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1st Level Activities rely directly on your applying course content from the unit. They do not require you to do any additional work beyond applying the course content from the unit to the activity.

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Note: The format for papers can be either APA, MLA or another standard format. Citations can be in the text or noted in notes or in a bibliography. Double spaced is preferred. Assume that approximately 250 words equals one page.

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Unit 5: DATA ANALYSIS We put a great deal of faith in scientific methods of data collection. Yet, the process can be riddled with errors. Before moving to how we should analyze data collected during an intervention it would be helpful to consider some common research errors. These include: • mistakes in recording and categorizing data • mistakes in sampling (perhaps drawing a small sample or one that is not representative of the larger workforce — for example, having the poorest 10% in terms of performance and designing a training program around the information they provide) • subject misrepresentation — or the possibility that people may provide inaccurate information • investigator bias that limits what one will find — only looking for and finding what we believe to be the problem • faulty instrumentation or the use of data collection techniques that are neither valid nor reliable Validity refers to measuring what you actually intend to measure, whereas reliability concerns measuring with consistency and accuracy. We can also experience errors when interpreting data. Common mistakes involve: • making too much out of limited data (drawing conclusions that may not be warranted) • making too many decisions based on limited data • ignoring important findings because of the commitment to established systems Effective diagnosis, then, is a function of two factors. Remaining objective and providing helpful feedback. Objectivity involves: • being aware of any biases in the data collected • questioning and confirming findings before drawing conclusions • looking beyond symptoms, to recognize actual problems • recognizing patterns that emerge • considering the uniqueness of each intervention • understanding how one’s presence can influence the data collection process Providing Feedback involves: • converting feedback into usable information for the client • clarifying findings and offering assistance in the interpretation of the data • translating findings into an action plans (solutions) for how to address the identified problems. To develop your skills in providing useful feedback and action plans, complete the Providing Useful Feedback Activity in this unit. There are many ways to analyze data collected depending on the type of data collection techniques used. Data can be numerical (quantitative) or textual (qualitative). Numerical/quantitative data derive from questionnaires or interviews and require statistical analysis. A review of basic statistical techniques is provided in the associated power points. You can find these under the supplemental materials for this unit. Descriptive Statistics Advanced Statistics Unit 5 Unit 5 Descriptive Statistics Advanced Statistics Textual/qualitative data comes from many sources, any which ask for people to report in their own words. This may include open-ended responses on questionnaires, interviews, focus groups, or organizational documents. Regardless of the source, textual/qualitative data require the use of textual analysis. More information about textual analysis is available in the associated power point, which can be found in the supplemental materials for this unit. Textual Analysis Unit 5 Textual Analysis Analyzing Results When reporting the results of a needs assessment it is important to include information about the data analysis, but not so much information that the findings are overshadowed by the analysis. Thus, it is important to streamline the information presented. Only provide the information that is essential. Just because you collected data does not mean you need to report about it necessarily. If it tells us little it will only complicate the process and can be left out of the final report. Or it can be part of a more detailed report when a shorter concise report is also made available. Final written reports should include: • a general overview • objectives and scope • methods of data collection • methods for data analysis • findings and conclusions • recommendations based on findings • expected benefits • implementation guidelines Advanced Statistics Unit 5 There are several related topics in this unit… Types of Variables in Analysis Univariate and Multivariate Statistics Overview Univariate Statistics Multivariate Statistics Types of Variables in Analysis Statistics Independent Variables (IV) This is the variable thought to influence or cause a change in the value of another variable. For example, if you do not get enough sleep you will experience fatigue and drowsiness during work. Lack of sleep, then, is the independent variable thought to affect fatigue and drowsiness. Dependent Variables (DV) This is the variable that is thought to be changed or affected by another (independent) variable. Said another way, the value of the dependent variable is responsive to or determined by changes in the independent variable. In the example above fatigue and drowsiness are the variables affected. We will experience more fatigue and drowsiness if we have less sleep. Confounding Variables This is a variable that confounds, or confuses, the relationship between the independent and dependent variables. Or we can think of it this way…something other than the independent variable is accounting for changes in the dependent variable. For example, how engaging and interesting a meeting is (vs. boring) will affect whether or not you feel fatigue and drowsiness during the meeting. Thus, lack of sleep is not accounting for fatigue or drowsiness. Rather the nature of the meeting or a combination of lack of sleep and the nature of the meeting are causing fatigue and drowsiness. Univariate and Multivariate Statistics Overview Statistics We differentiate statistics as univariate or multivariate depending on the number of dependent variables involved in the statistical analysis. When there is a single dependent variable we use a univariate statistic. When there is more than one dependent variable we use a multivariate statistic. We also need to consider how both the dependent and independent variables were measured in order to determine what statistic is appropriate. Remember that we can measure numerically (interval and ratio level of measurement) or we can measure simply by differentiating between types (nominal level of measurement). Univariate Statistics Statistics There are two groups of univariate statistics we commonly use when we have a single numerical dependent variable. The first set are appropriate when we have a nominal/categorical independent variable. This would include statistics that compare categories or groups like men/women, highly satisfied/dissatisfied employees, youth/seniors, etc. These include… t-test ANOVA ANCOVA and Factorial Analysis of Variance Univariate Statistics Statistics We use the following statistics when we have a single numerical dependent variable and we want to make… t-test a simple comparison between two groups ANOVA (a one-way analysis of variance) a comparison between three or more groups ANCOVA a comparison between three or more groups while controlling for a confounding variable In all these cases we have only a single independent variable, which may be comprised of two, three, or more groups. However, when we have more than one independent variable we need to use a factorial analysis of variance. Factorial Analysis of Variance Statistics A factorial analysis of variance involves a comparison of scores on a single, numerical dependent variable — the value of which is determined by several nominal or categorical independent variables. Factorial analyses of variance are prefaced with a numerical string or statement that indicates: the number of independent variables (designated by the total number of numbers in the