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BUS 367 FINAL Ch. 14 Analysis Methods
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Terms in this set (44)
coding
process of assigning a numerical score or other character symbols to previously edited data
nominal data- coded by word, letter, or any identifying mark
ordinal, interval, ratio- numbers
any mistakes in coding can dramatically change the conclusions
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codes
rules for interpreting, classifying, and recording data in the coding process
the actual numerical or other character symbol assigned to raw data
dummy coding
numeric "1" or "0" coding where each number represents an alternate response such as "female" or "male"
provides the researcher with more flexibility in how structured, qualitative responses are analyzed statistically
can only be two variables
effects coding
alternative to dummy coding using values of -1 and +1 to represent two categories of responses
class coding
assigns numbers to categories in a random way as a means of identifying some characteristic
descriptive analysis
most basic statistical analysis, elementary transformation of raw data in a way that describes the basic characteristics such as central tendency, distribution, and variability
averages, medians, modes, variable, range, and stand deviation
can summarize response from large numbers of respondents in a few simple statistics
histogram
graphical way of showing the frequency distribution in which the height of a bar corresponds to the frequency of a category
useful for any type of data
useful for providing a quick assessment
tabulation
orderly arrangement of data in a table or other summary format
tells the researcher how frequently each response occurs
tallying
when done by hand
frequency table
table showing the different ways respondents answered a question
marginal tabulation
simple tabulation
as long as a question deals with only one categorical variable, tabulation is the best approach to communicate the result
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simple tabulation may not yield the full value of the research when multiple variables are involved
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cross tabulation
more appropriate technique for addressing research questions involving relationships among multiple less than interval variables
combined frequency table
allows the inspection and comparison of differences among groups based on nominal or ordinal categories
contingency table
data metric that displays the frequency of some combination of possible responses to multiple variables
two way contingency tables are used most often
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beyond 3 variables, contingency tables become difficult to analyze and explain easily
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researchers are usually more interested in the inner cells of a contingency table
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inner cells display conditional frequencies
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marginals
row and column totals in a contingency table which are how in margins
when data from a survey are cross-tabulated, percentages help the researcher understand the nature of the relationship by making relative comparisons simpler
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statistical base
number of respondents or observations (in a row or column) used as a basis for computing percentages
the Oxford Universal Dictionary defines analysis as "the resolution of anything complex into its simplest elements"
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elaboration analysis
involves the basic cross-tabulation within various subgroups of the sample
researcher breaks down the analysis for each level of another variable
the finding is consistent with an interaction effect
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moderator variable
third variable that changes the nature of a relationship between the original independent and dependent variables
descriptive research and causal research designs often climax with hypotheses tests
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empirical testing typically involves inferential statistics
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an inference can be made about some population based on observations of a sample representing that population
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statistical analysis can be divided into several groups based on how many variables are involved:
univariate statistical analysis
bivariate statistical analysis
multivariate statistical analysis
univariate statistical analysis
tests hypotheses involving only one variable
bivariate statistical analysis
tests hypotheses involving two variables
multivariate statistical analysis
tests hypotheses and models involving multiple variables or sets of variable and potentially involving multiple equations
hypotheses are tested by comparing an educated guess with empirical reality
1. the hypotheses is derived from the research objective. the hypothesis should be stated as specifically as possible and should be theoretically sound
2. a sample is obtained and the relevant variables are measured. in univariate tests, only one variable is of interest
3. the measures value obtained in the sample is compared to the value either stated explicitly or implied in the hypothesis. if the value is consistent with the hypothesis, the hypothesis is supported. if the value is not consistent with the hypothesis, it is not supported
significance level
critical probability associated with a statistical hypothesis test that indicates how likely it is that an inference supporting a difference between an observed value and some statistical expectation is true
p value
stands for profitability value
another name for an observed or computed significance level
the probability in a p value is that the statistical expectation (null) for a given test is true
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low p values mean there is little likelihood that the statistical expectation is true
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hypotheses testing using sample observations is based on probability theory
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since we cannot make any statement about a sample with a complete certainty
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there is always a chance that an error will be made
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type 1 error
occurs when a condition that is true in the population is rejected based on statistical observations
ex.) suppose the observed sample mean described earlier leads to the conclusion that the mean is greater than 1.5 when in fact the true population mean is equal to 1.5
type 2 error
occurs when the sample data suggests that a relationship does not exist when in fact a relationship does exist
related to statistical power
sample size is sometimes too small to provide the power needed to find a relationship
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