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# 9 Quantitative Analysis Techniques

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### 9 Quantitative Analysis Techniques

1. 1. Research Design and Methods Quantitative Analysis Techniques FEKD62 Ralf Müller Tomas Blomquist School of Business and Economics Umeå University Concepts About Relationships Presence Nature Direction Strength of Association 1
2. 2. Relationship Presence . . . . assesses whether a systematic relationship exists between two or more variables. If we find statistical significance between the variables we say a relationship is present. Nature of Relationships Relationships between variables typically are described as either linear or nonlinear. •Linear relationship = a “straight line - association” between two or more variables. •Nonlinear relationship = often referred to as curvilinear, it is best described by a curve instead of a straight line. 2
3. 3. Direction of Relationship The direction of a relationship can be either positive or negative. Positive relationship = when one variable increases, e.g., loyalty to employer, then so does another related one, e.g. effort put forth for employer. Negative relationship = when one variable increases, e.g., satisfaction with job, then a related one decreases, e.g. likelihood of searching for another job. Strength of Association When a consistent and systematic relationship is present, the researcher must determine the strength of association. The strength ranges from very strong to slight. 3
4. 4. Covariation . . . . exists when one variable consistently and systematically changes relative to another variable. The correlation coefficient is used to assess this linkage. Correlation Coefficients Positive Correlation = when the value of X increases, + 1.0 the value of Y also increases. When the value of X decreases, the value of Y also decreases. 0.0 Zero Correlation = the value of Y does not increase or decrease with the value of X. Negative Correlation = when the value of X increases, - 1.0 the value of Y decreases. When the value of X decreases, the value of Y increases. 4
5. 5. Rules of Thumb about Correlation Coefficient Size Coefficient Strength of Range Association +/– .91 to +/– 1.00 Very Strong +/– .71 to +/– .90 High +/– .41 to +/– .70 Moderate +/– .21 to +/– .40 Small +/– .01 to +/– .20 Slight Pearson Correlation The Pearson correlation coefficient measures the linear association between two metric variables. It ranges from – 1.00 to + 1.00, with zero representing absolutely no association. The larger the coefficient the stronger the linkage and the smaller the coefficient the weaker the relationship. 5
6. 6. Coefficient of Determination The coefficient of determination is the square of the correlation coefficient, or r2. It ranges from 0.00 to 1.00 and is the amount of variation in one variable explained by one or more other variables. 6
7. 7. Definitions of Statistical Techniques ANOVA (analysis of variance) is used to examine statistical differences between the means of two or more groups. The dependent variable is metric and the independent variable(s) is nonmetric. One-way ANOVA has a single nonmetric independent variable and two-way ANOVA can have two or more nonmetric independent variables. Cluster analysis enables researchers to place objects (e.g., customers, brands, products) into groups so that objects within the groups are similar to each other. At the same time, objects in any particular group are different from objects in all other groups. Conjoint analysis enables researchers to determine the preferences individuals have for various products and services, and which product features are valued the most. Definitions of Statistical Techniques Discriminant analysis enables the researcher to predict group membership using two or more metric dependent variables. The group membership variable is a nonmetric dependent variable. Factor analysis is used to summarize the information from a large number of variables into a much smaller number of variables or factors. This technique is used to combine variables whereas cluster analysis is used to identify groups with similar characteristics. Logistic regression is a special type of regression that can have a non-metric/categorical dependent variable. Multiple regression has a single metric dependent variable and several metric independent variables. 7