string, not the values of the numbers) and the number of levels of each independent variable (designated by the actual values of each number in the string) Factorial Analysis of Variance Statistics So for example, a 3x2x3 factorial analysis of variance has… 3 independent variables, the first with 3 levels, the second with 2 levels, and the third with 3 levels. Similarly, a 4x2 factorial analysis of variance has… two independent variables, the first with four levels and the second with two. This could be a comparison that examines student achievement (A, B, C, and D students) and sex (male, female). Univariate Statistics Statistics When we attempt to determine if variables are related and both the independent and dependent variables have been measured numerically we use one of the following univariate statistics… Correlation simply assessing the relationship between independent and dependent variables Regression assessing the ability of the independent variable to predict the value of the dependent variable Multiple Regression assessing the predictive ability of several independent variables on a single dependent variable Univariate Statistics Statistics The chart below helps to clarify how the common univariate statistical procedures relate and differ from one another. Being univariate all the statistics below have a single dependent variable that is numerical (measured at the interval or ratio level of measurement). t-test (2 groups) ANOVA (3+ groups) ANCOVA (while controlling) Factorial Analysis of Variance (with more than 1 IV) Correlation (relating) Regression (predicting) Multiple Regression (with more than 1 IV) The family of statistics in the left-hand column have nominal/categorical independent variables (abbreviated in the chart as IV) and therefore involve comparisons between groups. The family of statistics in the right-hand column have numerical independent variables and thus involve assessing relationships between variables (versus groups). Multivariate Statistics Statistics Multivariate statistics are appropriate when we have more than one dependent variable. It is helpful to think of them as an extension of the two previous groups discussed. When we compare groups and we have more than one dependent variable we move from an ANOVA to a… MANOVA compares groups in terms of more than one dependent variable Or from an ANCOVA to a… MANCOVA compares groups in terms of more than one dependent variable while controlling for a confounding variable Multivariate Statistics Statistics Similarly, we can move from a multiple regression (which considers how several numerical independent variables predict a single numerical dependent variable) to a… Canonical Correlation examines the relationship between multiple independent and multiple dependent variables all of which are numerical or, said another way, examines the relationship between a group of numerical independent and a group of numerical dependent variables Multivariate Statistics Statistics The chart below serves to clarify how the common multivariate statistical procedures relate and differ from one another. As multivariate statistics all of those listed below have multiple dependent variables (abbreviated as DV in the chart) that are numerical in nature. MANOVA (more than 1 DV) MANCOVA (while controlling) Canonical Correlation (comparing two sets of variables) As with the univariate families of statistics, the family of statistics in the left-hand column have nominal/categorical independent variables and therefore involve comparisons between groups. The family of statistics in the right-hand column have numerical independent variables and thus involve assessing relationships between variables (versus groups). Uni- and Multivariate Statistics Statistics Finally, the chart below puts both the univariate and multivariate statistics together. You can see then how the univariate statistics link to the multivariate statistics. Univariate Statistics (Single Dependent Variable) t-test (2 groups) Correlation (relating) ANOVA (3+ groups) Regression (predicting) ANCOVA (while controlling) Multiple Regression Factorial Analysis (with more than 1 IV) of Variance (with more than 1 IV) Multivariate Statistics (More Than One Dependent Variable) MANOVA (more than 1 DV) MANCOVA (while controlling) Canonical Correlation (comparing two sets of variables) Descriptive Statistics Unit 5 There are several related topics in this unit… Descriptive Statistics Overview Measures of Central Tendency Measures of Dispersion Descriptive Statistics Overview Statistics Descriptive Statistics tell us about specific trends in our data and describe specific features of our sample. For example, a researcher will use descriptive statistics to tell readers about the proportion of men and women who participated in a study. The research may write something like: “In this study 40% of the sample were men, whereas 60% were female.” Or the researcher may inform readers about participants’ average scores on a particular variable in the study. In this case the researcher may say: “The mean score on the communication competence measure was 14.55”. The primary descriptive statistics fall into one of two “families”: measures of central tendency measures of dispersion. Measures of Central Tendency Statistics Measures of central tendency, as the name infers, tell us about a central characteristic of the data. Measures of central tendency include… the Mode the Median and the Mean Mode Statistics The mode is the simplest measure of central tendency. It indicates which score or value in a distribution occurs most frequently. The mode is appropriate when we have nominal or categorical data. In these instances we are interested in how often each category was used or appeared. In essence we count observations that appear in each category and then report which category had the most observations. Thus, the mode is the descriptive statistic that tells us which category has the most observations or which category appears most often in the data. Mode Statistics Say, for example, that we are interested in people’s perceptions of what constitutes sexual harassment? To determine this we could provide people with a list of behaviors and ask them to respond by simply checking “yes” or “no” (nominal categories) if they believe the behavior reflects sexual harassment: 1. sexual comments 2. inappropriate gaze 3. sexual jokes 4. display of pornographic materials in the office 5. “pick-up” or “come on” lines ____ yes ____ yes ____ yes ____ yes ____ yes ____ no ____ no ____ no ____ no ____ no The mode will tell us which of these behaviors people perceived to be more sexually harassing compared to the others as it would reflect the category that had the most “yes” responses. Or if we were interested in the least sexually harassing behavior, we could count up the “no” responses and report the mode for “no” responses. Median Statistics The median divides a distribution of quantitative data exactly in half. It is the score above which and below which half the observations fall. The median is most appropriate for describing the center point of a set of ordinal data because it tells us the point at which half of the cases rank higher and half rank lower. For example, in a horse race the horse that finished fifth out of nine represents the median, as four horses finished before or above it and four horses finished behind or below it. Median Statistics The median also can be used with interval/ratio data, but can be problematic because it is not sensitive to extreme scores. That is, two distributions may have the same median or middle point, but one could include much higher and/or lower values than the other. Simply seeing the median would lead us to believe that the two distributions of scores are more similar than they actually are. For example: 4 10 10 11 12 13 15 15 28 17 36 21 47 25 The median for both distributions is 15, but the first distribution includes much lower and higher values (from 4 to 47) than the second one (from 10 to 25). Median Statistics Beyond simply describing the middle point of a distribution, researchers may use the median to create groups to compare. This is called a median split. When researchers have ordinal or ratio data but want to create groups or categories they