Statistical package for the extension science research by vinay

Dept of Agril. Extension 129/11/2014

Dept of Agril. Extension 2

III Seminar on

Statistical Package for the extension science

research

By-Vinaya Kumar, H. M.

Ph.D. scholar

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3

Introduction


Available Statistical PackagesProprietary Excel SPSS MINITAB SAS

Free Software LibreOffice Calc PSPP EpiInfo R

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Microsoft Excel

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Minitab

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SAS

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LibreOffice Calc29/11/2014


EpiInfo

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PSPP29/11/2014 10Dept of Agril. Extension


R

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What is Used? (Academia)

29/11/2014Figure . Use of data analysis software in academic publications as measured by hits on Google Scholar.


What is Used? (Job Market)

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• To understand the SPSS and its related concepts

• To get an insight about the procedure and steps involved in SPSS software

• To review research studies related to SPSS (Non Parametric tests)

Objectives

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History

• Nie, Bent & Hull - 1970 • SPSS- later statistical product and service

solutions • 2009- IBM (PASW)• Now – IBM SPSS statistics • Resent one IBM SPSS statistics-22.0 version

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What is SPSS?

• Well suited for survey and experimental research• Data analysis in three basic ways Very expensive

• Four main stages – Defining Variables– Entering Data– Analyzing Data– SPSS Output

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Rules for Defining Variable Names• The name must begin with a letter.

• Maximum of 8 characters and no spaces.

• Names must be unique.

• @ # _ or $ allowed.

• A full stop can be used but not as the last character, so best avoided.

• The space character and others such as * ! ? And ‘ are not allowed.

• Names are not case sensitive so ID, id and Id are identical.

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Rules for Defining Variables• Certain SPSS keywords are no allowed as variable names they

are:

ALL TO WITH BY ANDOR NOT EQ NE LELT GE GT

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Step 1 – Enter variables in Variable View

Variable Name Default is var00001e.g. “VO2max”, “Grade”

Variable Type

Value Labels

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Before to it we should have a basic knowledge about SPSS software

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Entering Data


SPSS Data Editor

• Variables = Columns• Cases = Rows• Cell = Intersection of Variable & Case

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Variable View• Variable View with a data set already loaded in SPSS:

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Entering Data• File | Open an existing SPSS data document (.sav)

Or

• Manually Enter Data:

1. Define Variables in Variable View2. Enter data in Data view

Or

• Read Data in from Excel

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Output Viewer• Where results of statistical analysis performed via analyze are

displayed (will open automatically when analysis is performed).

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• To get an insight about the procedure and steps involved in SPSS software

Chi Square test Mann-Whitney U test Wilcoxon signed-rank test Spearman rank-order correlation Cochran's Q test

II Objective

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Dept of Agril. Extension

Parametric vs Non-parametric

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CHI SQUARE TEST

• First used by Karl Pearson

• Calculated using the formula- χ2 = ∑ ( O – E )2/ E

(O = observed frequencies E = expected frequencies) •

Karl Pearson (1857–1936)

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Mann-Whitney U test

• Mann-Whitney U – similar to Wilcoxon signed-ranks test except that the samples are independent and not paired.

• Used to analyse the difference between the medians of two data sets.

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where n1 and n2 is the sample size, and R1 is the sum of the ranks

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Wilcoxon signed-rank test

• Nonparametric equivalent of the paired t-test.

• used when comparing two related samples,

• Similar to sign test• The statistic T is found by calculating the

sum of the positive ranks, and the sum of the negative ranks.

Frank Wilcoxon (1892–1965)

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http://en.wikipedia.org/wiki/Frank_Wilcoxon

http://en.wikipedia.org/wiki/Frank_Wilcoxon

Dept of Agril. Extension 3129/11/2014


spearman rank-order correlation

• Use to assess the relationship between two ordinal variables or two skewed continuous variables.

• Nonparametric equivalent of the Pearson correlation.

• It is a relative measure which varies from -1 to +1

Charles Spearman (1863–1945)

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• where , is the difference between ranks

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Cochran's Q test

• Cochran's Q test is a non-parametric statistical test to verify if k treatments have identical effects

It is named for William Gemmell Cochran.Cochran's Q test should not be confused with

Cochran's C test,

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• Cochran's Q test is• H0: The treatments are equally effective.