8. 8. Calculating the “Explained” and “Unexplained” Variance in Regression The explained variance in regression, referred to as r2, is calculated by dividing the regression sum of squares by the total sum of squares. The unexplained variance in regression, referred to as residual variance, is calculated by dividing the residual sum of squares by the total sum of squares. How to calculate the t-value? The t-value is calculated by dividing the regression coefficient by its standard error. 8
9. 9. How to interpret the regression coefficient The regression coefficient of .459 for Samouel’s X1– Food Quality reported in Exhibit 11-11 is interpreted as follows: “ . . . for every unit that X1 increases, X17 will increase by .459 units.” Recall that in this example X1 is the independent (predictor) variable and X17 is the dependent variable. Dummy Variable . . . . an independent variable that has two (or more) distinct levels, which are coded 0 and 1. 9
10. 10. Regression Analysis Terms Explained variance = R2. Unexplained variance or error = residuals. Regression Assumptions The error variance is constant over all values of the independent variables; The errors are uncorrelated with each of the independent variables; and The errors are normally distributed. 10
11. 11. Residuals Plots Plot of standardized residuals – enables you to determine if the errors are normally distributed Normal probability plot – enables you to determine if the errors are normally distributed. It compares the observed standardized residuals against the expected standardized residuals from a normal distribution Plot of standardized residuals – can be used to identify outliers. It compares the standardized predicted values of the dependent variable against the standardized residuals from the regression equation. Exhibit A-5 Histogram of Employee Survey Dependent Variable X15 – Proud Histogram Dependent Variable: X15 -- Proud 10 8 6 4 Frequency Std. Dev = .97 2 Mean = 0.00 N = 63.00 0 -1.75 -1.25 -.75 -.25 .25 .75 1.25 1.75 2.25 -1.50 -1.00 -.50 0.00 .50 1.00 1.50 2.00 Regression Standardized Residual 11
12. 12. Normal Probability Plot of Regression Standardized Residuals Normal P-P Plot of Regression Standardized Residual Dependent Variable: X15 -- Proud 1.00 .75 Expected Cum Prob .50 .25 0.00 0.00 .25 .50 .75 1.00 Observed Cum Prob Scatterplot Scatterplot Dependent Variable: X15 -- Proud Regression Standardized Predicted Value 3 2 1 0 -1 -2 -2 -1 0 1 2 3 Regression Standardized Residual 12
13. 13. Example: Communication Research Appropriate Formal Situational Variables Communication Variables Organisation Structure H2 (+) Communication Frequency Level of organic Daily, (bi)-weekly, organisation structure monthly, at milestone, phase end, project end H3 Research Model (+) H H4 (-) ) Methodology Clearness H6 (+) Communication Media Richness Written, verbal, face-to-face ) H5 (- Objective Clearness Relational Norms Communication Contents Status, changes, issues, Flexibility, next steps, analysis, information exchange, measures solidarity H2 to H5: Hypothesized relationships Exploratory investigation Example: Communication Research Standard Deviation Questionnaire Item(s) Mean Variable (from Appendix A-3) (SD) Project Variables Respondents Role 1 N/A N/A Project Type 2, 3 N/A N/A Objective Clearness 4, 5, 6 5.39 (1.15) Methodology Clearness 7, 8, 9 5.05 (1.32) Summary statistics Relational Norms 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 5.45 (0.90) Organisation Structure 68, 69, 70, 71, 72, 73, 74 4.58 (1.05) Project Performance 65, 66, 67 5.69 (0.96) Communication Media Importance of Written Communication 20 6.34 (0.97) Importance of Verbal Communication 21 6.19 (0.96) Importance Personal Communication 22 6.21 (1.04) Communication Frequency Variable Interval Communication 27, 28, 33, 34, 35, 41, 42 2.83 (0.24) Fixed Interval Communication 25, 26, 32, 33, 39, 40 3.32 (0.44) Continuous Communication 23, 24, 30, 38 4.04 (0.79) Communication Contents Personal Review 58, 59, 60, 61, 62, 63, 64 5.23 (0.63) Project Analysis 45, 48, 52, 55, 59, 62 4.40 (0.45) Written Status 44, 46, 47, 49, 50, 57, 64 5.51 (0.91) Verbal Update 51, 53, 54, 56 5.47 (0.16) Demographic Variables Age 80 42.2 (8.15) Years of work experience 81 20.2 (8.87) Years of experience in proj. mgmt. 82 10.3 (6.47) Years as sponsor 83 5.9 (5.19) 13
