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==Population and sampling frame== | ==Population and sampling frame== | ||
| − | A sampling study starts with a number of questions that refer to a certain domain of interest. That domain is called the '''''population''''' and is defined as the totality of all elements. | + | A [[:Category:Sampling design|sampling]] study starts with a number of questions that refer to a certain domain of interest. That domain is called the '''''population''''' and is defined as the totality of all elements. It is important to distinguish this definition from the definition of a biological population that is the collection of inter-breeding organisms of a particular species (Zöhrer 1980 <ref> Zöhrer, F. Forstinventur. Ein Leitfaden für Studium und Praxis. Pareys Studientexte 26. Verlag Paul Parey. 207 S.</ref>). |
| − | In the ideal case the sampling frame contains all elements of a population, however one can imagine reasons for differences of both. It is | + | The number of elements from which a sample should be drawn is called the '''''sampling frame''''' which is a list of all elements that can be selected during [[statistical sampling]] (all elements that have a [[inclusion probability]] larger than 0). It is important to note that a ''sampling frame'' has the property that we can identify every single element and include any in our sample. |
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| + | In the ideal case the sampling frame contains all elements of a population, however one can imagine reasons for differences of both. It is good practice that both, population and sampling frame, should be clearly defined for any sampling study. Reasons for a sample frame that is smaller as the population is for example, that parts of the population can not be sampled, because they are not accessible. In [[Forest inventory|forest inventories]] we can imagine, that areas with extreme steep [[Measuring slope|slopes]] can not be sampled. In those cases one should consider to re-define the population. | ||
{{Info | {{Info | ||
|message=Note: | |message=Note: | ||
| − | |text=By means of a sampling study one is able to derive statistical sound estimations for the part of the population that is in the | + | |text=By means of a sampling study one is able to derive statistical sound estimations for the part of the population that is in the sampling frame. We can derive estimates for those sampling units that have a probability to be selected only! If the population is larger than the sampling frame, we can not justify any estimations assigned to the whole population. |
}} | }} | ||
| − | In forestry we are typically interested in estimating variables of forests or trees. Nevertheless the sampling frame is rarely the set of all trees in a forest area, but the area itself. This area consists of | + | ===The sampling frame in forest inventories=== |
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| + | In forestry we are typically interested in estimating variables of [[Forest Definition|forests]] or [[Tree Definition|trees]]. Nevertheless the sampling frame is rarely the set of all trees in a forest area, but the area itself. This area consists of an infinite number of dimensionless points from which a certain number is selected as [[Lecturenotes:sample point|sample point]]s (Kleinn 2007<ref name="kleinn2007">Kleinn, C. 2007. Lecture Notes for the Teaching Module Forest Inventory. Department of Forest Inventory and Remote Sensing. Faculty of Forest Science and Forest Ecology, Georg-August-Universität Göttingen. 164 S.</ref>). This definition is also called an '''areal sampling frame'''. Around such a sample point we define a certain area that is the [[Plot design|sample plot]] where the observation one makes on this area is assigned to the respective point. | ||
{{Info | {{Info | ||
|message=Important! | |message=Important! | ||
| − | |text=The elements that are sampled and of which the sample frame consists are the sample plots and not single trees! In other words: one selects areas in the forest (and observe the trees on these plots) and not trees. This fact has far reaching consequences for the statistical issues. | + | |text=The elements that are sampled and of which the sample frame consists are typically the [[Sample plot|sample plots]] and not single trees! In other words: one selects areas in the forest (and observe the trees on these plots) and not single trees. This fact has far reaching consequences for the statistical issues, for example for the definition of the population. |
| + | }} | ||
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| + | In contrast to the infinite size of the sample frame one obviously can only observe a discrete number of trees (and combinations of trees) in the area of interest. The infinite sample frame can be decomposed in areas related to this discrete number of possible clusters or samples of trees. More information about the decomposition of the sample frame can be found in an article about the so called [[Creating jigsaw puzzles in ArcMap|jigsaw puzzle view]]. | ||
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| + | ==References== | ||
| + | <references/> | ||
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| + | |keywords=Population,sampling frame,forest inventory, | ||
| + | |descrip=The population the totality of all elements in which we are interested and to which our estimations refer to and the sampling frame is the list of all elements from which the sample is being drawn. | ||
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| − | [[Category:sampling]] | + | [[Category:Introduction to sampling]] |
Latest revision as of 09:58, 28 October 2013
Contents |
[edit] Population and sampling frame
A sampling study starts with a number of questions that refer to a certain domain of interest. That domain is called the population and is defined as the totality of all elements. It is important to distinguish this definition from the definition of a biological population that is the collection of inter-breeding organisms of a particular species (Zöhrer 1980 [1]).
The number of elements from which a sample should be drawn is called the sampling frame which is a list of all elements that can be selected during statistical sampling (all elements that have a inclusion probability larger than 0). It is important to note that a sampling frame has the property that we can identify every single element and include any in our sample.
In the ideal case the sampling frame contains all elements of a population, however one can imagine reasons for differences of both. It is good practice that both, population and sampling frame, should be clearly defined for any sampling study. Reasons for a sample frame that is smaller as the population is for example, that parts of the population can not be sampled, because they are not accessible. In forest inventories we can imagine, that areas with extreme steep slopes can not be sampled. In those cases one should consider to re-define the population.
Note:
- By means of a sampling study one is able to derive statistical sound estimations for the part of the population that is in the sampling frame. We can derive estimates for those sampling units that have a probability to be selected only! If the population is larger than the sampling frame, we can not justify any estimations assigned to the whole population.
[edit] The sampling frame in forest inventories
In forestry we are typically interested in estimating variables of forests or trees. Nevertheless the sampling frame is rarely the set of all trees in a forest area, but the area itself. This area consists of an infinite number of dimensionless points from which a certain number is selected as sample points (Kleinn 2007[2]). This definition is also called an areal sampling frame. Around such a sample point we define a certain area that is the sample plot where the observation one makes on this area is assigned to the respective point.
- Important!
- The elements that are sampled and of which the sample frame consists are typically the sample plots and not single trees! In other words: one selects areas in the forest (and observe the trees on these plots) and not single trees. This fact has far reaching consequences for the statistical issues, for example for the definition of the population.
In contrast to the infinite size of the sample frame one obviously can only observe a discrete number of trees (and combinations of trees) in the area of interest. The infinite sample frame can be decomposed in areas related to this discrete number of possible clusters or samples of trees. More information about the decomposition of the sample frame can be found in an article about the so called jigsaw puzzle view.
[edit] References
- ↑ Zöhrer, F. Forstinventur. Ein Leitfaden für Studium und Praxis. Pareys Studientexte 26. Verlag Paul Parey. 207 S.
- ↑ Kleinn, C. 2007. Lecture Notes for the Teaching Module Forest Inventory. Department of Forest Inventory and Remote Sensing. Faculty of Forest Science and Forest Ecology, Georg-August-Universität Göttingen. 164 S.