can do so by using a median split to create two groups. Accordingly, the researcher determines what the median is for the variable of interest and then creates a “high” group with scores above the median and a “low group” with scores below the median. Median Statistics For example, a researcher interested in comparing people high and low in verbal aggressiveness can find the median of the verbal aggressiveness scores for all participants. She can then take all of the cases above that median to create the “high” group and all the scores below that median to create the “low” group. Then the researcher can compare people high and low in verbal aggressiveness on some other variable of interest. Do people high and low in verbal aggressiveness differ with regard to their marital satisfaction? Their communication competence? Mean Statistics The mean is the arithmetic average. It is computed by adding all the scores in a distribution and dividing by the total number of scores. It helps to clarify what the average score on a variable of interest is. For example, we may see any of the following reported. “The mean… “…for communication apprehension was 14.56.” “…for hours of television watched per week was 8.56.” “…for age of respondents in this study was 43.69 years.” Mean Statistics The mean is appropriate for interval/ratio data because of “assumed equivalence” or the idea that all points on the scale are assumed to be of equal distance from one another (i.e.., 1 is the same distance from two as two is from three and so on). Unlike the other types of central tendency descriptive statistics the mean is sensitive to all scores, including extreme scores, in the distribution. That is why it is thought to be the most sophisticated measure of central tendency. Measures of Dispersion Statistics Measures of dispersion show us how data spread out in a distribution. Think about, for example, dropping a glass of water and a can of motor oil on the floor. Both will spill and disperse (i.e., spread out), but they will do so very differently. Thus, measures of dispersion tell us about how data spread out across a distribution. They include… the Range Variance and Standard Deviation Range Statistics The range is the simplest measure of dispersion. It reports the distance between the highest and lowest scores in the distribution. The range, therefore, is calculated by subtracting the lowest number from the highest number in the distribution. The range gives a general sense of how much the data spread out across the distribution, which can be helpful for understanding whether a study included a lot of variability or whether it drew from a narrow spectrum. For example, if a researcher intends to study a communication variable across a wide range of age groups, a sample of people aged 18-21 (a range of 3) is not very diverse. Yet a sample of people aged 18-70 (a range of 52) is. Range Statistics One concern with the range is sensitivity to extreme scores. Because the range takes into account all scores in the distribution it can be misleading when “outlier” scores exist in a distribution. Outliers are scores that are far removed from the rest of the distribution. In the example of age just used you could have a distribution that ranges from 18-70, yet there is only one person aged 70 and the next closest score is actually 24. The age of 70 makes the distribution look much larger than it actually is once you take this outlier into account. If we exclude the outlier, which often researchers do when necessary, the range is actually 6 as the scores spread from 18-24, not 58 as is the case when the outlier is included and the scores spread from 18-70. To avoid problems with outliers researchers may report the interquartile range. This is the range of scores representing the middle 50% of scores (or the middle two quarters of the distribution). The upper and lower 25% of scores (the outer quarters where outliers will be) are excluded. An interquartile range provides a more conservative representation of the range. Variance Statistics Variance is the average distance of scores from the mean, in squared units. We can compute variance when we have interval or ratio data. Why squared units? Well, that has to do with how we compute variance. To compute variance we do the following: 1. Subtract each score in the distribution from the mean score of the distribution 2. Square each of these values 3. Sum all of the squared values 4. Divide the sum of squared values by the total number of scores Variance Statistics When computing variance we need to square the values in step two so that they do not cancel one another out in step 3. For example, say that we have values that are +2, +3, and +4 points above the mean and values that are -2, -3, and -4 points below the mean. When we go to add these up without squaring them they will cancel each other out and we will end up with a value of zero. To ensure this doesn’t happen we square all of the values. Thus, 2, 3, and 4 become 4, 9, and 16 regardless of whether or not they were positive or negative values previously. This is so, as you may recall, because we square negative numbers to get rid of the negative sign/value. Thus, all of the values are positive and can be summed for a total. This sum in turn (known as the sum of squares) can be divided by the total number of observations. Variance Statistics So, in our example above we would add 4 + 9 + 16 + 4 + 9 + 16 = 58 Then we would divide 58 by 6 (the number of observations) to obtain the variance. In this case the variance is = 9.67. You can see that this last part of the process essentially involves the computation of a mean. Thus, it is helpful to think of variance as the mean or average of how scores disperse or spread out from the mean score. Variance is a helpful measure of dispersion, however it is of very limited use because it is no longer in the original units of measure. Rather, because of the computation necessary it ends up in squared units. Standard Deviation Statistics How then can we change variance into something usable and meaningful? That is, how do we return to the original units of measure? Well, we need to get rid of the squared scores. You may recall that we use the square root when we want to get rid of squared scores. The same is true here. We can take the square root of variance to calculate or compute a measure of dispersion that is in the original units of measure. This produces the standard deviation. Standard Deviation Statistics Standard deviation, like variance, is a measure of dispersion that explains how much scores in a set of interval/ratio data vary from the mean. However, unlike variance, it is expressed in the original units of measurement. So, say the variance is 9.67 as was the case in our earlier example… …the square root of 9.67 is 3.11. …the standard deviation therefore is 3.11. Standard Deviation Statistics Standard deviation helps us understand how a distribution spreads out. It is often reported alongside the mean score of a distribution. So, for example, we may see reports that list any of the following means and standard deviations: M = 12.56, SD = 2.45 M = 10.21, SD = 5.64 M = 28.45, SD = 8.45 An italicized M is the statistical notation for the mean and an italicized SD is the statistical notation for the standard deviation. From the reports for each distribution above we would know both the average score (M) and the average distance of all other scores in the distribution from that average score (SD). Standard Deviation Statistics When we see the M and SD reported, we can draw some conclusions about the distribution. What if we saw the following descriptive statistics reported for three different distributions of data? M = 14.56, SD = 2.45 M = 14.56, SD = 5.64 M = 14.56, SD = 8.45 The examples above all include the same mean to make a point about the standard deviation. In the first distribution the scores do not disperse widely, in the second they disperse moderately, and in the third they disperse considerably. Thus, the first distribution would appear as a tall and narrow curve, the second as a bell-shaped curve, and the third as a broad and comparatively flat curve. Providing Useful Feedback Activity Below are several possible findings that could be generated