as row variable, LOCation as column and Average of PEOPLE in cells).

b. Expand from the sample to population. Figure out the total number of daylight hours in October (12 hour days * 31 days) or the number that fall into each of the six strata. Multiply the number of hours by the average use per hour computed in step a. Sum across strata if you compute by strata to get total October use.

2. ESTIMATING USE DISTRIBUTION AND TYPES OF USERS. You could repeat the procedures in (1) using numbers from the other columns on the data sheet in place of the PEOPLE column. For example, using the WHITE column would estimate number of "white" users. This would be very tedious and time consuming. A quicker method is to assume the visitor characteristics and types of use do not vary much by strata and simply total the observations directly (essential assuming the 30 observations are a representative sample). On spreadsheet, sum the columns to get total counts of all variables in the sample. Then simply divide by appropriate totals to estimate percent white vs non-white, male vs female, by age, etc. Be careful to make percents sum to 100% (due to some missing data, not all fields will sum to same total as PEOPLE column). For example, in getting percents by age divide each age group total by the sum of totals across all age groups. Note that you can put formulas at bottom of one column and simply copy them across to quickly compute all column sums or averages.

EXERCISE DUE: December 2

Follow-up on statistical matters:

3. Comment on the accuracy of your results (confidence intervals? or discussion of errors).

HINTS ON ERROR AND CONFIDENCE IN RESULTS. You should try to convey to the client some idea of accuracy/error in the estimate of use. Some ideas here are: a) commenting on representativeness of sample (did we get a typical mix of weather patterns and reasonable time distribution within the strata), b) noting the amount of variation in use counts (highs and lows) and hence sensitivity to which periods we sampled, or c) a formal estimate of confidence using standard errors. (C) can be done by strata (need at least two observations in a stratum to estimate variance from sample). Some strata will have higher variances than others, so estimates of means for those strata will have more error.

If you want to take a crack at a confidence interval, I'd suggest trying it with sample as a whole first, ignoring strata. To do this, in Excel compute the standard deviation of entries in the PEOPLE column (STDDEVP function), then calculate standard error of mean as standard deviation/sqrt(n), n=30. Convert this to a percent of the mean by dividing the standard error by the PEOPLE mean. Now apply this percent error to your (expanded) estimate of October use from step 1. Remember two standard errors either side of mean is a 95% confidence interval, so take two "Pct errors" either side of mean. For example: If mean is 100 and standard error is 10, the percent error is 10% (10/100) . Two standard errors is 20% either side of mean. If expanded use estimate is 25,000, then a 95% CI is 20% either side of 25,000 or between 20,000 and 30,000. If this makes no sense to you at all, disregard and restrict your comments about error to a or b. A better, but messier approach is to compute standard errors of means for each stratum and sum confidence intervals across strata. - for this use same Pivot Table as in (1) above. First copy the Pivot table below using Paste Special - Values to copy the values in table. Then re-enter Pivot Table wizard and change middle to STDEVP of PEOPLE rather than AVERAGE - copy this next to averages below; finally change middle to COUNT of PEOPLE. Now divide standard deviation by square root of the count for each cell to compute standard error, divide into average for each cell to express it as a percent of mean.

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PRR475 Stynes Exercises Page

Lansing Riverfront Trail Study

OBJECTIVES:

1. Estimate the amount of use on trail east of Potter Park during October 1996.2. Estimate use by user characteristics and type of use (activity).3. Describe temporal and spatial patterns of use.

APPROACH: Systematic observation.

POPULATION: All persons using the eastern sections of the trail during October, 1996 are included in the population of users. Users are defined as anyone physically present on the trail surface between the eastern trail terminus near Kalamazoo St. and the bridge on the trail over the Red Cedar River just west of Aurelius Rd.

The unit of analysis for sampling purposes is time blocks rather than people. We will sample from the population of one hour time periods in October at two access points. As trail access is limited on the eastern sections, we will use point measures of use to characterize each segment. Three segments will be studied :

· Segment 1 : Eastern terminus at Kalamazoo St. to mid-way point along the trail toward the Aurelius access - use measured by visitor counts at eastern terminus.

· Segment 2: Aurelius access point & eastward to same mid-point as above - measured by use at Aurelius access traveling to or from the east.

· Segment 3: Aurelius access west to Potter Park - measured by use at Aurelius access traveling to or from the west.

The time period covered in the study is the month of October. Use will be recorded only for daylight hours, approximately a 12 hour period from 7:30 am to 7:30 pm each day in October. There are 372 one hour blocks in October during daylight (31*12).

SAMPLE: A clustered, stratified random sample of time periods/access points will be taken. The population is stratified by time and location. Use will be observed on a sample of 36 one hour blocks, 18 at Kalamazoo and 18 at Aurelius. Hours are divided into strata by weekend/weekday and morning, mid-day and late afternoon. Combining the hourly and day of week strata yields 6 time strata - we will try to observe 6 hours in each strata for a total of 36 observations. Observations in each stratum will be split equally between Aurelius and Kzoo.

SAMPLING STRATA Morning

7:30-11:30 amAfternoon11:30-3:30

Evening3:30-7:30 pm

Total

Weekday (M-F 6 6 6 18Weekend (Sat, Sun) 6 6 6 18Total 12 12 12 36

15

PRR475 Stynes Exercises Page COMPLETE THE FOLLOWING FORM BEFORE YOU BEGIN OBSERVING: ADD LAST LINE AT COMPLETION OF INTERVIEW PERIOD.

LANSING TRAIL OBSERVATION FORM: Location : KZOO AURELIUS

Observer __________________________ Date : _______________, October ______, 1996 (day of week) (date)

Weather conditions:

Temperature _______; Sunny Partly Cloudy Raining

Trail Conditions: Dry Wet

Begin Observation At _____________ am pm ; Number of Cars in lot at start _______________.

