measurement scales measurement : the assignment of numbers or other symbols to characteristics of...

Measurement Scales Measurement : The assignment of Numbers or other symbols to characteristics of objects according to certain pre-specified rules. Scaling: The generation of a continuum upon which measured objects are located.

Primary Scales of Measurement There are 4 kinds of scales namely: Nominal scale Ordinal scale Interval scale Ratio scale

Nominal scale In this scale numbers are used to identify objects. For example University Registration numbers assigned to students. Have you visited Bangalore? Yes-1, No-2 Yes is coded as one and No is coded as Two. The numeric attached to the answers has no meaning and is a mere identification. If the numbers are interchanged it wont affect the answer.

Example for nominal scale The telephone number is a example of nominal scale where one number is assigned to one subscriber. Similarly bus route numbers are examples of nominal scale. How old are you? This is an example of nominal scale. What is your PAN Card Number? Arranging the books in the library subject wise, author wise

Limitations There is no rank ordering. No mathematical operation is possible. Statistical implication- calculation of standard deviation and the mean is not possible

Ordinal scale (ranking scale) The ordinal scale is used for ranking in most of market research studies. Ordinal scales are used to ascertain the consumer perceptions, preferences etc. This is also known as ranking scale

Example of ordinal scale The respondents may be given a list of brands which may be suitable and were asked to rank on the basis of ordinal scale. Lux Liril Cinthol Lifebuoy Hamam

Example for ordinal scale Rankitem No of Respondents ICinthol150 IILiril300 IIIHamam250 IVLux200 VLifebuoy100 Total1000

Nominal scale- contd In the previous example II is the mode and III is the median. In market research the researchers often ask the respondents to rank the items like for example A soft drink based upon flavor or Color. In such cases the ordinal scale is used

Interval scale Interval scale is more powerful than the nominal and ordinal scale. The distance given on the scale represents equal distance on the property being measured. Interval scale may tell us How far object are apart with respect to an attribute? This means that the difference can be compared. The difference between 1 and 2 is equal to the difference between 2 and 3.

Eg for interval scale Eg 1: Suppose we want to measure the rating of a refrigerator using interval scale it will appear as follows: 1 Brand name Poor------------Good 2 Price High-------------Low 3 Service after sales Poor-----------Good 4 Utility Poor----------Good

Interval scale-contd The researcher cannot conclude that the respondent who gives rating of 6 is 3 times more favorable towards the product under study than the respondent who awards the rating of 2. Eg 2: How many hours you spend to do class assignment every day?

Table 3 use of health drink Income per month 012345 More than 5 No of families 500061457101851 5921445074100152500

E.g.-Contd.. The above table shows that consumption of a health drink not only depends on income but also on the number of children per family. Health drinks are also popular among the family with no children. This shows that even adults consume this drink. It is obvious from the table that 59 out of 500 families consume health drinks even though they have no children. The table also shows that families in the income group of 2001-3000 consume health drink the most.

Module-4- Data Analysis Multivariate analysis This can be studied under: Discriminant analysis Factor analysis Cluster analysis Conjoint analysis Multidimensional scaling

Discriminant Analysis In this analysis 2 or more groups are compared. In the final analysis, we need to find out whether the groups differ one from another.

Example of discriminant analysis Where discriminant analysis is used: Those who buy our brand and those who buy competitors brand. Good salesman and poor salesman, medium salesman. Those who go to food world to buy and those who buy in a kirana shop. Heavy user, medium user and light user of the product.

Equn for discriminant analysis Z= b1x1+b2x2+b3x3 Z= Discriminant score B1=Discriminant weight for variable 1 B2= Discriminant weight for variable 2 B3= Discriminant weight for variable 3 X=Independent variable

Application of discriminant analysis A company manufacturing FMCG products introduces a sales contest among its marketing executives to find out How many distributors can be roped in to handle the companys product. Assume that this contest runs for 3 months. Each marketing executive is given a target regarding number of new distributors and they can generate during the period. This target is fixed and based on the past sales achieved by them about which, the data is available in the company.

