I have observed PM2.5 and PM10 concentration for a particular site on alternate days for a period of 1 week.
I wish to analyse the ratio of pollutants in that specific site.
But as the measurements are carried out in alternate days, is it reasonable to discuss the ratio of PM2.5 / PM10.
If it sounds good, what kind of statistical test should i perform to show that data obtained on alternate days can be compared.
Becoz studies suggest that daily variations in pollution levels will not be significantly varying in urban areas as the sources remains same.
Hi
1. The reliability measures could be developed for the whole questionnaire or for each dimension of the questionnaire .
2. Reliability consists of several measures:
Item alpha reliability and split-half reliability assess the internal consistency of the items in a questionnaire – that is, do the items tend to be measuring much the same thing?
3.Split-half reliability on SPSS refers to the correlation between scores based on the first half of items you list for inclusion and the second half of the items. This correlation can be adjusted statistically to maintain the original questionnaire length.
4.Coefficient alpha is merely the average of all possible split-half reliabilities for the questionnaire and so may be preferred, as it is not dependent on how the items are ordered. Coefficient alpha can be used as a means of shortening a questionnaire while maintaining or improving its internal reliability.
5.Inter-rater reliability (here assessed by kappa) is essentially a measure of agreement between the ratings of two different raters. Thus it is particularly useful for assessing codings or ratings by 'experts' of aspects of open-ended data; in other words, the quantification of qualitative data. It involves the extent of exact agreement between raters on their ratings compared with the agreement that would be expected by chance. Note then that it is different from the correlation between raters, which does not require exact agreement to achieve high correlations but merely that the ratings agree relatively for both raters.
For more details, please refer to
Howitt and Cramer (2008). Introduction to SPSS, Pages 249-258.
I think that 1 week of data is just very very few. You can not tell to much, I do no even question if the discussion of the ratio is worth it. You need more data for sure.
use spss test reliability for measurements made across a week and cronbach alpha can be calculated Nay alpha value more than 0.9 give high reliablity of the data.
Hi Marimuthu,
You could look at other databases that have historical values, and use that to bolster your claim to reliability. We have a very large database in the USA that you could use for comparison. It has both PM10 and PM 2.5 datasets, so you could pull data and test your ratio theory, then if a trend is apparent apply to your data set. You might want to take a look and see if your data matches - look at regional trends in area of USA that is similar to your area - much of this is based on prevailing winds and climate. Data can be found through this link:
https://www.epa.gov/air-trends/particulate-matter-pm10-trends
Hi!
I think Erik Beristain-Montiel is totally right: one week of date on alternate days provides very little information. In this case I would only recommend looking at the experimental uncertainty of each point and propagate the uncertainties in the PM2.5/PM10 ratio.
Prof. Z. A. Al-Hemyari
Thanks for your suggestions and reference.
Mr.Erik Beristain-Montiel
Mr. Ravishankar Suryanarayana
Mr. Javier Miranda
I thank all for your suggestions and discussions.
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