• Ha: There is a difference in effectiveness among treatments.

where• k is the number of treatments• X• j is the column total for the jth treatment• b is the number of blocks• Xi • is the row total for the ith block• N is the grand total

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Summary Table of Statistical Tests

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Why SPSS better than excel, Minitab, R, Mstat or other software

• Easy access to descriptive statistics and frequencies• Full set of statistical tests• Easy to run similar reports and graphics for subsets• Labels instead of codes in your reports• Accurate results when some data is missing• Wider variety of charts & graphs• Better, more flexible pivot tables • Helps you spot data-entry errors or unusual data points• Easy import functions• Unlimited rows• Using SPSS saves time and increases productivity• SPSS makes it easy to understand statistical results

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• To review research studies related to SPSS (Non Parametric tests)

III Objective

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Awareness and Usage of Statistical software for Data Analysis

Software Don’t Know(%)

Heard about this software

(%)

Know to use this software

(%)

Using/used this software for analysis (%)

n1=60 n2=20 n1 = 60 n2=20 n1 = 60 n2=20 n1 = 60 n2=20

Students Staff Students Staff Students Staff Students Staff

MS-Excel 0.0 0.0 100.0 100.0 98.0 90.0 83.0 60.0

Minitab 25.0 35.0 75.0 65.0 23.0 20.0 13.0 5.0

SPSS 12.0 10.0 88.0 90.0 46.0 45.0 36.0 40.0

SAS 35.0 25.0 65.0 75.0 15.0 15.0 7.0 10.0

Mstat 60.0 45.0 39.0 55.0 3.0 10.0 0.0 10.0

Others 90.0 85.0 10.0 15.0 10.0 10.0 10.0 10.0

(N=80)

(* Multiple Response Format)

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Ranjay et al.,

(2000)

1.Review of Adoption Research studies Published in Maharashtra Journal of Extension Education

since 1982 to 1997

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1. Data Analysis techniques used in research

• N = 213 diffusion and adoption research studies

Percentage: 43.50 % Correlation coefficient: 15.00 % Multiple Regression: 12.00 % Mean & SD: 55.00 % Chi-square test : 15.00 % t-test : 3.76 % z-test: 3.50 %

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Tripathi et al.,(2000)

2. Review of Agricultural communication Research studies

Published in Maharashtra Journal of Extension Education

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• N = 89 communication research studies

Majority (76.47%) of the respondents analyzed the data on the basis of percentage, correlation coefficient, Mean, SD and multiple regression.

Chi-square test : 12.03% t-test : 8.99 % z-test: 2.25 %

2. Data Analysis techniques used in research

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B.S. Lakshman Reddy

(2002)

3.Content Analysis of Post Graduate Research in Extension at UAS,

Bangalore

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Table 5: Statistical tools used in extension research studies

Parametric Tools No % Parametric Tools No %

1. Percentage 266 100.00 13. Yates correction 7 2.63

2. Frequency 266 100.00 14. Normality test 5 1.88

3. Mean 241 90.60 15. Path Analysis 4 1.50

4. SD 183 68.79 16. Binomial Analysis 4 1.50

5. Correlation co-efficient 103 38.72 17. Median 3 1.13

6. t-test 83 31.20 18. Mode 3 1.13

7. Multiple Regression 55 20.68 19. Critical Difference 2 0.75

8. ANOVA 43 16.16 20. Bartletts Test 1 0.37

9. F-test 16 6.02 21. Cluster Analysis 1 0.37

10. Rank Order 15 5.64 22. Paired comparison 1 0.37

11. Spearman Rank correlation

9 3.38 23. F-Matrix 1 0.37

12. PPMC 9 3.38 24. Z-Matrix 1 0.37

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Non-Parametric Tools No % Non-Parametric Tools No %1. Chi-square 13 52.25 8. Sign test 2 0.752. Kruskal wallis one way analysis

5 1.88 9. Omega test 1 0.37

3. Kendall co-efficient of concordance

5 1.88 10. Contingency co-efficient

1 0.37

4. Kolmogrov Smirnov test

4 1.50 11. Relevancy Co-efficient

1 0.37

5. Wilcoxon test 3 1.13 12. Cochran Q test 1 0.376. Mann Whitney U test

2 0.75 13. Linert Contrast analysis

1 0.37

7. Principal component Analysis

2 0.75

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4. The Ethiopian Extension Package Programme: its effect on Farmer’s Perception and adoption of wheat production technologies

- Elias Zerfu (1999)- Ph.D. study, UAS(B)

• Used SPSS Inc., 1996 window version software for DATA analysis.