Application of discriminant analysis-Contd.. It is also announced that the marketing executives who add 15 or more distributors will be given a maruti omni-van as prize. Those who generate between 5 and 10 distributor will be given a 2 wheeler as prize. Those who generate less than 5 distributor will get nothing. Now assume that 5 marketing executives won a maruti van and 4 won a 2 wheeler.

Application of discriminant analysis-contd.. The company wants to find out, which activities of the marketing executive made the difference in terms of winning a prize and not winning the prize. One can proceed in a number of ways. The company could compare those who won maruti van against others. Alternatively the company might compare those who won, one of the 2 prizes, against those who won nothing.

Application- contd.. Discriminant analysis will highlight the difference in activities performed by each group members to get the prize. The activity might include: More number of calls made to the distributors. More personal visits to the distributors with advance appointments. Use of better convincing skills.

Conducting Discriminant Analysis The steps involved in conducting Discriminant Analysis is as follows: Formulate the problem Estimate the discriminant function coefficients. Interpret the results Assess the validity of discriminant analysis

Factor analysis The main purpose of factor analysis is to group large set of variable factors into fewer factors. Each factor will account for one or more component. Each factor a combination of many variables.

Factor analysis model Mathematically, factor analysis is somewhat similar to multiple regression analysis, in that each variable is expressed as a linear combination of underlying factors.

Factor Analysis Model- Contd.. If the variables are standardized, the factor model may be represented as: Xi=Ai1F1+Ai2F2+Ai3F3+..+AimFm+ViUi Where Xi= ith Standardized variable Aij= standardized multiple regression coefficient of variable i on common factor j. F=Common Factor Vi= standardized regression coefficient of variable i on unique factor i. Ui= the unique factor for variable i. M= number of common factors.

Statistics associated with factor analysis Bartletts test of sphericity: is a test of statistics used to examine the hypothesis that the variables are uncorrelated in the population. In other words, the population correlation matrix is an identity matrix. Correlation matrix: A correlation matrix is a lower triangle matrix showing the simple correlation, r between all the possible pairs of variables included in the analysis. Communality: is the amount of variance, a variable shares with all the other variables being considered. This is also the proportion of variance explained by the common factors. Eigen value: represents the total variance explained by each factor.

Statistics associated with factor analysis- Contd.. Factor loadings: are simple correlations between the variables and the factors. Factor loading plot: A factor loading Plot is the plot of original variables using the factor loadings as coordinates. Factor matrix: A factor matrix contains the factor loadings of all the variables on the factors extracted. Factor scores: Factor Scores are composite scores estimated for each respondent on the derived statistics. KMO: Kaiser Meyer Olkin measure of sampling adequacy: is an index used to examine the appropriateness of factor analysis. High values between 0.5 and 1.0 indicate factor analysis is appropriate. Values below 0.5 imply that factor analysis may not be appropriate.

Statistics associated with factor analysis- Contd.. Percentage of variance: This is the percentage of the total variance attributed to each factor. Residuals: Residuals are the differences between the observed correlations, as given in the input correlation matrix, and the reproduced correlations, as estimated from the factor matrix. Scree plot: A scree plot is a plot of the eigenvalues against the number of factors in order of extraction.

Conducting factor analysis The steps involved in conducting factor analysis is as follows: Formulate the problem Construct of correlation matrix. Determine the method of factor analysis. Determine the number of factors. Rotate the factors. Interpret the factors: calculate the factor scores, select the surrogate variables. Determine the model fit.

Conducting factor analysis- Contd.. Principal component analysis: An approach to factor analysis that considers the total variance in the data. Common factor analysis: An approach to factor analysis that estimates the factors based on the common variance.

Conducting factor analysis- Contd.. Determine the number of factors: The number of factors can be determined using the following approaches: A priori determination. Determination based on Eigen values. Determination based on scree plots. Determination based on percentage of variance. Determination based on split-half reliability: The sample is split in half and factor analysis is performed on each half. Determination based on significance tests.

Conducting factor analysis- Contd.. The rotation of factor can be done based on; Orthogonal Rotation: Rotation of factors in which the axes are maintained at right angles. Variance procedure: It is a commonly used procedure. An orthogonal method of factor rotation that minimizes the number of variables with high loadings on a factor, thereby enhancing the interpretability of the factors. Oblique rotation: Rotation of factors, when the axes are not maintained at right angles.

