Descriptive Analysis


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1 Research Methods William G. Zikmund Basic Data Analysis: Descriptive Statistics Descriptive Analysis The transformation of raw data into a form that will make them easy to understand and interpret; rearranging, ordering, and manipulating data to generate descriptive information
2 Type of Measurement Type of descriptive analysis Nominal Two categories More than two categories Frequency table Proportion (percentage) Frequency table Category proportions (percentages) Mode Type of Measurement Type of descriptive analysis Ordinal Rank order Median
3 Type of Measurement Type of descriptive analysis Interval Arithmetic mean Type of Measurement Type of descriptive analysis Ratio Index numbers Geometric mean Harmonic mean
4 Tabulation Tabulation  Orderly arrangement of data in a table or other summary format Frequency table Percentages Frequency Table The arrangement of statistical data in a rowandcolumn format that exhibits the count of responses or observations for each category assigned to a variable
5 Central Tendency Measure of Central Measure of Type of Scale Tendency Dispersion Nominal Mode None Ordinal Median Percentile Interval or ratio Mean Standard deviation CrossTabulation A technique for organizing data by groups, categories, or classes, thus facilitating comparisons; a joint frequency distribution of observations on two or more sets of variables Contingency table The results of a crosstabulation of two variables, such as survey questions
6 CrossTabulation Analyze data by groups or categories Compare differences Contingency table Percentage crosstabulations Base The number of respondents or observations (in a row or column) used as a basis for computing percentages
7 Elaboration and Refinement Moderator variable A third variable that, when introduced into an analysis, alters or has a contingent effect on the relationship between an independent variable and a dependent variable. Spurious relationship An apparent relationship between two variables that is not authentic. Quadrant Analysis Two rating scales 4 quadrants twodimensional table Importance Performance Analysis)
8 Data Transformation Data conversion Changing the original form of the data to a new format More appropriate data analysis New variables Data Transformation Summative Score VAR1 + VAR2 + VAR 3
9 Collapsing a FivePoint Scale Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagree Strongly Agree/Agree Neither Agree nor Disagree Disagree/Strongly Disagree Index Numbers Score or observation recalibrated to indicate how it relates to a base number CPI  Consumer Price Index
10 Calculating Rank Order Ordinal data Brand preferences Tables Bannerheads for columns Studheads for rows
11 Pie charts Line graphs Bar charts Vertical Horizontal Charts and Graphs
12 Line Graph Bar Graph st Qtr 2nd Qtr 3rd Qtr 4th Qtr East West North
13 WebSurveyor Bar Chart How did you find your last job? Temporary agency 1.5 % 643 Networking 213 print ad 179 Online recruitment site 112 Placement firm 18 Temporary agency Placement firm 9.6 % Online recruitment site 15.4 % print ad 18.3 % Networking 55.2 % SPSS SAS SYSTAT Microsoft Excel WebSurveyor Computer Programs
14 Microsoft Excel Data Analysis The Paste Function Provides Numerous Statistical Operations
15 Computer Programs Box and whisker plots Interquartile range  midspread Outlier
16 Interpretation The process of making pertinent inferences and drawing conclusions concerning the meaning and implications of a research investigation Research Methods William G. Zikmund Univariate Statistics
17 Univariate Statistics Test of statistical significance Hypothesis testing one variable at a time Hypothesis Unproven proposition Supposition that tentatively explains certain facts or phenomena Assumption about nature of the world
18 Hypothesis An unproven proposition or supposition that tentatively explains certain facts or phenomena Null hypothesis Alternative hypothesis Null Hypothesis Statement about the status quo No difference
19 Alternative Hypothesis Statement that indicates the opposite of the null hypothesis Significance Level Critical probability in choosing between the null hypothesis and the alternative hypothesis
