Conjoint Analysis

1. In conjoint analysis, researchers describe products or services by sets of attribute values or levels and then typically measure respondents' purchase interest. The primary purpose of conjoint analysis is to model human behaviour, usually purchase behaviour. By measuring purchase interest in a “complete” product or service, conjoint analysis captures the essential dilemma of market choice: The perfect product is seldom available, but lesser alternatives are. By forcing respondents to trade off competing values and needs, conjoint analysis uncovers purchase motivations respondents may be unwilling to admit to and may not even realise they have. Conjoint Analysis

2. A potential concern for any approach that accommodates a large number of attributes is attribute additivity. Seldom mentioned in the literature, attribute additivity is the phenomenon where a large number of less important attributes may overwhelm one or two extremely important ones. For example, a feature-rich product may have more total utility than a low-priced one simply because all the small utility weights of the various product features, when summed, exceed the utility weight of the price attribute. McCullough D., A User's Guide To Conjoint Analysis, Marketing Research, 2002, 14(2), 18-22. Conjoint Analysis - Caution

3. Within medicine, understanding how patients and other stakeholders value various aspects of an intervention in health care is vital to both the design and evaluation of programs. Incorporating these values in decision making may ultimately result in clinical, licensing, reimbursement, and policy decisions that better reflect the preferences of stakeholders, especially patients. Aligning health care policy with patient preferences could improve the effectiveness of health care interventions by improving adoption of, satisfaction with, and adherence to clinical treatments or public health programs. Conjoint Analysis

4. Methods using ranking, rating, or choice designs (either individually or in combination) to quantify preferences for various attributes of an intervention (often referred to as conjoint analysis, discrete-choice experiments, or stated-choice methods). Conjoint-analysis methods are particularly useful for quantifying preferences for nonmarket goods and services or where market choices are severely constrained by regulatory and institutional factors, such as in health care. Conjoint Analysis

5. The checklist should be used to understand the steps involved in producing good conjoint-analysis research in health care. Bridges, J.F.P., Hauber, A.B., Marshall, D., Lloyd, A., Prosser, L.A., Regier, D.A., Johnson, F.R. and Mauskopf, J., Conjoint Analysis Applications in Health-a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force, Value In Health, Volume: 14 Issue: 4, Pages: 403-413, 2011. Conjoint Analysis

6. Conjoint Analysis See also Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force Johnson, F.R., Lancsar, E., Marshall, D., Kilambi, V., Muhlbacher, A., Regier, D.A., Bresnahan, B.W., Kanninen, B. and Bridges, J.F.P Value In Health, 16(1), 3-13, 2013. But beware of Discrete Choice Experiments Are Not Conjoint Analysis Louviere J.J., Flynn T.N. and Carson R.T. Journal of Choice Modelling, 3(3), 57-72, 2010. 6

7. 1. Was a well-defined research question stated and is conjoint analysis an appropriate method for answering it? 1.1 Were a well-defined research question and a testable hypothesis articulated? 1.2 Was the study perspective described, and was the study placed in a particular decision-making or policy context? 1.3 What is the rationale for using conjoint analysis to answer the research question? Conjoint Analysis

8. 2. Was the choice of attributes and levels supported by evidence? 2.1 Was attribute identification supported by evidence (literature reviews, focus groups, or other scientific methods)? 2.2 Was attribute selection justified and consistent with theory? 2.3 Was level selection for each attribute justified by the evidence and consistent with the study perspective and hypothesis? Conjoint Analysis

9. 3. Was the construction of tasks appropriate? 3.1 Was the number of attributes in each conjoint task justified (that is, full or partial profile)? 3.2 Was the number of profiles in each conjoint task justified? 3.3 Was (should) an opt-out or a status-quo alternative (be) included? Conjoint Analysis

10. 4. Was the choice of experimental design justified and evaluated? 4.1 Was the choice of experimental design justified? Were alternative experimental designs considered? 4.2 Were the properties of the experimental design evaluated? 4.3 Was the number of conjoint tasks included in the data-collection instrument appropriate? Conjoint Analysis

11. 5. Were preferences elicited appropriately, given the research question? 5.1 Was there sufficient motivation and explanation of conjoint tasks? 5.2 Was an appropriate elicitation format (that is, rating, ranking, or choice) used? Did (should) the elicitation format allow for indifference? 5.3 In addition to preference elicitation, did the conjoint tasks include other qualifying questions (for example, strength of preference, confidence in response, and other methods)? Conjoint Analysis

