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What is the difference between agreestrongly agree disagree and strongly disagree?
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What is the ordinal scale in statistics?
An ordinal scale is a method of categorising observation according to a scheme in which there is a sense of ordering between categories but the difference between categories is variable and unspecified. For example, the scale {strongly disagree, disagree, neither disagree nor agree, agree, strongly disagree}.
How is the interval scale more sophisticated than the nominal and ordinal?
In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".
How does an ordinal categorical variable works.?
An ordinal categorical variable is often used in questions for which the responses can be put into some kind of natural order but where the difference between categories is not the same. One example may be where the respondent is asked to class statements as "disagree strongly", "disagree", "neither disagree nor agree", "agree", and "agree strongly". There is a natural progression in the response but the difference between "disagree strongly" and "neither ... " [2 steps] may not be the same as that between "disagree" and "agree" [also 2 steps].The results of any analyses which attaches numerical value to the answers for processing is sensitive to the coding system used. The results with the answers coded as {1, 2, 3, 4, 5} will be quite different to those coded {1, 4, 10, 20, 25}.
What is ordinal and nominal scale?
A nominal scale is one in which the data are categoric, for example, pet animal, town, colour of eyes. There is no order in the categories. A dog is not "more" or less than a "cat". An ordinal scale in which there is an implied ordering but this is based on an arbitrary scale. The difference between a pair of adjacent categories may or may not be related to the difference between another pair. Typical survey questionnaires with answers categorised as: "strongly disagree, disagree, neither, agree, strongly agree".
What is an ideal measure of central tendency?
It depends primarily on the nature of the data. If the data are qualitative data then the only option is the mode. Sometimes data can be ordered but the interval between adjacent categories is not always the same. An example of such data might be answers to a questionnaire where the answers are "strongly disagree", "disagree", "agree" and "strongly agree". The difference between strongly disagree and disagree may not be comparable to the difference between disagree and agree. In such cases, the median is readily available but the mean depends on arbitrarily assigned weights (for the categories). You can have interval data, in which the values of the variable are known and the differences between the values are also quantified. In such cases both the mean and median may be used. The median is generally preferred for skewed data since it is not greatly affected by outliers. For more symmetric data sets there is little to choose between the median and the mean since they will be close together. However, by the Central Limit Theorem, the mean result from repeated trials will tend towards the population mean. The sample mean is a maximum likelihood unbiased estimate of the population mean. Also, the means are often one of the parameters of parametric statistical distributions. The distribution of the mean of repeated trials has been extensively studied and there are many efficient tests for testing hypotheses concerning the mean.
What is the ordinal scale in statistics?
An ordinal scale is a method of categorising observation according to a scheme in which there is a sense of ordering between categories but the difference between categories is variable and unspecified. For example, the scale {strongly disagree, disagree, neither disagree nor agree, agree, strongly disagree}.
How is the interval scale more sophisticated than the nominal and ordinal?
In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".In an ordinal scale it is possible to order the categories by some measure. However, it is not possible to know if the difference between the categories is the same or different.For example, clothing items may be classed as extra small (XS), small (S), medium (M), large (L) and extra large (XL). This is an ordinal scale since you know that the sizes increase in the order in which these have been listed. But you do not know if the difference between S and M is the same as the difference between L and XL (or each adjacent pair).Another example is attitude surveys where answers may be "strongly disagree", "disagree", "neither disagree not agree", "agree", "strongly agree".
How does an ordinal categorical variable works.?
An ordinal categorical variable is often used in questions for which the responses can be put into some kind of natural order but where the difference between categories is not the same. One example may be where the respondent is asked to class statements as "disagree strongly", "disagree", "neither disagree nor agree", "agree", and "agree strongly". There is a natural progression in the response but the difference between "disagree strongly" and "neither ... " [2 steps] may not be the same as that between "disagree" and "agree" [also 2 steps].The results of any analyses which attaches numerical value to the answers for processing is sensitive to the coding system used. The results with the answers coded as {1, 2, 3, 4, 5} will be quite different to those coded {1, 4, 10, 20, 25}.
