Data Analysis Using Spss T Test

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The t-test is used to determine if there are significant differences between the means of two groups. An independent-samples t-test was conducted to compare the affective commitment, continuance commitment, and normative commitment of male and female employees. The t-test results showed a significant difference in affective commitment between males (M=3.49720) and females (M=3.38016), but no significant differences in continuance commitment or normative commitment between the two groups.

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The main objective of project is study factors influencing women entrepreneurs in Tiruchirappalli district, analyzing the socio economic cultural factors, government policy factors, family status factors, personal characteristic factors, financial factors, motivational factors, market and network factors. In this study researcher tried to identify the factors influencing women entrepreneurs, so that areas of improvement can be identified and necessary steps can be given for implementation. The research design adopted in this study comes under descriptive design. A descriptive study is undertaken in order to ascertain and be able to describe the characteristics of factors influencing women entrepreneurs in Trichy district. The sampling method used for this study is simple random sampling. In this study the researcher has adopted, independent sample t-test, multivariate analysis, correlation, regression, factor analysis comes under the data analysis. The study provides a comprehensive review of the 26 critical factors influencing the growth of women entrepreneurs particularly in Trichy as the factors are derived from the global literature on women entrepreneurship. These would aid in better positioning the significance of these critical factors towards the success of the entrepreneurs in Trichy and in general.

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Week 2 – Lecture 3 Making judgements about differences between group statistics is one of the most powerful things that statistics can do for us. It is also one of the most counter-intuitive things that we need to master in the class. Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture 2 examined how to set up, perform, and interpret the F test for variance equality. This lecture will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio variable, while the homework should use the Salary variable. The T-Test While we test for variance equality with an F test, we use the T-Test to test for mean equality testing. The t-test also uses the degree of freedom (df) value in providing us with our probability result; but again, Excel does the work for us. There are three versions of the T-Test done for us by Excel. The first two are similar except one version is done if the variances are equal and the other if the variances are not equal. (Now we see the second reason for performing the F-test first.) The third version of the T-test is for paired data, and is called T-test Paired Two Sample for Means. Paired data are two measures taken on the same subject. Examples include a math and English test score for students, preference for different drinks, and, in our data set the salary and midpoint values. Note that paired data must be measured in the same units, and be from the same subjects. Students in the past have incorrectly used the paired t-test on male and female salaries. These are not paired, as the measures are taken on different people and cannot be paired together for analysis. In many ways, setting up Excel’s T-tests, and virtually all the functions we will study, follow the same steps as we just went through: 1. Set up the data into distinct groups. 2. Select the test function from either the Fx or Analysis list 3. Input the data ranges and output ranges into the appropriate entry boxes, checking Labels if appropriate. 4. Clicking on OK to produce the output. As with the F-test, the T-test has a couple of options depending upon what you want your output to look like. The Fx (or Formulas) option returns simply the p-value for the selected version of the test. The Data | Analysis selection provides descriptive statistics that are useful for additional analysis (some of which we will discuss later in the course). The t-test requires that we select between three versions, one assuming equal variances between the populations, one assuming unequal variances in the populations, and one requiring paired data (two measures on each element in the sample, such as salary and midpoint for each person in our data set.) All have the same data set-up approach, so only one will be shown. Question 2 The second question for this week asks about salary mean equality between males and females. The data and test se.

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DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .

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Homework Computing a t-test using AGE as the independent variable and CSSE (Current statistics self-efficacy) as the dependent measure. We know from research that age 37 is a good cutpoint for our AGE variable. Use tables below to answer the questions. Note: ¨ Levenes equality of variance tests: a significance value larger than 0.05 is good. This means the variances for groups DO NOT differ from each other. What is Levenes equality of variance tests significance? What is the value of t when variances are equal? What about unequal? Is either value of t significant? T-Test Group Statistics Age N Mean Std. Deviation Std. Error Mean Current Stats Self-Efficacy >= 37 45 6.44 2.833 .422 < 37 68 6.47 2.391 .290 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Current Stats Self-Efficacy Equal variances assumed 1.782 .185 -.053 111 .958 -.026 .495 -1.007 .954 Equal variances not assumed -.051 83.123 .959 -.026 .512 -1.045 .993 Homework Computing a t-test using AGE as the independent variable and CSSE (Current statistics self-efficacy) as the dependent measure. We know from research that age 37 is a good cutpoint for our AGE variable. Use tables below to answer the questions. Note: ¨ Levenes equality of variance tests: a significance value larger than 0.05 is good. This means the variances for groups DO NOT differ from each other. What is Levenes equality of variance tests significance? What is the value of t when variances are equal? What about unequal? Is either value of t significant? T-Test Group Statistics Age N Mean Std. Deviation Std. Error Mean Current Stats Self-Efficacy >= 37 45 6.44 2.833 .422 < 37 68 6.47 2.391 .290 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Current Stats Self-Efficacy Equal variances assumed 1.782 .185 -.053 111 .958 -.026 .495 -1.007 .954 Equal variances not assumed -.051 83.123 .959 -.026 .512 -1.045 .993 ...

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DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .

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1 Running head: RESEARCH PROJECT PROPOSAL 1 3 RESEARCH PROJECT PROPOSAL Research Project Proposal Name Here South University Online Research Project Proposal Provide a brief, general introduction to the topic and importance to your role (do not use first person anywhere in the paper). Conclude with a thesis statement. Background and Significance of the Problem Discuss here. Statement of the Problem and Purpose of the Study Discuss here. Literature Review Discuss here. Research Questions, Hypothesis, and Variables Discuss here. Theoretical Framework Overview and Guiding Propositions Discuss here. Application of Theory to Study Focus Discuss here. Methodology Sample/Setting Discuss here. Sampling Strategy Discuss here. Research Design Discuss here. Extraneous Variables Discuss here. Instruments Discuss here. Description of the Intervention Discuss here. Data Collection Procedures Discuss here. Data Analysis Plans Discuss here. Ethical Issues Discuss here. Limitations of Proposed Study Discuss here. Implications for Practice Discuss here. References Appendices Lecture 6 (Additional information on t-tests and hypothesis testing) Lecture 5 focused on perhaps the most common of the t-tests, the two sample assuming equal variance. There are other versions as well; Excel lists two others, the two sample assuming unequal variance and the paired t-test. We will end with some comments about rejecting the null hypothesis. Choosing between the t-test options As the names imply each of the three forms of the t-test deal with different types of data sets. The simplest distinction is between the equal and unequal variance tests. Both require that the data be at least interval in nature, come from a normally distributed population, and be independent of each other – that is, collected from different subjects. The F-test for variance. To determine if the population variances of two groups are statistically equal – in order to correctly choose the equal variance version of the t-test – we use the F statistic, which is calculated by dividing one variance by the other variance. If the outcome is less than 1.0, the rejection region is in the left tail; if the value is greater than 1.0, the rejection region is in the right tail. In either case, Excel provides the information we need. To perform a hypothesis test for variance equality we use Excel’s F-Test Two-Sample for Variances found in the Data Analysis section under the Data tab. The test set-up is very similar to that of the t-test, entering data ranges, checking Labels box if they are included in the data ranges, and identifying the start of the output range. The only unique element in this test is the identification of our alpha level. Since we are testing for equality of variances, we have a two sample test and the rejection region is again in both tails. This means that our rejection region in each tail is 0.25. The F-test id ...

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MARKETING MANAGEMENT PHILOSOPHIES CHAPTER 1 - ASSIGNMENT Question 1. Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions. In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies. Be specific with your examples. DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variabl ...

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