End Observation At _______________ am pm Number of Cars in Lot at end _______________.

COMMENTS:

OBSERVER INSTRUCTIONS:

1. Sign up for one of the sampling periods/locations.2. Obtain observation forms and instructions and read these prior to observation period.3. Report to the designated location with the observation form, a watch, pencil and clipboard. Find a

comfortable location for observing where you have an unobstructed view of the trail. 4. Complete the first part of observation form entering your name, date and time you began interviewing,

location, and weather conditions.5. Record each trail user passing your station on a separate line. Circle appropriate items within each category

. Enter a sequential user number in left column, i.e. 1,2,3,4,5,... See coding instructions below for details.

Time Periods: For this exercise we will give you some flexibility in choosing the time of interview to accomodate your schedules. You may choose a one hour period within the assigned time starting on the hour or half hour. For example, if you are assigned the 7:30-11:30 am block, you may choose any of the following one hour periods: 7:30-8:30, 8:00-9:00, 8:30-9:30, 9:00-10:00, 9:30-10:30, or 10:00-11:00. Normally the precise hour would be assigned to assure representation within the 4 hour blocks.

Observation Points At Kalamazoo observe at trail entrance at far end of parking lot behind Steak House. You may be able to

observe from your car or station yourself in vicinity of the entrance. At Aurelius observe from trail immediately in front of parking area so you can observe entering,

exiting and thu traffic. You may observe from the "grassy knoll", the fishing pier/boat ramp, or perhaps from car.

16

PRR475 Stynes Exercises Page

CODING INSTRUCTONS: List each individual (including babies, but not pets) passing your position (or in the immediate vicinity) using the trail on a separate row. Enter a sequential user id number in left column. Complete the information for each individual in the remaining columns by circling the appropriate response in each column. In the left margin of form, mark a 2,3, and 4 at the beginning of the second, third, & fourth 15 minute period. We will want to generate counts for the hour and each 15 minute block. If you count more than 20, start on new page and number pages at top.

REPEAT TRAFFIC: When you can recognize the same individual or party returning to your interview point (once, twice, or several times), find that person on the list and make a second entry in the TRAVEL DIR column, e.g. if person was going E first time, circle W, If In, perhaps out or thru. If unsure about repeats, treat as a new visitor. You needn't record more than one repeat for each individual, although you should finish with exiting information if you observe the person leaving the trail after multiple passes of your observation point.

CODING BY VARIABLE:

ACTIVITY: Circle W(walk), J(jog), B(bike), RB(rollerblade), RS(rollerskate), RD(ride in backpack, stroller, wagon, bikeseat, etc.) . Write in any other activity - enter H for wheelchair users.

PETS: enter number of pets (leave blank if none). Assign pets to individual with leash or nearest to pet. Count strays (not with anyone) and record at bottom of pet column outside the margins.

TRAVEL DIRECTION: Enter direction of travel and if the individual is entering trail, leaving trail or traveling thru to east or west (at Aurelius). Enter S (Stationary) for people hanging around access points, not traveling linearly along trail (e.g. anglers at Aurelius). Don't include people sitting in cars who do not get out.

At Kalamazoo - W = coming onto trail and traveling west along trailE= leaving trail traveling in an easterly directionS = stationary

At Aurelius - W= going toward Potter Park or exiting here and coming from the eastE = going toward Kalamazoo St or exiting from west (Potter).

Also circle one of following:

IN= entering trail hereOUT = leaving trail hereTHRU = passing thru along trail (ie, not entering or leaving). At Kzoo this would be

turning around and returning.

ACCESS: Indicate for those entering or leaving trail whether access to trail is on foot, by car, or bicycle. (e.g. if they load/unload bicycles into car check car).

AGE: Estimate age within broad groups as indicated. Circle the number for the age category.RACE: Check White(W) or non-white (NW). Treat Non-white as an "other" category including blacks, native

americans, asians, and hispanics. Circle ? if can't tell or unsure.GENDER: Note male or female. Circle ? if unsure or you can't tell.CAR COUNTS: At Kzoo count any cars toward back of lot (paved or dirt section). Don't count cars parked close to

steak house.

17

PRR475 Stynes Exercises Page OBSERVATION FORM Page __________

RECORD EACH INDIVIDUAL OBSERVED ON A SEPARATE LINE ACTIVITY P

ETS

DIRECTION- ACCESS AGE RACE SEX

1 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

2 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

3 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

4 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

5 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

6 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

7 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

8 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

9 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

10 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

11 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

12 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

13 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

14 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

15 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

16 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

17 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

18 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

19 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

20 W J B RB RS RD

E W SIn Out Thru

Ft Car Bike 2 6 13 18 30 60+ W NW ?

M F ?

18

PRR475 Stynes Exercises Page SUMMARY COUNTS (count number of trail users in each category during your hour of observation). These counts were transferred to Excel spreadsheet - one row for each hour of observation. The first 14 variables on Excel spreadsheet come from summary sheet above. Labeled "Lansing Trail Observation Form". The rest are counts made from the coding sheet.

DO NOT INCLUDE REPEATS in these Tablulations

Category Number ObservedTotal visitors _________By Activity:

Walk _________Jog _________Bike _________RB _________RS _________RD _________Other _________

By Direction (Include repeats here)East _________West _________South _________

By In/Out/Thru (AURELIUS ONLY) - Include repeats hereIn _________Out _________Thru _________

Pets Number (Not entered in Excel)

By AccessWalk _________Car _________Bike _________

By Age<2 _________6 _________13 _________18 _________30 _________60 _________

By RaceW _________NW _________

By GenderM _________F _________

19