Factor analysis contd.. There are 2 most commonly employed factors analysis procedures. They are: Principle component analysis Common factor analysis When the objective is to summarize information from a large set of variables in to a few factors, principle component factor analysis is used. On the other hand if the researcher wants to analyze the components of the main factor, common factor analysis is used.

Example of common factor analysis Example: inconvenience inside a car. The components may be: Leg room Seat arrangement Entering the rare seat Inadequate dickey space Door locking mechanism

Example of principle component factor analysis Example: customer feedback about a 2 wheeler manufactured by a company. The MR Manager prepares a questionnaire to study the customer feedback. The researcher has identified 6 variables or factors for this purpose.

e.g for principle factor analysis- contd.. The factors are as follows: Fuel efficiency (A) Durability (B) Comfort (C) Spare parts availability (D ) Breakdown frequency (E) Price (F)

Factor analysis- contd.. The questionnaire may be administered to 5000 respondents. The opinion of the customer is gathered. Let us allot points 1 to 10 for the variables factors A to E. 1 is the lowest and 5 is the highest. Let us assume that the application of factor analysis has led to grouping the variables as follows.

Factor analysis- contd.. A, B, D,E into factor 1 F into factor-2 C into factor-3 Factor-1 can be termed as technical factors Factor-2 can be termed as Price factor. Factor-3 can be termed as Personal factor.

Applications of factor Analysis It is used for market segmentation. Product research: can be employed to determine the brand attributes that influence the consumers choice. Advertising studies: media consumption habits of target audience. Pricing studies: to identify characteristics of price sensitive consumers.

Cluster Analysis Cluster analysis is used to: To classify persons or objects into small number of clusters or groups. To identify specific customer segment for the companys brand. Cluster analysis is a technique used for classifying objects into groups. This can be used to sort data( a number of people, companies, cities, brands or any other objects) into homogenous groups based on their characteristics.

Applications of Cluster Analysis Customer segmentation Estimation of segment sizes Industries where this technique is useful includes Automobiles Retail stores Insurance B to B Durables and packaged goods VALS (consumer Behavior)

Statistics associated with cluster Analysis Agglomeration schedule: An agglomeration schedule gives information on the objects or cases being combined at each stage of the hierarchical clustering process. Cluster centroid: Is the mean values of the variables for all the cases or objects in a particular cluster. Cluster membership: Indicates the cluster to which each case or object belongs. Dendrogram: A Dendrogram or tree graph is a graphical dev ice for displaying clustering results. Distances between cluster centers: These distances indicate how separated the individual pairs of clusters are. Icicle diagram: An icicle diagram is a graphical display of clustering results, so called because it resembles a row of icicles hanging from the eaves of the house. Similarity/distance coefficient matrix: Is a lower triangle matrix containing pair wise distances between objects or cases.

Conducting Cluster Analysis Formulate the problem Select a distance measure Select a clustering procedure Decide on the number of clusters Interpret and profile clusters. Assess the validity of clustering.

Select a clustering Procedure Hierarchical Clustering: A Clustering procedure characterized by the development of hierarchy or tree like structure. Agglomerative clustering: hierarchical clustering procedure where each object starts out in a separate cluster. Divisive clustering: Hierarchical clustering procedure where all the objects start out in one giant cluster. Clusters are formed by dividing this cluster into smaller and smaller clusters. Linkage methods: Agglomerative methods of hierarchical clustering that cluster objects are based on computation of distances between them. Single linkage: Linkage method that is based on minimum distance or the nearest neighbor approach. Complete linkage: Linkage method that is based on maximum distance or the farthest neighbor approach. Average linkage: A Linkage method based on the average distance between all the pairs of objects, where one member of the pair is from each of the clusters.

Select a clustering Procedure- Contd.. Variance methods: An agglomerative method of hierarchical clustering in which clusters are generated to minimize the within cluster variance. Wards procedure: variance method in which the squared Euclidean distance to the cluster means is minimized. Centroid methods: A Variance method of hierarchical clustering in which the distance between 2 clusters is the distance between their centroids.

