Study of peoples perceptions of charities in Ireland

Marc P.

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Study of peoples perceptions of charities in Ireland

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Lecture
Résumé
Sommaire
Extraits

Résumé du document

Perceptual Mapping: "A perceptual map is a spatial representation in which competing alternatives are plotted in Euclidean space" (Lilien.G.Rangaswamy). Perceptual maps allow managers to summarize and visualize key elements of the market structure. They also depict the competitive structure of markets in a manner that facilitates differentiation and positioning strategies. Perceptual maps are most commonly used: to understand the market structure of product categories as perceived by customers; Select the set of competitors to compete against; Administer image studies to help position the organization; Represent customer perceptions and preferences in a manner that aids communication and discussion within the organization; Evaluate a new product concept in the context of existing brands in the market; and Develop a name for a new product. This process thus, allows mangers to take a broad view on the company's strengths and weaknesses in relation to other competitors in an industry. It also allows the marketer to view the product in the eyes of consumers, in relation to the competition within the marketplace. This enables the marketer to view both the customer and the competition at the same time and on the same map.

Sommaire

Literature review
1. Perceptual mapping
2. Multi-dimensional scaling
3. Correspondence analysis theory
Industry Analysis
1. Introduction
2. The history of the charity organisation
3. Concern
4. Trocaire
5. Focus Ireland
6. Irish Red Cross
7. World vision of Ireland
8. Sight savers
9. Barnardos
10. The history of the charity shop
11. Oxfam
12. St Vincent de Paul
13. The Irish charity Sector
Methodology
1. Problem definition
2. Research objectives and research questions
3. Research design
4. Fieldwork and data collection: qualitative
5. Focus group analysis
6. Questionnaire creation and justification
7. Fieldwork and data collection
8. Sampling
Conclusion
1. Research objectives
2. Crosstabs
3. Perceptual map
4. Multidimensional scaling
5. Correspondence analysis
6. Final comment

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Extraits

[...] they are not required to give details on the attributes which their perceptions are based on. Perceptual maps can be formed for each of the respondents in the study, or the results can be aggregated and used to develop a single map. Disadvantages As no attributes have been specified, it is up to the researcher to “identify the basic dimensions of evaluation of the objects” (Hair, Anderson, Tatham and Black, p531, 1998). Back to Top Compositional or Attribute-Based Approach: Advantages Unlike the decompositional approach, there is little difficulty in identifying the dimensions on the map as all attributes which respondents base their perceptions on have been pre-specified by the researcher. [...]

[...] Chi-square distances 3. Geometric representation 4. Decomposition of inertia 5. Eigenvalues 6. Coordinates 7. Absolute Contribution 8. Squared correlation 19. The interdependence of the points 20. Principal distribution equivalence 21. Supplementary points 22. Outliers 23. Interpretation and dimensionality 24. Solutions to this problem 1. The scree test 2. Interpretability 3. Reproducibility 25. Assumptions and restrictions 26. The process Page Calculating a measure of association 28. Creating the perceptual map 1. [...]

[...] Diagnostics for Perceptual Maps 2. Graphs examining the rating given to the various charities on each variable 2. Multidimensional Scaling 1. Case Processing Summary 3. Correspondence Analysis 1. Visual Display of input Data 1. Row Plot 2. Column Plot 3. Symmetric Plot 4. Asymmetric Row Plot 5. Asymmetric Column Plot 8. Sampling 1. Sampling Procedure 1. Define Population 2. Determine Sampling Frame 3. Select Sampling Techniques 4. Determine Sample Size 5. Execute the Sampling Process 6. Validate the Sample 4. [...]

[...] This is often expressed in terms of distances between objects, which a sample has observed. MDS depicts the structure of n objects from data that approximate the distances between pairs of the objects. This data, which is also known as similarities, dissimilarities or distances, reflects the amount of dissimilarity between pairs. For our purposes we term similarities to refer to great similarity and dissimilarities simply relates to objects that have great dissimilarity. MDS is most useful for products in the early stages of their design. [...]

[...] Analysis of rows and columns 4. Maximum number of dimensions 5. Row and column coordinates 6. Compatibility of row and column coordinates 7. Scaling of coordinates Page Metric of coordinate system 9. Judging the quality of a solution 10. Relative inertia for each dimension 11. Statistical significance 12. Supplementary points 13. Multiple correspondence analysis 14. Analysing the design matrix 15. Fuzzy coding 16. Interpretation of coordinates and other results 17. The Burt Table 18. Data produced 1. Profiles and Masses 2. [...]

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