through a needs analysis. But they need to be converted into useable feedback before they can be applied. Read each of the findings and convert them into usable feedback (i.e., clarify findings and offer action plans related to the particular findings). Consult the example below carefully before completing the remainder of the activity. Use as much space as necessary to provide your responses. EXAMPLE Qualitative data revealed that numerous employees reported incidents in which they felt they were disciplined excessively when they made mistakes on the job. Clarification: Employees reported feeling like they were excessively reprimanded. Action Plan: Train mid-level managers in providing constructive rather than punitive employee feedback. ACTIVITY 1. Questionnaire data revealed that production workers’ scores on an index of organizational loyalty were lower than their counterparts in sales and engineering. Clarification: Action Plan: 2. Interview data collected from 15 customer service representatives revealed discrepancies about how employees felt the company expected them to deal with customer complaints. Clarification: Action Plan: 3. Questionnaire data revealed that sales agents’ scores on an index of organizational conflict indicated that they generally avoided conflict with their supervisors. Clarification: Action Plan: 4. Interview data collected from 25 engineers revealed that they believed it would damage their careers if they reported product defects to management. Clarification: Action Plan: 5. Observations of janitorial staff indicated that their friendly demeanor with the office personnel was not reciprocated. Clarification: Action Plan: Instruction: 1st Level Activities rely directly on your applying course content from the unit. They do not require you to do any additional work beyond applying the course content from the unit to the activity. PAPERS Note: The format for papers can be either APA, MLA or another standard format. Citations can be in the text or noted in notes or in a bibliography. Double spaced is preferred. Assume that approximately 250 words equals one page. You have to write at least 2 double space pages on the activity worksheet. Unit 5: DATA ANALYSIS We put a great deal of faith in scientific methods of data collection. Yet, the process can be riddled with errors. Before moving to how we should analyze data collected during an intervention it would be helpful to consider some common research errors. These include: • mistakes in recording and categorizing data • mistakes in sampling (perhaps drawing a small sample or one that is not representative of the larger workforce — for example, having the poorest 10% in terms of performance and designing a training program around the information they provide) • subject misrepresentation — or the possibility that people may provide inaccurate information • investigator bias that limits what one will find — only looking for and finding what we believe to be the problem • faulty instrumentation or the use of data collection techniques that are neither valid nor reliable Validity refers to measuring what you actually intend to measure, whereas reliability concerns measuring with consistency and accuracy. We can also experience errors when interpreting data. Common mistakes involve: • making too much out of limited data (drawing conclusions that may not be warranted) • making too many decisions based on limited data • ignoring important findings because of the commitment to established systems Effective diagnosis, then, is a function of two factors. Remaining objective and providing helpful feedback. Objectivity involves: • being aware of any biases in the data collected • questioning and confirming findings before drawing conclusions • looking beyond symptoms, to recognize actual problems • recognizing patterns that emerge • considering the uniqueness of each intervention • understanding how one’s presence can influence the data collection process Providing Feedback involves: • converting feedback into usable information for the client • clarifying findings and offering assistance in the interpretation of the data • translating findings into an action plans (solutions) for how to address the identified problems. To develop your skills in providing useful feedback and action plans, complete the Providing Useful Feedback Activity in this unit. There are many ways to analyze data collected depending on the type of data collection techniques used. Data can be numerical (quantitative) or textual (qualitative). Numerical/quantitative data derive from questionnaires or interviews and require statistical analysis. A review of basic statistical techniques is provided in the associated power points. You can find these under the supplemental materials for this unit. Descriptive Statistics Advanced Statistics Unit 5 Unit 5 Descriptive Statistics Advanced Statistics Textual/qualitative data comes from many sources, any which ask for people to report in their own words. This may include open-ended responses on questionnaires, interviews, focus groups, or organizational documents. Regardless of the source, textual/qualitative data require the use of textual analysis. More information about textual analysis is available in the associated power point, which can be found in the supplemental materials for this unit. Textual Analysis Unit 5 Textual Analysis Analyzing Results When reporting the results of a needs assessment it is important to include information about the data analysis, but not so much information that the findings are overshadowed by the analysis. Thus, it is important to streamline the information presented. Only provide the information that is essential. Just because you collected data does not mean you need to report about it necessarily. If it tells us little it will only complicate the process and can be left out of the final report. Or it can be part of a more detailed report when a shorter concise report is also made available. Final written reports should include: • a general overview • objectives and scope • methods of data collection • methods for data analysis • findings and conclusions • recommendations based on findings • expected benefits • implementation guidelines Unit 5: DATA ANALYSIS We put a great deal of faith in scientific methods of data collection. Yet, the process can be riddled with errors. Before moving to how we should analyze data collected during an intervention it would be helpful to consider some common research errors. These include: • mistakes in recording and categorizing data • mistakes in sampling (perhaps drawing a small sample or one that is not representative of the larger workforce — for example, having the poorest 10% in terms of performance and designing a training program around the information they provide) • subject misrepresentation — or the possibility that people may provide inaccurate information • investigator bias that limits what one will find — only looking for and finding what we believe to be the problem • faulty instrumentation or the use of data collection techniques that are neither valid nor reliable Validity refers to measuring what you actually intend to measure, whereas reliability concerns measuring with consistency and accuracy. We can also experience errors when interpreting data. Common mistakes involve: • making too much out of limited data (drawing conclusions that may not be warranted) • making too many decisions based on limited data • ignoring important findings because of the commitment to established systems Effective diagnosis, then, is a function of two factors. Remaining objective and providing helpful feedback. Objectivity involves: • being aware of any biases in the data collected • questioning and confirming findings before drawing conclusions • looking beyond symptoms, to recognize actual problems • recognizing patterns that emerge • considering the uniqueness of each intervention • understanding how one’s presence can influence the data collection process Providing Feedback involves: • converting feedback into usable information for the client • clarifying findings and offering assistance in the interpretation of the data • translating findings into an action plans (solutions) for how to address the identified problems. To