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5. Contribution of Livestock production system to farmers Livelihood in western region of Maharashtra

- MONIKA (2009)- M.Sc study, UAS(D)

• used Ms Excel and SPSS version 11 software's for analysis of research data.

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302

87

80

49

43

33

18

9

9

8

SAS

SPSS

STATA

Epi Info

SUDAAN

S-PLUS

StatXact

BMDP

StatView

Statistica

0 100 200 300 400

Statistical Software Packages Most Commonly Cited in the NEJM and JAMA between 1998 and 2002

Number of articles software was sited29/11/2014


Table 6: Data Analysis

techniques used in the Master Degree

studies, Department of

Agril. Extension, UAS, Bangalore

UAS(B) M.Sc 1991-2005 2006-2014 TotalN n1=132 n2=99 N=231

Parametric Tests No. % No. % No. %1 Frequency 132 100.00 99 100.00 231 100.002 Mean 60 45.45 89 89.90 149 64.503 Standard deviation 55 41.67 84 84.85 139 60.174 Percentage 132 100.00 99 100.00 231 100.005 correlation 86 65.15 66 66.67 152 65.806 Multiple correlation 0 0.00 1 1.01 1 0.437 Regression 21 15.91 23 23.23 44 19.058 Multiple regression 46 34.85 31 31.31 77 33.339 Pearson product moment correlation 8 6.06 4 4.04 12 5.19

10 Path analysis 1 0.76 0 0.00 1 0.4311 t-test 25 18.94 13 13.13 38 16.4512 paired t-test 8 6.06 10 10.10 18 7.7913 Z-test 0 0.00 2 2.02 2 0.8714 ANOVA 9 6.82 3 3.03 12 5.1915 Median test 5 3.79 0 0.00 5 2.1616 Ranks 3 2.27 5 5.05 8 3.4650

Table 6: Data Analysis techniques used in the Master Degree studies, Department of Agril. Extension, UAS, Bangalore

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Table 6 contd…UAS(B) M.Sc 1991-2005 2006-2014 Total

N n1=132 n2=99 N=231Parametric Tests No. % No. % No. %

18 Discriminant factor analysis 1 0.76 0 0.00 1 0.4319 Time series analysis 1 0.76 1 1.01 2 0.8720 Logistic Regression analysis 2 1.52 0 0.00 2 0.8721 co-efficient of variation 1 0.76 0 0.00 1 0.43

Non-Parametric tests22 Chi-square 60 45.45 50 50.51 110 47.6223 contingency co-efficient 6 4.55 3 3.03 9 3.9024 Kruskal wallis one way

ANOVA4 3.03 1 1.01 5 2.16

25 Principal component Analysis 1 0.76 0 0.00 1 0.4326 Sign test 0 0.00 1 1.01 1 0.4327 Wilcoxon Rank sum test 2 1.52 0 0.00 2 0.8728 Mann-whitney U test 4 3.03 1 1.01 5 2.1629 Kendalls co-efficient of

concordance5 3.79 1 1.01 6 2.60

30 Contingent valuation method 1 0.76 0 0.00 1 0.4329/11/2014


Table 7: Data Analysis techniques used in the Master Degree studies, Department of Agril. Extension, UAS, Dharwad

UAS(D) M.Sc 2004-2007 2008-2010 TotalN n1 =24 n2=24 N=48

Parametric Tests No. % No. % No. %1 Frequency 21 87.50 21 87.50 42 87.502 Mean 15 62.50 15 62.50 30 62.503 Standard deviation 15 62.50 15 62.50 30 62.504 Percentage 24 100.00 24 100.00 48 100.005 correlation 13 54.17 10 41.67 23 47.926 Regression 3 12.50 0 0.00 3 6.257 Multiple regression 2 8.33 0 0.00 2 4.178 t-test 3 12.50 3 12.50 6 12.509 Z-test 1 4.17 0 0.00 1 2.08

