Read this article

http://www.nepjol.info/index.php/NJE/article/view/6151

Read this article also

http://www.nepjol.info/index.php/NJE/article/view/5752

hi

I found a useful article for you :

www.sfu.ca/~palys/SPSS%20Lesson%201.pdf

and to create a var. in SPSS try to read : http://academic.udayton.edu/gregelvers/psy216/spss/define_var.htm

you need more ? pls let me know .

From the SPSS technical manual:

Nominal: A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the department of the company in which an employee works). Examples of nominal variables include region, zip code, and religious affiliation.

Ordinal: A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.

Scale: A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

Descriptive statistics and statistical analysis&tests change according to scales of variables.

Frequency distribution, mode, pie and bar charts could be derived from the nominal variables.

Frequency distribution, mode, median, min-max values, range, bar chart could be derived from the ordinal variables.

Frequency distribution, mode, median, min-max values and range, mean(arithmetic, geometric, harmonic), variance, standard deviation, coefficient of deviation, percentile, interquantile range, skewness, kurtosis, moments, bar chart, histogram, box plot, scatter plot... could be derived from the scale variables.

Because of being required some assumptions, parametric tests are performed only for the scale variables.

Nonparametric tests could be performed for both scale, ordinal, nominal variables. It is possible to say that nonparametric tests are usually prefered to use for scale variables whether the assumptions of the parametric tests are violated.

thank you all for your help.I am trying to understand which analysis is most suitable for each type of variable.thank you again

Hello Anna Sintichaki

If you go to the favourite links page of my website there is an electronic stats textbook and it has good descriptions of these terms. Thanks, Deborah Hilton Statistics Online

http://sites.google.com/site/deborahhilton/

either you choose the level of measurement ot leave it to the default setting in SPSS. I found SPSS is smart to recognize the variable level of measurement. 

I found few relevant papers

Likert scales, levels of measurement and the “laws” of statistics

Norman, G. Adv in Health Sci Educ (2010) 15: 625. https://doi.org/10.1007/s10459-010-9222-y

Article LIkert scales, levels of measurement adn the “laws” of statistics

The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability

JL Fleiss, J Cohen - Educational and psychological measurement, 1973Kappa is the proportion of agreement corrected for chance, and scaled to vary from-1 to+ 1 so that a negative value indicates poorer than chance agreement, zero indicates exactly chance agreement, and a positive value indicates better than chance agreement. A value of unity indicates perfect agreement. The use of kappa implicitly assumes that all disagreements are equally serious. When the investigator can specify the relative serious-ness of each kind of disagreement, he may employ weighted kappa, the proportion of

Journal Article

A Model for Agreement Between Ratings on an Ordinal Scale

Alan Agresti Biometrics Vol. 44, No. 2 (Jun., 1988), pp. 539-548 Published by: International Biometric Society DOI: 10.2307/2531866 Stable URL: http://www.jstor.org/stable/2531866 Page Count: 10