COM 308
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Author:
mgt1084
ID:
159332
Filename:
COM 308
Updated:
2012-06-20 12:45:29
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Communication Methods Statistics
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Description:
Test Three Material: Statistics
Show Answers:
what is a statistic
any numerical indicator of a set data
b. the application of procedures to produce numerical descriptions and statistical inferences
descriptive statistics
- used to summerize the information in a given data set pertaining to a particular sample
inferential statistics
trying to infer; draw conclusions about the data so that we can make generalizations
Two key components to Inferential Statistics
estimation
: estimating the characteristics of a population from data gathered on a sample; how representative is my estimation of my population
significance testing
: testing for significant statistical differences between groups and significant relationships between variables; p<.05
Five Key Types of Descriptive Stats
Central Tendency
: a center point in my data; could be the mean
Dispersion
: how spread out are the participants
Standard scores
: standard deviation and z scores; takes the numbers and standardized
Frequencies
: how many in each group;
Visual Displays
: graphs and charts
Central Tendency: Mode
Mode
: simplest; what number occurs most often; can have multiple modes
- Appropriate for nominal data/ not for ordinal
Central Tendency: Median
- middle most score in a distribution; cut the distribution in half
- appropriate for ordinal data
- it is resistant to extreme scores (outlyer)
- does not describe "typical"
Central Tendency: Mean
- arithmetic average; it is not resistant
- most appropriate and effective for interval/ration data
- often fractional (round to two decimal points)
Dispersion: Range
- simplest measure
- reports the distance between our highest and lowest score
- general sense of the spectrum of scores
- non resistant
: like the mean, an extreme score will affect the range
Dispersion: Variance
mathematical index of the average distance of teh scores in a distribution from the mean
- tells us the amount of error in our study
Dispersion: Standard Deviation
- average deviation fromt the mean espressed in the original unit of measure
- most often used by researchers
- square root of variance
Standard Score
- common unit of measurement that indicates how far any particular score is away from the mean
- they locate scores within a distribution
Z score
several uses beyond "locating"
:
- multiple raters
- same scale but different context
- different scales
Frequencies:
- frequency distribution
: used to calculate the mode
- absolute frequency
- relative frequency
: the proportion of times each data occurs
- cumulative frequency
Visual Displays of Frequency
pie charts
bar charts
histograms
: like a bar chart, except it is using a ratio or interval variable
Estimating Population Parameters
guessing at the characteristics of our population, statistically speaking
estimates
statistics computed
Normality Assumption
the variable of interest is "normally" distributed in the population
Random Sample
rarely have a true random sample
Normal Distribution
- theoretical distribution representing the location of deviations about the mean and the probablity of these deviations happening
- interval or ratio data
- deviations about the mean are expressed in units
: SD's
- the normal distribution tells researchers the probability of a score falling in any given area of the curve
68-95-99.7 Rule
99.7 of scores fall 3 SD above of 3 SD below the mean
95% of scores fall between 2 and -2 SD
69% of scores fall between 1 and -1 SD
Abnormal Distributions
it is not perfectly symmetrical
can be abnormal in two ways
- kurtosis
: how pointed is my normal distribution
- skewness
: direction of asymmetry
Mesokurtic (0)
Perfectly normal distribution
Leptokurtic (>0)
pointy kurtosis
Platykurtic (<0)
flat kurtosis; most people are widely distributed
Skewed Distribution
all about the direction of the tail; mode, median, then mean (not all perfectly aligned)
Central Limit Theorem
- larger sample size
: the distribution of the means is normal
- larger samples give more accurate results than do smaller samples
- if you cant do random, do large
Making Inferences
-standard error of the mean
: how much does my sample mean differ from my population mean; look at sampling distribution
- confidence level
: how confident am I that my mean in my sample, represents the populatin mean
- confidence interval
: range of my mean score associated with the confidence level
- size of CL influenced by
: variability: factors you cant necessarily control that could affect your findings confidence level: sample size
Statistical significance
patterns or relationships between variables are likely to exist in the real world
Do we really test research hypotheses?
We dont actually test the hypotheses proposed in the study. We test the null hypothesis
Null Hypothesis
- a statement that statistical differences or relationships have occurred for no reason other than chance
- we use statistics to determine whether or not to accept or reject the null, not to prove or disprove H's
- we focus on estimating the probability that H's are true/not true. Hence, our language regarding findings is qualified and tentative
Null Decision
- accept or reject the null
- based upon statistical significance
- in making this decision, we risk making one of two errors