develop your skills in providing useful feedback and action plans, complete the Providing Useful Feedback Activity in this unit. There are many ways to analyze data collected depending on the type of data collection techniques used. Data can be numerical (quantitative) or textual (qualitative). Numerical/quantitative data derive from questionnaires or interviews and require statistical analysis. A review of basic statistical techniques is provided in the associated power points. You can find these under the supplemental materials for this unit. Descriptive Statistics Advanced Statistics Unit 5 Unit 5 Descriptive Statistics Advanced Statistics Textual/qualitative data comes from many sources, any which ask for people to report in their own words. This may include open-ended responses on questionnaires, interviews, focus groups, or organizational documents. Regardless of the source, textual/qualitative data require the use of textual analysis. More information about textual analysis is available in the associated power point, which can be found in the supplemental materials for this unit. Textual Analysis Unit 5 Textual Analysis Analyzing Results When reporting the results of a needs assessment it is important to include information about the data analysis, but not so much information that the findings are overshadowed by the analysis. Thus, it is important to streamline the information presented. Only provide the information that is essential. Just because you collected data does not mean you need to report about it necessarily. If it tells us little it will only complicate the process and can be left out of the final report. Or it can be part of a more detailed report when a shorter concise report is also made available. Final written reports should include: • a general overview • objectives and scope • methods of data collection • methods for data analysis • findings and conclusions • recommendations based on findings • expected benefits • implementation guidelines Advanced Statistics Unit 5 There are several related topics in this unit… Types of Variables in Analysis Univariate and Multivariate Statistics Overview Univariate Statistics Multivariate Statistics Types of Variables in Analysis Statistics Independent Variables (IV) This is the variable thought to influence or cause a change in the value of another variable. For example, if you do not get enough sleep you will experience fatigue and drowsiness during work. Lack of sleep, then, is the independent variable thought to affect fatigue and drowsiness. Dependent Variables (DV) This is the variable that is thought to be changed or affected by another (independent) variable. Said another way, the value of the dependent variable is responsive to or determined by changes in the independent variable. In the example above fatigue and drowsiness are the variables affected. We will experience more fatigue and drowsiness if we have less sleep. Confounding Variables This is a variable that confounds, or confuses, the relationship between the independent and dependent variables. Or we can think of it this way…something other than the independent variable is accounting for changes in the dependent variable. For example, how engaging and interesting a meeting is (vs. boring) will affect whether or not you feel fatigue and drowsiness during the meeting. Thus, lack of sleep is not accounting for fatigue or drowsiness. Rather the nature of the meeting or a combination of lack of sleep and the nature of the meeting are causing fatigue and drowsiness. Univariate and Multivariate Statistics Overview Statistics We differentiate statistics as univariate or multivariate depending on the number of dependent variables involved in the statistical analysis. When there is a single dependent variable we use a univariate statistic. When there is more than one dependent variable we use a multivariate statistic. We also need to consider how both the dependent and independent variables were measured in order to determine what statistic is appropriate. Remember that we can measure numerically (interval and ratio level of measurement) or we can measure simply by differentiating between types (nominal level of measurement). Univariate Statistics Statistics There are two groups of univariate statistics we commonly use when we have a single numerical dependent variable. The first set are appropriate when we have a nominal/categorical independent variable. This would include statistics that compare categories or groups like men/women, highly satisfied/dissatisfied employees, youth/seniors, etc. These include… t-test ANOVA ANCOVA and Factorial Analysis of Variance Univariate Statistics Statistics We use the following statistics when we have a single numerical dependent variable and we want to make… t-test a simple comparison between two groups ANOVA (a one-way analysis of variance) a comparison between three or more groups ANCOVA a comparison between three or more groups while controlling for a confounding variable In all these cases we have only a single independent variable, which may be comprised of two, three, or more groups. However, when we have more than one independent variable we need to use a factorial analysis of variance. Factorial Analysis of Variance Statistics A factorial analysis of variance involves a comparison of scores on a single, numerical dependent variable — the value of which is determined by several nominal or categorical independent variables. Factorial analyses of variance are prefaced with a numerical string or statement that indicates: the number of independent variables (designated by the total number of numbers in the string, not the values of the numbers) and the number of levels of each independent variable (designated by the actual values of each number in the string) Factorial Analysis of Variance Statistics So for example, a 3x2x3 factorial analysis of variance has… 3 independent variables, the first with 3 levels, the second with 2 levels, and the third with 3 levels. Similarly, a 4x2 factorial analysis of variance has… two independent variables, the first with four levels and the second with two. This could be a comparison that examines student achievement (A, B, C, and D students) and sex (male, female). Univariate Statistics Statistics When we attempt to determine if variables are related and both the independent and dependent variables have been measured numerically we use one of the following univariate statistics… Correlation simply assessing the relationship between independent and dependent variables Regression assessing the ability of the independent variable to predict the value of the dependent variable Multiple Regression assessing the predictive ability of several independent variables on a single dependent variable Univariate Statistics Statistics The chart below helps to clarify how the common univariate statistical procedures relate and differ from one another. Being univariate all the statistics below have a single dependent variable that is numerical (measured at the interval or ratio level of measurement). t-test (2 groups) ANOVA (3+ groups) ANCOVA (while controlling) Factorial Analysis of Variance (with more than 1 IV) Correlation (relating) Regression (predicting) Multiple Regression (with more than 1 IV) The family of statistics in the left-hand column have nominal/categorical independent variables (abbreviated in the chart as IV) and therefore involve comparisons between groups. The family of statistics in the right-hand column have numerical independent variables and thus involve assessing relationships between variables (versus groups). Multivariate Statistics Statistics Multivariate statistics are appropriate when we have more than one dependent variable. It is helpful to think of them as an extension of the two previous groups discussed. When we compare groups and we have more than one dependent variable we move from an ANOVA to a… MANOVA compares groups in terms of more than one dependent variable Or from an ANCOVA to a… MANCOVA compares groups in terms of more than one dependent variable while controlling for a confounding variable Multivariate Statistics Statistics Similarly, we can move from a multiple regression (which considers how several numerical independent variables predict a single