12. 6. Was the data collection instrument designed appropriately? 6.1 Was appropriate respondent information collected (such as socio-demographic, attitudinal, health history or status, and treatment experience)? 6.2 Were the attributes and levels defined, and was any contextual information provided? 6.3 Was the level of burden of the data-collection instrument appropriate? Were respondents encouraged and motivated? Conjoint Analysis

13. 7. Was the data-collection plan appropriate? 7.1 Was the sampling strategy justified (for example, sample size, stratification, and recruitment)? 7.2 Was the mode of administration justified and appropriate (for example, face-to-face, pen-and-paper, web-based)? 7.3 Were ethical considerations addressed (for example, recruitment, information and/or consent, compensation)? Conjoint Analysis

14. 8. Were statistical analyses and model estimations appropriate? 8.1 Were respondent characteristics examined and tested? 8.2 Was the quality of the responses examined (for example, rationality, validity, reliability)? 8.3 Was model estimation conducted appropriately? Were issues of clustering and subgroups handled appropriately? Conjoint Analysis

15. 9. Were the results and conclusions valid? 9.1 Did study results reflect testable hypotheses and account for statistical uncertainty? 9.2 Were study conclusions supported by the evidence and compared with existing findings in the literature? 9.3 Were study limitations and generalizability adequately discussed? Conjoint Analysis

16. 10. Was the study presentation clear, concise, and complete? 10.1 Was study importance and research context adequately motivated? 10.2 Were the study data-collection instrument and methods described? 10.3 Were the study implications clearly stated and understandable to a wide audience? Conjoint Analysis

17. This presentation is loosely based on notes from IBM/SPSS main and statistics examples. In a popular example of conjoint analysis (Green and Wind, 1973), a company interested in marketing a new carpet cleaner wants to examine the influence of five factors on consumer preference - package design, brand name, price, a Good Housekeeping seal, and a money-back guarantee. Conjoint Analysis - Example

18. There are three factor levels for package design, each one differing in the location of the applicator brush; three brand names (K2R, Glory, and Bissell); three price levels; and two levels (either no or yes) for each of the last two factors. The following table displays the variables used in the carpet-cleaner study, with their variable labels and values. Conjoint Analysis - Example

19. There could be other factors and factor levels that characterize carpet cleaners, but these are the only ones of interest to management. This is an important point in conjoint analysis. You want to choose only those factors (independent variables) that you think most influence the subject's preference (the dependent variable). Using conjoint analysis, you will develop a model for customer preference based on these five factors. Green, P. E., and Y. Wind. 1973. Multiattribute decisions in marketing: A measurement approach. Hinsdale, Ill.: Dryden Press. Conjoint Analysis

20. The first step in a conjoint analysis is to create the combinations of factor levels that are presented as product profiles to the subjects. Since even a small number of factors and a few levels for each factor will lead to an unmanageable number of potential product profiles, you need to generate a representative subset known as an orthogonal array. Conjoint Analysis

21. The “Generate Orthogonal Design” procedure creates an orthogonal array - also referred to as an orthogonal design - and stores the information in a data file. Unlike most procedures, an active dataset is not required before running the Generate Orthogonal Design procedure. If you do not have an active dataset, you have the option of creating one, generating variable names, variable labels, and value labels from the options that you select in the dialog boxes. If you already have an active dataset, you can either replace it or save the orthogonal design as a separate data file. Conjoint Analysis

22. Conjoint Analysis To create an orthogonal design: ► From the menus choose: Data > Orthogonal Design > Generate

23. ► Enter package in the Factor Name text box, and enter package design in the Factor Label text box. ► Click Add Conjoint Analysis

24. This creates an item labelled package 'package design' (?). Select this item. ► Click Define Values. Conjoint Analysis

25. ► Enter the values 1, 2, and 3 to represent the package designs A*, B*, and C*. Enter the labels A*, B*, and C* as well. ► Click Continue. Conjoint Analysis

26. Conjoint Analysis You'll now want to repeat this process for the remaining factors, brand, price, seal, and money.

27. Use the values and labels from the following table, which includes the values you've already entered for package. Conjoint Analysis

28. Once you have completed the factor specifications: ► In the Data File group, leave the default of Create a new dataset Conjoint Analysis

29. Enter a dataset name. The generated design will be saved to a new dataset, in the current session, with the specified name. ► Select Reset random number seed to and enter the value 2000000. Conjoint Analysis

30. Generating an orthogonal design requires a set of random numbers. If you want to duplicate a design - in this case, the design used for the present case study - you need to set the seed value before you generate the design and reset it to the same value each subsequent time you generate the design. The design used for this case study was generated with a seed value of 2000000. This value is essential to ensure repeat analysis will reproduce identical results. Conjoint Analysis