14. 14. Example: Communication Research Frequency Factors Variable Interval Fixed Interval Continuous Final Factor Names: Communication Communication Communication Factor Eigenvalue 5.760 2.277 1.728 % Variance Explained 29.684 18.309 13.040 Analysis Factor Loading Variable/Item Frequency in Communication Written daily communication 0.007 -0.273 0.583 Written weekly communication -0.077 -0.014 0.688 Written bi-weekly communication -0.087 -0.042 0.803 Written monthly communication 0.349 -0.243 0.641 Written communication at milestone achievement 0.121 -0.186 0.808 Written communication at phase or project end 0.074 -0.117 0.826 No formal written communication* Verbal daily communication -0.060 -0.107 0.766 Verbal weekly communication** Verbal bi-weekly communication 0.229 -0.242 0.781 Verbal monthly communication -0.273 0.485 0.570 Verbal communication at milestone achievement 0.196 -0.131 0.847 Verbal communication at phase or project end 0.090 -0.120 0.849 No formal Verbal communication* Personal daily communication** Personal weekly communication -0.214 0.146 0.563 Personal bi-weekly communication -0.070 0.309 0.668 Personal monthly communication 0.342 -0.011 0.549 Personal communication at milestone achievement 0.115 0.007 0.821 Personal communication at phase or project end 0.066 -0.027 0.841 No formal Personal communication* * Items not included because of low preference ** Items dropped because of low MSA or Alpha Example: Communication Research Contents Factors Written status Personal Project report with Verbal Final Factor Names: Factor Analysis Review Analysis possible follow- Update up Eigenvalue 5.136 2.487 2.091 1.963 % Variance Explained 15.396 13.999 13.812 12.392 Factor Loading Variable/Item Contents in Communication Written contents: status and achievements 0.036 0.151 0.151 0.489 Written contents: measures and quality metrics -0.078 0.356 -0.171 0.673 Written contents: issues or 'open items' -0.004 0.044 0.212 0.628 Written contents: project changes -0.027 0.186 0.177 0.598 Written contents: trends 0.156 0.364 -0.108 0.663 Written contents: next steps 0.137 -0.095 0.112 0.611 Written contents: other 0.217 0.139 -0.269 0.554 Verbal contents: status and achievements 0.025 0.154 -0.093 0.740 Verbal contents: measures and quality metrics -0.029 0.027 0.353 0.741 Verbal contents: issues or 'open items' 0.150 -0.051 0.154 0.747 Verbal contents: project changes 0.053 0.069 0.115 0.808 Verbal contents: trends 0.047 0.109 0.194 0.759 Verbal contents: next steps 0.301 -0.034 0.350 0.481 Verbal contents: other 0.129 0.137 -0.093 0.548 Personal contents: status and achievements 0.060 -0.114 0.184 0.762 Personal contents: measures and quality metrics -0.104 -0.016 0.560 0.590 Personal contents: issues or 'open items' 0.006 0.272 0.088 0.711 Personal contents: project changes 0.085 0.106 0.304 0.727 Personal contents: trends 0.000 -0.096 0.540 0.624 Personal contents: next steps 0.012 0.367 -0.059 0.750 Personal contents: other 0.186 -0.263 0.465 0.495 14
15. 15. Example: Communication Research Appropriate Formal Situational Variables Communication Variables Organisation Structure Level of organic organisation structure Communication H2 (+) Frequency Variable Intervals --- Fixed Intervals --- Continuous Methodology Clearness Revised Research Model H3 (-) ) ,H H 6( Communication +) 4 4( Contents and Media Objective Clearness -) Personal Review H5 (+) --- Project Analysis --- Written Status Relational Norms --- Verbal Update Flexibility, information exchange, solidarity H2 to H6: Hypothesized relationships Exploratory investigation Example: Communication Research Normal distribution 40 30 of dependent variables 30 20 20 10 10 Std. Dev = 1.00 Std. Dev = 1.00 Mean = 0.00 Mean = 0.00 N = 200.00 0 N = 200.00 0 -1.50 -.50 .50 1.50 2.50 -1.00 0.00 1.00 2.00 -2 -2 -1 -1 -.7 -.2 .2 .7 1.2 1.7 2.2 5 5 .7 .2 .7 .2 5 5 5 5 5 5 5 5 5 Variable Interval Communication Continuous Communication 30 30 30 20 20 20 10 10 10 Std. Dev = 1.00 Std. Dev = 1.00 Std. Dev = 1.00 Mean = 0.00 Mean = 0.00 Mean = 0.00 N = 200.00 0 N = 200.00 0 N = 200.00 0 -2 -1 -1 -.5 0.0 .5 1.0 1.5 2.0 2.5 0 .0 .5 .0 0 -3 -3 -2 -2 -1 -1 -.5 0.0 .5 1.0 1.5 2.0 0 0 0 0 0 0 0 0 0 .5 .0 .5 .0 .5 .0 -3 -3 -2 -2 -1 -1 -.7 -.2 .2 .7 1.2 1.7 0 0 0 0 0 0 0 0 0 0 0 5 5 .7 .2 .7 .2 .7 .2 5 5 5 5 5 5 5 5 5 5 Fixed Interval Communication Project Analysis Personal Reviews 15