Select a clustering Procedure- Contd.. Non-hierarchical clusters: A Procedure that first assigns or determines a cluster center and then groups all objects within a prespecified threshold value from the center. Sequential threshold method: A non-hierarchical clustering method in which a cluster center is selected and all the objects within a prespecified threshold value from the center are grouped together. Parallel threshold method: Non-hierarchical clustering method that specifies several cluster centers at once. All objects that are within a prespecified threshold value from the center are grouped together. Optimizing partitioning method: Non-hierarchical clustering method that allows for later reassignment of objects to clusters to optimize an overall criterion.

Cluster analysis is applicable An FMCG company wants to map the profile of its target audience in terms of lifestyle, attitude, and perceptions. A consumer durable company wants to know the features and services a consumer takes into account, when purchasing through catalogues. A housing finance corporation wants to identify and cluster the basic characteristics, lifestyles and mindset of persons who would be availing housing loans. Clustering can be done based on parameters such as interest rates, documentation, processing fee, number of installments

Process There are 2 ways in which cluster analysis is carried out: First, objects/respondents are segmented into a pre- decided number of clusters. In this case a method called non-hierarchical method can be used which partitions data into the specified number of clusters. The second method is called the hierarchical method.

Interpretation of Results Ideally the variables should be measured on an interval or ratio scale. This is because the clustering techniques use the distance measure to find the closest objects to group into clusters. An example of its use can be clustering of towns similar to each other which will help decide where to locate new retail stores.

Interpretation of Results-Contd.. If clusters of customers are found based on their attitudes towards new products and interest in different kinds of activities an estimate of the segment size for each segment of the population can be obtained by looking at the number of objects in each cluster. Names can also be given to clusters to describe each one. Marketing strategies for each segment are based on segment characteristics.

Steps in Cluster Analysis Selection of the sample to be clustered (buyers, products, employees) Definition on which the measurement to be made. (e.g. Product attributes, buyer behavior, characteristics) Clusters should be arranged in hierarchy. Cluster comparison and validation.

Steps in Cluster Analysis-Contd.. Selection of the sample to be clustered (buyers, products, employees) Definition on which the measurement to be made. (e.g. product attributes, buyer characteristics). Computing the similarities among the entities. Arrange the clusters in hierarchy. Cluster comparison and validation.

Conjoint Analysis A technique that attempts to determine the relative importance consumers attach salient attributes and the utilities they attach to the level of attributes. Conjoint analysis is concerned with the measurement of the joint effect of the 2 or more attributes that are important from the consumers point of view.

Statistics associated with conjoint analysis Part worth functions: The part worth functions or utility functions describe the utility consumers attach to the levels of each attribute. Relative importance weights: The relative important weights are estimated and indicate which attributes are important in influencing consumer choice. Attribute levels: The attribute levels denote the values assumed by the attributes. Full profiles: Full profiles or complete profiles of brands are constructed in terms of all the attributes by using the attribute levels specified by the design. Pair wise tables: In Pair wise tables, the respondents evaluate two attributes at the same time until all the required pairs of attributes have been evaluated.

Statistics associated with conjoint analysis-Contd.. Cyclical designs: Cyclical designs are designs employed to reduce the number of paired comparisons. Fractional factorial designs: Fractional factorial designs are designs employed to reduce the number of stimulus profiles to be evaluated in the full profile approach. Orthogonal arrays: Orthogonal arrays are a special class of factorial designs that enable the efficient estimation of all main effects. Internal validity: This involves correlations of the predicted evaluations for the holdout or validation stimuli with those obtained from the respondents.

Steps in Conducting Conjoint Analysis Formulate the Problem Construct the Stimuli. Decide on the form of Input data. Select a Conjoint analysis procedure. Interpret the results. Assess reliability and validity.

Conjoint Analysis Model Conjoint analysis model: The mathematical model expressing the fundamental relationship between attributes and utility in conjoint analysis.