31. ► In the Data File group, change the default of Create a new data file Enter a data file name (with appropriate directory structure for your machine). The generated design will be saved to a new data file. Conjoint Analysis

32. ► Click Options Conjoint Analysis

33. ► In the Minimum number of cases to generate text box, type 18. Conjoint Analysis

34. Conjoint Analysis By default, the minimum number of cases necessary for an orthogonal array is generated. The procedure determines the number of cases that need to be administered to allow estimation of the utilities. You can also specify a minimum number of cases to generate, as you've done here. You might want to do this because the default number of minimum cases is too small to be useful or because you have experimental design considerations that require a certain minimum number of cases.

35. ► Select Number of holdout cases and type 4. Conjoint Analysis Holdout cases are judged by the subjects but are not used by the conjoint analysis to estimate utilities. They are used as a check on the validity of the estimated utilities. The holdout cases are generated from another random plan, not the experimental orthogonal plan.

36. ► Click Continue in the Generate Orthogonal Design Options dialog box. Conjoint Analysis

37. ► Click OK in the Generate Orthogonal Design dialog box. Conjoint Analysis

38. The syntax is *Generate Orthogonal Design. SET SEED 2000000. ORTHOPLAN /FACTORS=package 'package design' (1 'A*' 2 'B*' 3 'C*') brand 'brand name' (1 'K2R' 2 'Glory' 3 'Bissell') price 'price' (1.19 '$1.19' 1.39 '$1.39' 1.59 '$1.59') seal 'Good Housekeeping seal' (1 'no' 2 'yes') money 'money-back guarantee' (1 'no' 2 'yes') /OUTFILE='15a.sav' /MINIMUM 18 /HOLDOUT 4 /MIXHOLD NO. Conjoint Analysis

39. The orthogonal design is displayed in the Data Editor and is best viewed by displaying value labels rather than the actual data values. This is accomplished by choosing Value Labels from the View menu. To retrieve your saved plan. Conjoint Analysis

40. Conjoint Analysis

41. The orthogonal design is a required input to the analysis of the data. Therefore, you will want to save your design to a data file. For convenience, the current design has been saved in 15a.sav (orthogonal designs are also referred to as plans). Once you have created an orthogonal design, you'll want to use it to create the product profiles to be rated by the subjects. You can obtain a listing of the profiles in a single table or display each profile in a separate table. Conjoint Analysis

42. To display an orthogonal design: ► From the menus choose: Data > Orthogonal Design > Display Conjoint Analysis

43. ► Select package, brand, price, seal, and money for the factors. Conjoint Analysis

44. The variables in the data file are the factors used to specify the design. Each case represents one product profile in the design. Notice that two additional variables, CARD_ and STATUS_, appear in the data file. CARD_ assigns a sequential number to each profile that is used to identify the profile. STATUS_ indicates whether a profile is part of the experimental design (the first 18 cases), a holdout case (the last 4 cases), or a simulation case (to be discussed in a later topic in this case study). The information contained in the variables STATUS_ and CARD_ is automatically included in the output, so they don't need to be selected. Conjoint Analysis

45. ► Select Listing for experimenter in the Format group. This results in displaying the entire orthogonal design in a single table. ► Click OK. Conjoint Analysis

46. The output resembles the look of the orthogonal design as shown in the Data Editor—one row for each profile, with the factors as columns. Notice, however, that the column headers are the variable labels rather than the variable names that you see in the Data Editor. Conjoint Analysis

47. Also notice that the holdout cases are identified with a footnote. This is of interest to the experimenter, but you certainly don't want the subjects to know which, if any, cases are holdouts. Conjoint Analysis

48. Depending on how you create and deliver your final product profiles, you may want to save this table as an HTML, Word/RTF, Excel, or PowerPoint file. This is easily accomplished by selecting the table in the Viewer, right clicking, and selecting Export. Also, if you're using the exported version to create the final product profiles, be sure to edit out the footnotes for the holdout cases. Conjoint Analysis

49. Perhaps the needs for your survey are better served by generating a separate table for each product profile. This choice lends itself nicely to exporting to PowerPoint, since each table (product profile) is placed on a separate PowerPoint slide. Conjoint Analysis

50. To display each profile in a separate table: ► Click the Dialog Recall button and select Display Design. ► Deselect Listing for experimenter and select Profiles for subjects. ► Click OK. Conjoint Analysis