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measurement

assigning the characteristics of objects to a number or a symbol.

Description:

the unique labels or descriptors that are used to designate each value of the scale. All scales possess description.

Order:

the relative sizes or positions of the descriptors. Numbers indicate the relative extent to which the objects possess some characteristic. Order is denoted by descriptors such as greater than, less than, and equal to.

Distance

: the absolute differences between the scale descriptors are known and may be expressed in units. Equal distances on the scale equal values in the characteristic measured. Distance implies order, but reveres might not be true.

Origin

that the scale has a unique or fixed beginning or true zero point. (the "0" point has a unique, real meaning, that cannot be substituted with other numbers)

Nominal-

description

Ex: 1 male 2 female

example of nominal scale

Ordinal

description and order

Ex: rating soccer teams based upon their points scored

example of ordinal scale

Interval-

description, order, and distance

Ex: ranking soccer teams in order with equal distance in between each

example of interval scale

Ex: strongly disagree, agree, neither, disagree, and strongly disagree

example of interval scale

Ratio

description, order, distance, and origin

Ex: what is your annual income? ____

example of ratio scale

Scaling:

describes how the measurement is obtained

Comparative scale technique

: involve the direct comparison of stimulus objects. ? data must be interpreted in relative terms

Paired Comparison

: A respondent is presented with two objects and asked to select one according to some criterion.

Rank Order Scaling

: Respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion.

Constant Sum Scaling

Respondents allocate a constant sum of units, such as 100 points to attributes of a product to reflect their importance (e.g., preference, attitudes,..).

Advantages of Comparative Scales:

Small differences between stimulus objects can be detected.

• Same known reference points for all respondents.

• Easily understood and can be applied.

• Same known reference points for all respondents.

• Easily understood and can be applied.

Disadvantages of Comparative Scales:

Ordinal nature of the data (rank ordering and paired comparison)

- You don't know how much more a product is preferred to another

• Inability to generalize beyond the stimulus objects scaled.

- You don't know how much more a product is preferred to another

• Inability to generalize beyond the stimulus objects scaled.

Constant Rating Scale

Respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. The form of the continuous scale may vary considerably.

constant rating scale

what type of scale is this?

Example

Probably the worst ----------------*-----------------Probably the best

Example

Probably the worst ----------------*-----------------Probably the best

Itemized Rating Scales

: The respondents are provided with a scale that has a number or brief description associated with each category.

The Likert scale

requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects

The semantic differential

: is a rating scale with end points associated with bipolar labels that have (opposite) semantic meaning.

semantic differential

What type of scale is this?

EX:

Powerful - - - - - - -Weak

EX:

Powerful - - - - - - -Weak

Rules for Individual question content

is the question necessary?

Are several questions needed instead of one?

Can the respondent remember?

effort required

avoid ambiguous questions

avoid generalizations and estimates

order of questions

Are several questions needed instead of one?

Can the respondent remember?

effort required

avoid ambiguous questions

avoid generalizations and estimates

order of questions

Descriptive analysis

used to describe the data set

Measures of central tendency

used to report a single piece of information that describes the most typical response to a question

Mean:

the average value characterizing a set of numbers

Mode

: the value in a string of numbers that occurs most often

• Median

the value whose occurrence lies in the middle of a set of ordered values

Measures of variability

: used to reveal the typical difference between the values in a set of values

Frequency distribution

reveals the number (percent) of occurrences of each number or set of numbers

Range

identifies the maximum and minimum values in a set of numbers

• Standard deviation

indicates the degree of variation in a way that can be translated into a bell-shaped curve distribution

Cumulative distribution

sum of the count/ percentage all of the preceding numbers plus the present one

The variance

is the mean squared deviation from the mean. The variance can never be negative

The standard deviation

is the square root of the variance. (Tells you how far apart from the mean are, on average, the observations in your dataset)

Inferential analysis

: used to generate conclusions about the population's characteristics based on the sample data

Differences analysis

: used to compare means

. Associative analysis

determines the strength and direction of relationships between two or more variables

. Predictive analysis

allows one to make forecasts for future events

Population.

The target ? is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made.

A sample

on the other hand, is a subgroup of the population selected for participation in the study

Parameter

A ? is a summary description of a fixed characteristic or measure of the target population. A ? denotes the true value which would be obtained if a census rather than a sample was undertaken.

Statistic

A ? is a summary description of a characteristic or measure of the sample. The sample ?is used as an estimate of the population parameter.

A Confidence interval

tells that there "X"% of the samples means drawn from that population will have a mean within in a certain interval (sample mean ± T)

Confidence Interval equation

= x (+or -) T

X equals

This is what we got from the sample (i.e., $18)

T equals

Tolerance (your book call this sample error)

z* s/square root of n

z* s/square root of n

confidence level

Z equals

S equals

standard deviation

N equals

population size

when is t test appropriate?

when the sample size is less than or equal to 30

when is the p test appropriate?

when the sample size is greater than 30

Duncan Post Hoc Test

when the group means are not equal which test do you use to find out which group is different?