Conjoint Analysis Model-Contd.. The model estimated may be represented by: m ki U(X)= aij xij i=1 j=1 Where U(X)= overall utility of an alternative aij= the part worth contribution or associated with the jth level. (j, j= 1,2..ki) of the ith attribute (i, i = 1,2m) Ki = number of levels of attribute i m = number of attributes Xij = 1 if the jth level of ith attribute is present = 0 otherwise

Hybrid Conjoint Analysis A form of conjoint analysis that can simplify the data collection task and estimate selected interactions as well as all its main effects. It has been developed to serve 2 main purposes: Simplify data collection task by imposing less burden on each respondent. Permit the estimation of selected interactions at the subgroup level as well as all main effects at individual level.

Conjoint Analysis-Contd.. In a situation where the company would like to know the most desirable attributes or their combination for a new product or service, the use of conjoint analysis is not appropriate.

Example for conjoint analysis An airline would like to know, which is the most desirable combination of attributes to a frequent traveller: Punctuality Airfare Quality of food served on the flight Hospitality and empathy shown

Conjoint analysis.. Contd Conjoint analysis is a multivariate technique that captures the exact levels of utility that an individual consumer places on various attributes of the product offering. Conjoint analysis enables direct comparison.

Example of conjoint analysis Designing an automobile loan or insurance plan in the insurance industry. Designing a complex machine for business customers.

Process of conjoint analysis Design attributes for the product are first identified. For a shirt manufacturer, these could be design such as designer shirts Vs plain shirts, this price of Rs400 versus Rs.800. The outlets can have exclusive distribution. All possible combinations of these attributes level are then listed out. Each design combination will be ranked by customers and used as input data for conjoint analysis. Then the utility of the products relative to the price are measured.

Process of conjoint analysis The output is a part-worth or utility for each level of each attribute. For example the design may get a utility level of 5 and plain as 7.5. Similarly, the exclusive distribution may have a part utility of 2, and mass distribution, 5.8. We then put together the part utilities and come up with a total utility for any product combination we want to offer and compare that with the maximum utility combination for this customer segment.

Approach to conjoint analysis From a discussion with the client, identify the design attributes to be studied and the levels at which they can be offered. Then build a list of product concepts on offer. These product concepts are then ranked by customers. Once this data is available, use conjoint analysis to derive the part utilities of each attribute level. This is then used to predict the best product design for the given customer segment. Use the SPSS Conjoint procedure to analyse the data.

Uses of Conjoint Analysis The uses of Conjoint analysis is as follows: Determining the relative importance of attributes in the consumer choice process. Estimating market share of brands that differ in attribute levels. Determining the composition of most preferred brand. Segmenting the market based on similarity of preferences for attribute levels. Applications of conjoint analysis have been made in consumer goods, industrial goods, financial and other services.

MDS The most common and useful marketing application of multidimensional scaling is product positioning or brand positioning. Positioning is essentially concerned with mapping a consumers mind and placing all the competing brands of a product category in appropriate slots or positions on it. One obvious way to do that is to ask customers what they think of competing brands or say 6 attributes with a rating scale of 5 to 10 points. This would result in rating for all the brands on all attributes which could be taken as 2 attributes at a time and plotted on a graph.

MDS A class of procedures for representing perceptions and preferences of respondents spatially by means of a virtual display. Perceived or psychological relationship among stimuli are represented as geometric relationships among points in a multidimensional space.

Statistics and terms associated with MDS Similarity judgments: are ratings on all possible pairs of brands or other stimuli in terms of their similarity using a likert- type scale. Preference rankings: are rank ordering of the brands or other stimuli from the most preferred to least preferred. They are normally obtained from the respondents. Stress: This is lack of fit-measure; higher the values of stress indicates poor fits

Statistics and terms associated with MDS- Contd.. R-Square: R Square is a squared correlation Index that indicates the proportion of variance of the optimally scaled data that can be accounted for by the MDS Procedure. This is a goodness of fit measure. Spatial map: Perceived relationship among brands or other stimuli are represented as geometric relationship among points in a Multi Dimensional space called spatial map. Coordinates: indicate the positioning of a brand or a stimulus in a spatial map. unfolding: The representation of both brands and respondents as points in the same space is referred to as unfolding.

Conducting MDS Formulate the problem Obtain input data Select an MDS Procedure Decide on the number of dimensions. Label the dimensions and interpret the configuration. Assess reliability and validity.