20 Significance Level Critical Probability Confidence Level Alpha Probability Level selected is typically.05 or.01 Too low to warrant support for the null hypothesis The null hypothesis that the mean is equal to 3.0: H o : µ 3.0
21 The alternative hypothesis that the mean does not equal to 3.0: H : µ A Sampling Distribution µ3.0 x
22 A Sampling Distribution α.025 α.025 µ3.0 x A Sampling Distribution LOWER LIMIT µ3.0 UPPER LIMIT
23 Critical values of µ Critical value  upper limit µ + ZS X or µ + Z S n Critical values of µ ( 0.1).196
24 Critical values of µ Critical value  lower limit µ  ZS X or µ Z S n Critical values of µ ( 0.1)
25 Region of Rejection LOWER LIMIT µ3.0 UPPER LIMIT Hypothesis Test µ µ
26 Type I and Type II Errors Accept null Reject null Null is true Correct no error Type I error Null is false Type II error Correct no error Type I and Type II Errors in Hypothesis Testing State of Null Hypothesis Decision in the Population Accept Ho Reject Ho Ho is true Correctno error Type I error Ho is false Type II error Correctno error
27 Calculating Z obs x z obs sx Alternate Way of Testing the Hypothesis X Z obs S X
28 Alternate Way of Testing the Hypothesis Z obs µ 3.78 S. 1 X Choosing the Appropriate Statistical Technique Type of question to be answered Number of variables Univariate Bivariate Multivariate Scale of measurement
29 PARAMETRIC STATISTICS NONPARAMETRIC STATISTICS tdistribution Symmetrical, bellshaped distribution Mean of zero and a unit standard deviation Shape influenced by degrees of freedom
30 Degrees of Freedom Abbreviated d.f. Number of observations Number of constraints Confidence Interval Estimate Using the tdistribution or µ X ±. l. t c S X Upper limit X + Lower limit X t t c. l. c. l. S n S n
31 Confidence Interval Estimate Using the tdistribution µ X t c.l. S X S n population mean sample mean critical value of t at a specified confidence level standard error of the mean sample standard deviation sample size Confidence Interval Estimate Using the tdistribution µ X S n 17 X ± t cl s x
32 upper limit ( ) 5.07 Lower limit ( ) 2.33
33 Hypothesis Test Using the tdistribution Univariate Hypothesis Test Utilizing the tdistribution Suppose that a production manager believes the average number of defective assemblies each day to be 20. The factory records the number of defective assemblies for each of the 25 days it was opened in a given month. The mean X was calculated to be 22, and the standard deviation, S,to be 5.
34 H 0 : µ 20 H 1 : µ 20 S X S n
35 Univariate Hypothesis Test Utilizing the tdistribution The researcher desired a 95 percent confidence, and the significance level becomes.05.the researcher must then find the upper and lower limits of the confidence interval to determine the region of rejection. Thus, the value of t is needed. For 24 degrees of freedom (n1, 251), the tvalue is Lower limit: µ t c S / ( 1) l. X 25
36 Upper limit: µ + t c. l. S + X / ( ) Univariate Hypothesis Test ttest t obs X µ S X
37 Testing a Hypothesis about a Distribution ChiSquare test Test for significance in the analysis of frequency distributions Compare observed frequencies with expected frequencies Goodness of Fit ChiSquare Test x O i E i E i
38 ChiSquare Test x² chisquare statistics O i observed frequency in the i th cell E i expected frequency on the i th cell ChiSquare Test Estimation for Expected Number for Each Cell ij i j
39 ChiSquare Test Estimation for Expected Number for Each Cell R i total observed frequency in the i th row C j total observed frequency in the j th column n sample size Univariate Hypothesis Test Chisquare Example X 2 ( ) 2 O E ( O E ) 1 E E 2 2 2
40 Univariate Hypothesis Test Chisquare Example X ( ) ( 40 50) Hypothesis Test of a Proportion π is the population proportion p is the sample proportion π is estimated with p
41 Hypothesis Test of a Proportion H 0 : π. 5 H 1 : π ¹. 5 S p ( 0.6)( 0.4)
42 Zobs p π. 6.5 S p Hypothesis Test of a Proportion: Another Example n 1,200 p.20 S p S p S p S p pq n (.2)(.8) S p.0115
43 Hypothesis Test of a Proportion: Another Example n 1,200 p.20 S p S p S p S p pq n (.2)(.8) S p.0115 p π Z S Hypothesis Test of a Proportion: Another Example p Z Z.0115 Z The Z value exceeds 1.96,so the null hypothesis should be rejected at Indeed it is significant beyond the.001 the.05 level.
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