numerical dependent variable) to a… Canonical Correlation examines the relationship between multiple independent and multiple dependent variables all of which are numerical or, said another way, examines the relationship between a group of numerical independent and a group of numerical dependent variables Multivariate Statistics Statistics The chart below serves to clarify how the common multivariate statistical procedures relate and differ from one another. As multivariate statistics all of those listed below have multiple dependent variables (abbreviated as DV in the chart) that are numerical in nature. MANOVA (more than 1 DV) MANCOVA (while controlling) Canonical Correlation (comparing two sets of variables) As with the univariate families of statistics, the family of statistics in the left-hand column have nominal/categorical independent variables and therefore involve comparisons between groups. The family of statistics in the right-hand column have numerical independent variables and thus involve assessing relationships between variables (versus groups). Uni- and Multivariate Statistics Statistics Finally, the chart below puts both the univariate and multivariate statistics together. You can see then how the univariate statistics link to the multivariate statistics. Univariate Statistics (Single Dependent Variable) t-test (2 groups) Correlation (relating) ANOVA (3+ groups) Regression (predicting) ANCOVA (while controlling) Multiple Regression Factorial Analysis (with more than 1 IV) of Variance (with more than 1 IV) Multivariate Statistics (More Than One Dependent Variable) MANOVA (more than 1 DV) MANCOVA (while controlling) Canonical Correlation (comparing two sets of variables) Descriptive Statistics Unit 5 There are several related topics in this unit… Descriptive Statistics Overview Measures of Central Tendency Measures of Dispersion Descriptive Statistics Overview Statistics Descriptive Statistics tell us about specific trends in our data and describe specific features of our sample. For example, a researcher will use descriptive statistics to tell readers about the proportion of men and women who participated in a study. The research may write something like: “In this study 40% of the sample were men, whereas 60% were female.” Or the researcher may inform readers about participants’ average scores on a particular variable in the study. In this case the researcher may say: “The mean score on the communication competence measure was 14.55”. The primary descriptive statistics fall into one of two “families”: measures of central tendency measures of dispersion. Measures of Central Tendency Statistics Measures of central tendency, as the name infers, tell us about a central characteristic of the data. Measures of central tendency include… the Mode the Median and the Mean Mode Statistics The mode is the simplest measure of central tendency. It indicates which score or value in a distribution occurs most frequently. The mode is appropriate when we have nominal or categorical data. In these instances we are interested in how often each category was used or appeared. In essence we count observations that appear in each category and then report which category had the most observations. Thus, the mode is the descriptive statistic that tells us which category has the most observations or which category appears most often in the data. Mode Statistics Say, for example, that we are interested in people’s perceptions of what constitutes sexual harassment? To determine this we could provide people with a list of behaviors and ask them to respond by simply checking “yes” or “no” (nominal categories) if they believe the behavior reflects sexual harassment: 1. sexual comments 2. inappropriate gaze 3. sexual jokes 4. display of pornographic materials in the office 5. “pick-up” or “come on” lines ____ yes ____ yes ____ yes ____ yes ____ yes ____ no ____ no ____ no ____ no ____ no The mode will tell us which of these behaviors people perceived to be more sexually harassing compared to the others as it would reflect the category that had the most “yes” responses. Or if we were interested in the least sexually harassing behavior, we could count up the “no” responses and report the mode for “no” responses. Median Statistics The median divides a distribution of quantitative data exactly in half. It is the score above which and below which half the observations fall. The median is most appropriate for describing the center point of a set of ordinal data because it tells us the point at which half of the cases rank higher and half rank lower. For example, in a horse race the horse that finished fifth out of nine represents the median, as four horses finished before or above it and four horses finished behind or below it. Median Statistics The median also can be used with interval/ratio data, but can be problematic because it is not sensitive to extreme scores. That is, two distributions may have the same median or middle point, but one could include much higher and/or lower values than the other. Simply seeing the median would lead us to believe that the two distributions of scores are more similar than they actually are. For example: 4 10 10 11 12 13 15 15 28 17 36 21 47 25 The median for both distributions is 15, but the first distribution includes much lower and higher values (from 4 to 47) than the second one (from 10 to 25). Median Statistics Beyond simply describing the middle point of a distribution, researchers may use the median to create groups to compare. This is called a median split. When researchers have ordinal or ratio data but want to create groups or categories they can do so by using a median split to create two groups. Accordingly, the researcher determines what the median is for the variable of interest and then creates a “high” group with scores above the median and a “low group” with scores below the median. Median Statistics For example, a researcher interested in comparing people high and low in verbal aggressiveness can find the median of the verbal aggressiveness scores for all participants. She can then take all of the cases above that median to create the “high” group and all the scores below that median to create the “low” group. Then the researcher can compare people high and low in verbal aggressiveness on some other variable of interest. Do people high and low in verbal aggressiveness differ with regard to their marital satisfaction? Their communication competence? Mean Statistics The mean is the arithmetic average. It is computed by adding all the scores in a distribution and dividing by the total number of scores. It helps to clarify what the average score on a variable of interest is. For example, we may see any of the following reported. “The mean… “…for communication apprehension was 14.56.” “…for hours of television watched per week was 8.56.” “…for age of respondents in this study was 43.69 years.” Mean Statistics The mean is appropriate for interval/ratio data because of “assumed equivalence” or the idea that all points on the scale are assumed to be of equal distance from one another (i.e.., 1 is the same distance from two as two is from three and so on). Unlike the other types of central tendency descriptive statistics the mean is sensitive to all scores, including extreme scores, in the distribution. That is why it is thought to be the most sophisticated measure of central tendency. Measures of Dispersion Statistics Measures of dispersion show us how data spread out in a distribution. Think about, for example, dropping a glass of water and a can of motor oil on the floor. Both will spill and disperse (i.e., spread out), but they will do so very differently. Thus, measures of dispersion tell us about how data spread out across a distribution. They include… the Range Variance and Standard Deviation Range Statistics The range is the simplest measure of dispersion. It reports the distance between the highest and lowest scores in the distribution. The range, therefore, is calculated by subtracting the lowest number from the highest number in the distribution. The range gives a general sense of how much the data spread out across the distribution, which can be helpful for understanding whether a study included a lot of variability or whether it drew