Conducting MDS-Contd.. Obtain Input Data: Perception Data: Direct Approaches: In Direct Approaches to gathering perception data, the respondent, the respondents are asked to judge how similar or dissimilar the various brands or stimuli are, using their own criteria. Respondents are often required to rate all possible pairs of brands or stimuli in terms of similarity on a likert scale. These data are referred to as similarity judgements.

Example Similarity judgments on all the possible pairs of toothpaste brands may be obtained in the following manner: very very Dissimilar similar Colgate vs. Crest 1 2 3 4 5 6 7 Aqua fresh vs,crest 1 2 3 4 5 6 7 Colgate vs aquafresh 1 2 3 4 5 6 7 The number of pairs to be evaluated is n(n-1)/2, where n is the number of stimuli. Other procedures are also available.

Conducting MDS- Contd.. Derived approach: In MDS attribute based approach to collecting perception data requiring the respondents to rate the stimuli on the identified attributes using semantic differential or likert scale For example different brands of toothpaste may be rated on attributes such as: Whitens ------------------------------------------Does not teeth whiten teeth

Conducting MDS- Contd.. Direct Vs Derived Approach: Direct approaches have the advantage that the researcher does not have to identify a set of salient attributes. Respondents make similarity judgments using their own criteria, as they would under normal circumstances. The disadvantages are that the criteria are influenced by the brands or stimuli being evaluated. If various brands of automobiles being evaluated are in the same price range, then price will not emerge as an important factor. It may be difficult to determine before analysis if and how the individual respondents judgment should be combined.

Conducting MDS- Contd.. Direct Vs Derived Approach: The advantage of Derived or Attribute based approach is that it is easy to identify respondents with homogenous perceptions. The respondents can be clustered based on the attribute ratings. It is also easier to label the dimensions. A disadvantage is that the researcher must identify all the salient attributes a difficult task. The spatial map obtained depends on the attributes identified.

Conducting MDS- Contd.. Select an MDS Procedure Non-metric MDS- A type of MDS method that assumes that the input data are ordinal. Metric MDS- A MDS method that assumes that the input data are metric.

Conducting MDS- Contd.. Decide on Number of Dimensions: The following guidelines are suggested for determining the number of dimensions: A priori knowledge: theory or past research may suggest a particular number of dimensions. Interpretability of the spatial map: Generally it is difficult to interpret configurations or maps derived in more than 3 dimensions. Elbow criterion: A plot of stress versus dimensionality should be examined. The point in this plot usually form a convex pattern. The point at which a n elbow or a sharp bend occurs indicates appropriate no of dimensions. Ease of use: it is generally easier to work with 2 dimensional maps or configurations than with those involving more dimensions. Statistical approach: It is used for determining dimensionality.

Conducting MDS- Contd.. Scaling Preference Data: Internal Preference Data: Takes into account both brands stimuli and respondent points. External analysis of preference: vectors based on preference data.

Example of MDS A product category of shampoos could be identified as having 5 attributes important to consumers- price, lather, fragrance, consistency, and favorable effects on hair. If this were to be rated on a 7-point scale for say six leading brands of shampoo A, B, C,D,E, and F, then we could pick up any 2 attributes and plot the six brands on a map according to consumer ratings.

Example of MDS- Contd.. For example if we plotted rating on price Versus rating on favorable effect on hair, we may find that all the 6 brands are positioned in different places based on consumer ratings or perceptions. This is called perceptual map of consumer perception about competing brands in a product category.

Methods of MDS Attribute based approach Similarity/dissimilarity based approach

Recommended Usage Knowing particular attribute Number of dimensions as well as interpretation. Naming of attributes of the brands and their target segment such as age, price, quality, or attempted positioning through brand communication and so on.

Research report There are 2 types of report Oral report Written report Oral report: This type of reporting is required, when the researchers are asked to make an oral presentation. Making an oral presentation is somewhat difficult compared to written report. This is because the reporter has to interact directly with the audience. Any faltering during an oral presentation can leave a negative impression on the audience.

Nature of an oral presentation Opening Finding/Conclusion Recommendation Method of presentation.

