Course: Research Methodology-O192IBasic Data AnalysisWeek 8-9

TopicsBina Nusantara University*1. Introduction to Research Methodology2. Problem Definition : The Foundation of Business Research3. Qualitative Research Tools4. Survey Research5. Measurement and Scaling Concepts6. Sampling Designs and Sampling Procedures7. Basic Data Analysis8. Communicating Research Results

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Evaluating and GradingBina Nusantara University*

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Learning OutcomeLO 1: Describe the basics of research methodology and the research reportLO 2: Choose appropriate sampling and research designLO 3: Construct questionnaires, measurement and scaling, and research proposalLO 4: Interpret the results of statistics calculation

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REFERENCESZikmund, Babin, Carr, and Griffin. (2009). Business Research Methods. 8th edition. Thomson South-Western. ISBN : 978-0324320626.Bina Nusantara University*

The Nature of Descriptive AnalysisDescriptive analysisThe elementary transformation of raw data in a way that describes the basic characteristics such as central tendency, distribution, and variability.Bina Nusantara University*HistogramA graphical way of showing a frequency distribution in which the eight of a bar corresponds to the observed frequency of the category.(1) Descriptive Statistics

Bina Nusantara University*Levels of Scale Measurement and Suggested Descriptive Statistics

TabulationBina Nusantara University*1

TabulationThe orderly arrangement of data in a table or other summary format showing the number of responses to each response category; TallyingFrequency tableA table showing the different ways respondents answered a question.

TabulationBina Nusantara University*

Cross-TabulationBina Nusantara University*Cross-TabulationThe appropriate technique for addressing research questions involving relationships among multiple less-than interval variables; results in a combined frequency table displaying one variable in rows and another in columns.

Contingency TablesBina Nusantara University*Contingency tableA data matrix that displays the frequency of some combination of possible responses to multiple variables; cross-tabulation results.MarginalsRow and column totals in a contingency table, which are shown in its margins.2

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Statistical baseThe number of respondents or observations (in a row or column) used as a basis for computing percentages.Elaboration analysisAn analysis of the basic cross tabulation for each level of a variable not previously considered, such as subgroups of the sample.Moderator variableA third variable that changes the nature of a relationship between the original independent and dependent variables.Bina Nusantara University*Contingency Tables

How Many Cross-Tabulations?Quadrant analysisAn extension of cross-tabulation in which responses to two rating scale questions are plotted in four quadrants of a two dimensional table.Importance-performance analysisAnother name for quadrant analysis.Bina Nusantara University*

How Many Cross-Tabulations?Bina Nusantara University*

Data TransformationBina Nusantara University*Data transformation

Process of changing the data from their original form to a format suitable for performing a data analysis addressing research objectives.

Data TransformationBina Nusantara University*2

Problems with Data TransformationsBina Nusantara University*Median splitDividing a data set into two categories by placing respondents below the median in one category and respondents above the median in another.3

Index NumbersBina Nusantara University*Scores or observations recalibrated to indicate how they relate to a base number.

Index NumbersBina Nusantara University*

Calculating Rank OrderBina Nusantara University*

Calculating Rank OrderBina Nusantara University*2

Tabular and Graphic Methodsof Displaying DataBina Nusantara University*

QuestionsBina Nusantara University*What are five descriptive statistics used to describe the basic properties of variables?What is a histogram? What is the advantage of overlaying a normal distribution over a histogram?A survey asks respondents to respond to the statement My work is interesting. Interpret the frequency distribution shown here (taken from an SPSS output):

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Bina Nusantara University*Empirical testing typically involves inferential statistics. This means that an inference will be drawn about some population based on observations of a sample representing that population.

Statistical analysis can be divided into several groups:Univariate statistical analysis tests hypotheses involving only one variable.Bivariate statistical analysis tests hypotheses involving two variables.Multivariate statistical analysis tests hypotheses and models involving multiple (three or more) variables or sets of variables.(2) Univariate and Bivariate Statistical Analysis

Hypothesis TestingBina Nusantara University*Descriptive research and causal research designs often climax with hypothesis tests.Hypotheses are defined as formal statements of explanations stated in a testable form. Generally, hypotheses should be stated in concrete fashion so that the method of empirical testing seems almost obvious.

The Hypothesis-Testing ProcedureBina Nusantara University*Hypotheses are tested by comparing the researchers educated guess with empirical reality. The process can be described as follows:First, the hypothesis is derived from the research objectives. The hypothesis should be stated as specifically as possible.Next, a sample is obtained and the relevant variable is measured.The measured value obtained in the sample is compared to the value either stated explicitly or implied in the hypothesis. If the value is consistent with the hypothesis, the hypothesis is supported. If the value is not consistent with the hypothesis, the hypothesis is not supported.

Significance Levels and p-ValuesBina Nusantara University*Significance levelA critical probability associated with a statistical hypothesis test that indicates how likely an inference supporting a difference between an observed value and some statistical expectation is true. The acceptable level of Type I error.p-valueProbability value, or the observed or computed significance level; p-values are compared to significance levels to test hypotheses.

Choosing the Appropriate Statistical TechniqueBina Nusantara University*Numerous statistical techniques are available to assist the researcher in interpreting data. Choosing the right tool for the job is just as important to the researcher as to the mechanic. Making the correct choice can be determined by consideringThe type of question to be answeredThe number of variables involvedThe level of scale measurement

Parametric versus Nonparametric Hypothesis TestsBina Nusantara University*Parametric statisticsInvolve numbers with known, continuous distributions; when the data are interval or ratio scaled and the sample size is large, parametric statistical procedures are appropriate.Nonparametric statisticsAppropriate when the variables being analyzed do not conform to any known or continuous distribution.

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The t-DistributionBina Nusantara University*t-testA hypothesis test that uses the t-distribution. A univariate t-test is appropriate when the variable being analyzed is interval or ratio.Degrees of freedom (df )The number of observations minus the number of constraints or assumptions needed to calculate a statistical term.t-distributionA symmetrical, bell-shaped distributionthat is contingent on sample size; has a mean of 0 and a standard deviation equal to 1.

The Chi-Square Test for Goodness of FitBina Nusantara University*Chi-square testOne of the basic tests for statistical significance that is particularly appropriate for testing hypotheses about frequencies arranged in a frequency or contingency table.Goodness-of-fit (GOF)A general term representing how well some computed table or matrix of values matches some population or predetermined table or matrix of the same size.

QuestionsBina Nusantara University*What is the purpose of a statistical hypothesis?What is a significance level? How does a researcher choose a significance level?What is the difference between a significance level and a p-value?How is a p-value used to test a hypothesis?Distinguish between a Type I and Type II error.

The t-Test for Comparing Two MeansBina Nusantara University*Independent samples t-testA test for hypotheses stating that the mean scores for some interval- or ratio-scaled variable grouped based on some less-than interval classificatory variable.

The t-Test for Comparing Two MeansBina Nusantara University*Pooled estimate of the standard errorAn estimate of the standard error for a t-test of independent means that assumes the variances of both groups are equal.Paired-samples t-testAn appropriate test for comparing the scores of two interval variables drawn from related populations.

The Z-Test for Comparing Two ProportionsBina Nusantara University*Z-test for differences of proportionsA technique used to test the hypothesis that proportions are significantly different for two independent samples or groups.

Analysis of Variance (ANOVA)Bina Nusantara University*Analysis of Variance (ANOVA)Analysis involving the investigation of the effects of one treatment variable on an interval scaled dependent variable-a hypothesis-testing technique to determine whether statistically significant differences in means occur between two or more groups.

QuestionsBina Nusantara University*

QuestionsBina Nusantara University*

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