from a narrow spectrum. For example, if a researcher intends to study a communication variable across a wide range of age groups, a sample of people aged 18-21 (a range of 3) is not very diverse. Yet a sample of people aged 18-70 (a range of 52) is. Range Statistics One concern with the range is sensitivity to extreme scores. Because the range takes into account all scores in the distribution it can be misleading when “outlier” scores exist in a distribution. Outliers are scores that are far removed from the rest of the distribution. In the example of age just used you could have a distribution that ranges from 18-70, yet there is only one person aged 70 and the next closest score is actually 24. The age of 70 makes the distribution look much larger than it actually is once you take this outlier into account. If we exclude the outlier, which often researchers do when necessary, the range is actually 6 as the scores spread from 18-24, not 58 as is the case when the outlier is included and the scores spread from 18-70. To avoid problems with outliers researchers may report the interquartile range. This is the range of scores representing the middle 50% of scores (or the middle two quarters of the distribution). The upper and lower 25% of scores (the outer quarters where outliers will be) are excluded. An interquartile range provides a more conservative representation of the range. Variance Statistics Variance is the average distance of scores from the mean, in squared units. We can compute variance when we have interval or ratio data. Why squared units? Well, that has to do with how we compute variance. To compute variance we do the following: 1. Subtract each score in the distribution from the mean score of the distribution 2. Square each of these values 3. Sum all of the squared values 4. Divide the sum of squared values by the total number of scores Variance Statistics When computing variance we need to square the values in step two so that they do not cancel one another out in step 3. For example, say that we have values that are +2, +3, and +4 points above the mean and values that are -2, -3, and -4 points below the mean. When we go to add these up without squaring them they will cancel each other out and we will end up with a value of zero. To ensure this doesn’t happen we square all of the values. Thus, 2, 3, and 4 become 4, 9, and 16 regardless of whether or not they were positive or negative values previously. This is so, as you may recall, because we square negative numbers to get rid of the negative sign/value. Thus, all of the values are positive and can be summed for a total. This sum in turn (known as the sum of squares) can be divided by the total number of observations. Variance Statistics So, in our example above we would add 4 + 9 + 16 + 4 + 9 + 16 = 58 Then we would divide 58 by 6 (the number of observations) to obtain the variance. In this case the variance is = 9.67. You can see that this last part of the process essentially involves the computation of a mean. Thus, it is helpful to think of variance as the mean or average of how scores disperse or spread out from the mean score. Variance is a helpful measure of dispersion, however it is of very limited use because it is no longer in the original units of measure. Rather, because of the computation necessary it ends up in squared units. Standard Deviation Statistics How then can we change variance into something usable and meaningful? That is, how do we return to the original units of measure? Well, we need to get rid of the squared scores. You may recall that we use the square root when we want to get rid of squared scores. The same is true here. We can take the square root of variance to calculate or compute a measure of dispersion that is in the original units of measure. This produces the standard deviation. Standard Deviation Statistics Standard deviation, like variance, is a measure of dispersion that explains how much scores in a set of interval/ratio data vary from the mean. However, unlike variance, it is expressed in the original units of measurement. So, say the variance is 9.67 as was the case in our earlier example… …the square root of 9.67 is 3.11. …the standard deviation therefore is 3.11. Standard Deviation Statistics Standard deviation helps us understand how a distribution spreads out. It is often reported alongside the mean score of a distribution. So, for example, we may see reports that list any of the following means and standard deviations: M = 12.56, SD = 2.45 M = 10.21, SD = 5.64 M = 28.45, SD = 8.45 An italicized M is the statistical notation for the mean and an italicized SD is the statistical notation for the standard deviation. From the reports for each distribution above we would know both the average score (M) and the average distance of all other scores in the distribution from that average score (SD). Standard Deviation Statistics When we see the M and SD reported, we can draw some conclusions about the distribution. What if we saw the following descriptive statistics reported for three different distributions of data? M = 14.56, SD = 2.45 M = 14.56, SD = 5.64 M = 14.56, SD = 8.45 The examples above all include the same mean to make a point about the standard deviation. In the first distribution the scores do not disperse widely, in the second they disperse moderately, and in the third they disperse considerably. Thus, the first distribution would appear as a tall and narrow curve, the second as a bell-shaped curve, and the third as a broad and comparatively flat curve. Textual Analysis Unit 5 There are several related topics in this unit… What is Textual Analysis? Where do we find texts to examine? How do we do textual analysis? What are some concerns associated with conducting textual analysis? What is Textual Analysis? Textual Analysis The fundamental premise of textual analysis is that we can learn about communication by examining communication artifacts. Textual analysis is the methodology communication professionals use to analyze and interpret organizational artifacts. Where do we find texts to examine? Textual Analysis Communication artifacts or texts derive from one of two sources. An Existing Universe of Texts Organizational Texts exist naturally in many forms including, but not limited to… reports training manuals emails training videos corporate newsletters mission statements memos web pages advertisements Or the Creation of Texts through Another Methodology Texts can be created by asking people in an interview or a questionnaire to report about a communication phenomenon. Conducting a Textual Analysis Textual Analysis Textual Analysis is a multi-step process that involves… Selecting Texts Determining the Unit of Analysis Determining Categories Coding and Analyzing Data Selecting Texts Textual Analysis We begin textual analysis by first selecting a sample of texts from the existing universe of texts. A researcher must ensure that the texts selected are: Representative that the texts sampled are representative of all possible types of texts that exist within the universe of texts Sufficient that there are enough texts in the sample to draw meaningful conclusions Determining the Unit of Analysis Textual Analysis Once texts have been selected the researcher must determine what the unit of analysis will be. The unit of analysis can be a particular statement within the text, the entire text, or some specific feature of the text. For example, a researcher may be examining billboards on state highways as a set of texts. She is interested in how people see and understand the first line of text in billboards. Thus, she decides that she wants the first line of text on the billboards to be her unit of analysis. A different researcher may interested in the overall message of the billboard and therefore he decides that the unit of analysis would be all of the text on the billboard, not just the first line of text. A third researcher may be interested in the graphic images that appear on billboards. She decides to make these the unit of analysis. Thus we can see how the same set of texts (in this case billboards on state highways) can be analyzed differently depending on the unit of analysis designated by the researcher. Determining the Unit of Analysis Textual Analysis