Points to remember in oral presentation Language used must be simple and understandable. Time Management should be adhered. Use of charts, graphs etc, will enhance understanding by the audience. Vital data such as figures, may be printed and circulated to the audience, so that their ability to comprehend increases. The presenter should know his target audience well in advance. The presenter should know the purpose of the report.

Guidelines for oral report Employ visual aids Avoid reading the report KYA- Know Your Audience Plan and deliver.

Types of written reports On the basis of time interval reports can be classified as: Daily, Weekly, Monthly, Quarterly, Yearly Types of Report: Short Report, long Report, Formal Report, Informal Report, Government Report.

Preparation of written reports Preparation of research report: The following is the format of research report: Title Page Page contents/Table of contents Executive Summary Introduction Methodology Data collection and Analysis Conclusions Suggestions and Recommendations Bibliography. Appendix

Explanation of contents of reports Executive summary: This includes a brief detail of what the report consists of. It should be written in one or two pages. Body: this section include: Introduction: the introduction should clearly explain the decision problem. Sometimes it consists of details about the topic, company profile etc.

Contents of report- contd.. Methodology: this includes the following: Statement of objectives Data collection method: whether primary, secondary data or both. Questionnaire design, ie tools for data collection. Sample design: which includes sample type, sample size etc.

Contents of report- contd.. Analysis and interpretation: this should include analysis of question in the questionnaire by using tables and graphs and other statistical tools.

Contents of report- contd.. Conclusions: this includes the conclusions drawn from the study. Suggestions and recommendations: based on the conclusions, suggestions and recommendations are made. Appendix: the purpose of appendix is to provide a place for material which is not absolutely necessary in the body of the report: such as questionnaire, broucher etc.

Bibliography If portions of the report is based on secondary data, use bibliography section to list the publications or sources that you have consulted. It includes: Title of the book Name of the journal in case of article Volume no Page number Edition

Writing the Report- Contd.. Pre writing considerations: The outline : I. Major Topic Heading A Major subtopic heading 1. Sub topic a. Minor subtopic (1) Further details (a) Even further details

Writing the Report- Contd.. Writing Considerations: Contd.. The Bibliography Writing the Draft Readability Comprehensibility Tone Final proof.

Presenting the research report Carrying out professional approach Use short paragraphs Use headings and subheadings Use vertical listings of points. Incident part of the text that represents listings, long quotations or examples.

Presenting the research report Presentation of statistics involves 4 ways: A text paragraph Semi tabular form Tables Graphics Pie charts

Presenting the research report Preparation Opening Findings and conclusions Recommendations. Delivery

Presenting the research report- Contd.. Common Research Problems Speaker problems Vocal characteristics: Should not speak too softly Do not speak to rapidly Vary volume tone quality Do not use overworked pet phrase, uhs, etc. Do not stare into space Do not misuse visuals Do not hitch or tug on clothing, scratch or fiddle with pocket. Do not rock back and forth or twist from side to side, or lean too much on the lectern.

Presenting the research report- Contd.. Other problems Cost considerations Limitations on time Quality of research report Effectiveness of research.

Presenting the research report Audio-Visuals Low tech: Chalk board and white boards Hand out materials Flip charts Overhead transparencies. Slides High Tech Computer drawn visuals Computer animations

Writing the research Report- Contd.. Other guidelines: Consider the audience Attitude 1: adopt fresh mind approach Kiss Approach (Keep it short and simple).

Oral and written report Distinguish between oral and written report: oral Report No rigid standard format Remembering all that is said is difficult if not impossible. This is because the presenter cannot be interrupted frequently for clarification. Tone, voice modulation, comprehensibility and several other communication factors play an important role. Correcting mistakes if any is difficult. The audience has no control over the speed of presentation. The audience does not have the choice of picking and choosing from the presentation.

Oral and written report Distinguish between oral and written report: Written Report Standard format can be adopted This can be read a number of times and clarification can be sought whenever the reader chooses. Free from presentation problems. Mistakes if any, can be pinpointed and corrected. Not applicable The reader can pick and choose what he thinks is relevant to him. For instance, the need for information is different for technical and non technical persons.

measurement scales measurement : the assignment of numbers or other symbols to characteristics of...

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