What is difference between continuous and ordinal variable?
A continuous variable is one which can take any numerical value over some interval. An ordinal variable is one that can take non-numerical or categoric values which can be put into some logical order but where the difference between successive categories cannot be quantified. One example may be Small-Medium-Large, or a popular one among opinion pollsters: Disagree Strongly-Disagree-Agree-Agree Strongly.
What is ordinal and nominal scale?
A nominal scale is one in which the data are categoric, for example, pet animal, town, colour of eyes. There is no order in the categories. A dog is not "more" or less than a "cat". An ordinal scale in which there is an implied ordering but this is based on an arbitrary scale. The difference between a pair of adjacent categories may or may not be related to the difference between another pair. Typical survey questionnaires with answers categorised as: "strongly disagree, disagree, neither, agree, strongly agree".
What is an ideal measure of central tendency?
It depends primarily on the nature of the data. If the data are qualitative data then the only option is the mode. Sometimes data can be ordered but the interval between adjacent categories is not always the same. An example of such data might be answers to a questionnaire where the answers are "strongly disagree", "disagree", "agree" and "strongly agree". The difference between strongly disagree and disagree may not be comparable to the difference between disagree and agree. In such cases, the median is readily available but the mean depends on arbitrarily assigned weights (for the categories). You can have interval data, in which the values of the variable are known and the differences between the values are also quantified. In such cases both the mean and median may be used. The median is generally preferred for skewed data since it is not greatly affected by outliers. For more symmetric data sets there is little to choose between the median and the mean since they will be close together. However, by the Central Limit Theorem, the mean result from repeated trials will tend towards the population mean. The sample mean is a maximum likelihood unbiased estimate of the population mean. Also, the means are often one of the parameters of parametric statistical distributions. The distribution of the mean of repeated trials has been extensively studied and there are many efficient tests for testing hypotheses concerning the mean.
Kinds of variables used in statistics?
There are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
What are the types of variables in statistics?
dependent variables, independent variable, nominal, ordinal, interval, ratio variableThere are three main kinds:Nominal: such as colour of eyes, or gender, or species of animal. With nominal variables there is no intrinsic sense in which one category can be said to be "more" than another.Ordinal: Such as Small/Medium/Large, orStrongly Disagree/Disagree/Indifferent/Agree/Srongly Agree. The categories can be ordered but the differences between pairs is not comparable. For example, it is not really possible to say that the difference betwen Strongly disagree and disagree is the same as (or double or half or whatever) the difference between indifferent and agree.Interval: These are variables where the distance between one pair of values (their interval) can be related to the distance between another pair. Such variables can be subdivided into discrete and continuous.Another way of classifying variables is independent and dependent.The dependent variable is a random variable but the independent variable can be random or non-random.
What did Juana try to do with witch kino strongly disagree?
Juana tried to get rid of the pearl after realizing how destructive it was, but Kino strongly disagreed and believed they could still benefit from it. This disagreement led to conflict between them as they had different perspectives on the pearl's value and potential consequences.
What tools are used to measure qualitative data?
The answer depends on the type of qualitative data.You would use your taste buds as tools to distinguish between sweet, sour, salt and so on.You could use you sight to determine the colour of eyes, hair or cars.You would use your own judgement to choose between "strongly agree", "agree", "disagree" or "strongly disagree".
Are there any tips for passing the customer support supervisor assessment test at walmart?
the tip to passing any manager assessment test is to strongly disagree or strongly agree to any question.. nothing in between, they want their managers to have strong opinions about situations so that they can make a decision quickly on matters and not hesitate or reverse their decision.
What is the difference between in love and love?
being in love means that you have found your soul mate. love means that you strongly like someone, but not you 'special' one.