Once the unit of analysis has been determined the researcher must begin “unitizing” the texts. Unitizing is the process of identifying the units of analysis within the texts to be examined. Determining the Unit of Analysis Textual Analysis For the researchers studying billboards unitizing would involve the following: identify where the first line of text begins and ends identify what constitutes the entire text of the billboard identify what graphic images would be signaled out for study. In each case the researcher sets the parameters for determining what constitutes a unit of analysis in the given set of texts. Then the researcher decides how many units of analysis there actually are in the data set. In the fist two cases the number of units of analysis would match the number of billboards included in the sample of texts. However, in the case of graphic images on billboards there could in fact be many more units of analysis than there are billboards because individual billboards may have several graphics. Determining Categories Textual Analysis Next the researcher must determine what the categories used in the textual analysis will be. Categories can be drawn from one of two sources: Theory and/or Previous Literature Previous research and theory will suggest to us what categories are relevant for a given phenomenon. For example, when analyzing diaries cataloguing employees’ emotional exchanges at work we could rely upon the standard 6 category list of emotional prototypes, which includes happiness, sadness, joy, affection, surprise, and anger. Determining Categories Textual Analysis Or the researcher can look to the actual data for categories. The Current Data (Grounded Theory) Via a type of analysis known as grounded theory we can examine the data with the intention of allowing the categories to emerge naturally from the data. For instance we could identify several themes that appear in fans’ web blogs posted on their favorite athletes’ websites. By reviewing these texts we could find some emergent themes to provide a sense of what types of messages fans post and why they post such messages. Determining Categories Textual Analysis Categories derived from previous literature and/or theory must be: Mutually exclusive refers to the idea that the categories are independent and separate from one another. It is a fancy way of saying that they should not overlap. Exhaustive means that the categories exhaust all of the possible categories that should be used. Equivalent means the categories are measuring or getting at the same idea. Coding Textual Analysis Once we have selected texts, unitized them, and decided on categories we can begin coding. Coding is the process by which we place the units of analysis into the categories we’ve decided to use. We do this by reviewing each unit of analysis and placing it in one of the predetermined categories. We cannot be certain that all people would code the same data the same way. Therefore we use multiple coders to do the coding. Coding Textual Analysis The coding process involves: Training Coders Assessing Intercoder Reliability and Achieving Consensus Training Coders Textual Analysis To train coders we need to first introduce them to the category scheme to be used, reviewing carefully what does and does not belong in each category. Then we should have coders practice coding with a set of texts that are not part of the data to be analyzed. If we recognize any problems with the coding scheme at this point or how the coders are using it we need to make the necessary corrections before beginning our analysis of the actual data. Assessing Intercoder Reliability Textual Analysis As we noted earlier, we should and usually do use more than one coder. To determine the degree to which multiple coders have categorized the data similarly we need to compute what is called intercoder reliability. We determine intercoder reliability to assess the degree to which coders used the category scheme similarly and placed units of analysis into categories accordingly. There are several ways to compute intercoder reliability including: Percentage Agreement Cohen’s Kappa and Scott’s π Assessing Intercoder Reliability Textual Analysis Percentage agreement simply involves computing the number of times coders agreed out of the number of total times they could have agreed. The problem with percentage agreement is that it includes something called chance agreement (or the possibility that coders agreed by chance and not intentionally). Therefore percentage agreement is thought of as an “inflated” measure of intercoder reliability. In contrast Cohen’s Kappa and Scott’s π use a mathematical computation that factors out chance agreement and they are therefore considered more “conservative” estimates of intercoder reliability. Achieving Consensus Textual Analysis Because coders will not agree all of the time there will be unresolved cases where disagreement has occurred. That is, cases in which coders believe the same observation belongs in two different categories. We do not want to exclude these cases. Rather we need to train the coders to reach consensus. Coders can achieve consensus by talking about the cases in which originally there was disagreement until they can come to some consensus about where the observation belongs. In research reports we usually see the practice described in a statement like “Coders discussed cases in which disagreement occurred until consensus was reached and all observations were categorized.” Analyzing Data Textual Analysis Once the coding procedure has been completed we are ready to analyze the results. Results can be analyzed either qualitatively or quantitatively. Qualitative analysis simply involves examining the texts for themes and describing the themes accordingly. Quantitative analysis involves counting the number of observations in each category and then comparing those amounts using the X2 (chi square) statistic. This statistic tells us whether or not differences in amounts are robust enough to have happened beyond chance. Concerns with Textual Analysis Textual Analysis When conducting textual analysis we have to be attentive to any limitations in the universe of texts. There are two particular limitations to which we should attend: Selective deposit refers to the idea that not all texts would have been retained or archived. For example, there are many more gospels than just those that were included in the New Testament. Selective survival refers to the idea that only some texts have survived from a larger universe of texts. For example, how many of the speeches of this country’s founding fathers have survived? Certainly nowhere near as many as they gave.
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Explanation & Answer

Attached.

Running head: UNIT 5

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Unit 5: Providing Useful Feedback Activity
Name
Institution

UNIT 5

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Unit 5: Providing Useful Feedback Activity

1. Questionnaire data revealed that production workers’ scores on an index of
organizational loyalty were lower than their counterparts in sales and engineering.
Clarification:
The study has shown that workers in the production department have relatively lower
levels of loyalty than those in sales and engineering. This is mostly due to the lack of motivation
and job satisfaction.
Action Plan:
The lack of motivation is attributed to factors that affect the production workers
adversely more than the sales and engineering workers. According to Börsch-Supan & Weiss
(2016), production line workers can be motivated by increasing the value of their work. This
includes embracing the employees’ productivity and quality of work as well as implementing
motivation strategies that not only target the earning equality but also improve the workplace
conditions. The company should research more into the causes of lack of loyalty among
production workers and implement the relevant interventions to improve loyalty. These could be
interventions such as providing a work/life balance and implementing tokens to improve
employee satisfaction.
2. ...


Anonymous
Excellent resource! Really helped me get the gist of things.

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