10 Discriminant factor analysis 1 4.17 0 0.00 1 2.08

Non-Parametric tests11 Chi-square 2 8.33 2 8.33 4 8.3312 Wilcoxon Rank sum test 1 4.17 0 0.00 1 2.0829/11/2014


Table 8: Data Analysis techniques used in Doctoral Degree studies, Department of Agril. Extension, UAS, Bangalore

UAS(B) Ph.D. 1990-2005 2006-2013 TotalN n1 =41 n2=22 N=63

Parametric Tests No. % No. % No. %1 Frequency 41 100.00 22 100.00 63 100.002 Mean 21 51.22 17 77.27 38 60.323 Standard deviation 19 46.34 18 81.82 37 58.734 Percentage 41 100.00 22 100.00 63 100.005 Standard normal variate 1 2.44 0 0.00 1 1.596 correlation 34 82.93 11 50.00 45 71.437 Multiple correlation 4 9.76 1 4.55 5 7.948 Regression 27 65.85 9 40.91 36 57.149 Multiple regression 14 34.15 9 40.91 23 36.51

10 Pearson product moment correlation 11 26.83 5 22.73 16 25.4011 Path analysis 12 29.27 3 13.64 15 23.8112 t-test 13 31.71 5 22.73 18 28.5713 paired t-test 3 7.32 6 27.27 9 14.2914 ANOVA 12 29.27 2 9.09 14 22.2215 Critical difference 1 2.44 0 0.00 1 1.5916 Measures of Normal distrribution 1 2.44 0 0.00 1 1.5929/11/2014


Table 8 contd..18 Bivariate frequency distribution 1 2.44 0 0.00 1 1.5919 Factor analysis 2 4.88 2 9.09 4 6.3520 Discriminant factor analysis 11 26.83 1 4.55 12 19.0521 Neumans Keuls multiple Range test 1 2.44 0 0.00 1 1.5922 proportion test 1 2.44 0 0.00 1 1.5923 co-efficient of variation 2 4.88 0 0.00 2 3.1724 Multiple Discrimination Analysis 1 2.44 0 0.00 1 1.5925 Multivariate path co-efficient Analysis 1 2.44 1 4.55 2 3.17

Non-Parametric tests26 Chi-square 17 41.46 17 77.27 34 53.9727 contingency co-efficient 4 9.76 1 4.55 5 7.9428 Kruskal wallis one way ANOVA 2 4.88 0 0.00 2 3.1729 Principal component Analysis 0 0.00 4 18.18 4 6.3530 Mann-whitney U test 4 9.76 0 0.00 4 6.3531 Kendalls co-efficient of concordance 1 2.44 0 0.00 1 1.59

17 Ranks 1 2.44 0 0.00 1 1.59

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Table 9: Data Analysis techniques used in Doctoral Degree studies, Department of Agril. Extension, UAS, Dharwad

UAS(D) Ph.D. 2004 2005-06 TotalN n1 =7 n2=3 N=10

Parametric Tests No. % No. % No. %1 Frequency 7 100.00 3 100.00 10 100.002 Mean 3 42.86 1 33.33 4 40.003 Standard deviation 4 57.14 1 33.33 5 50.004 Percentage 6 85.71 3 100.00 9 90.005 correlation 3 42.86 2 66.67 5 50.006 Regression 3 42.86 1 33.33 4 40.007 Multiple regression 0 0.00 1 33.33 1 10.008 Path analysis 2 28.57 1 33.33 3 30.009 t-test 1 14.29 1 33.33 2 20.00

10 Z-test 1 14.29 0 0.00 1 10.0011 Discriminant factor analysis 1 14.29 0 0.00 1 10.00

Non-Parametric tests12 Chi-square 3 42.86 0 0.00 3 30.0029/11/2014


Conclusion

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Many thanks for your patience listening

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Statistical package for the extension science research by vinay

Data & Analytics

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