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Descriptive statistics tell what is. There are 9 in class. Theyare used to organize and summarize information or data. They allow a researcher to provide a description of what actually exists in the data. Researchers use descriptive statistics to help us reduce large amounts of data to a more manageable size. Perhaps one of the most commonly used descriptive statistic is percentages.
Are often the next stage is data analysis. Are used when a researcher wants to make predictions. If I look at exam scores of 3 students, can I predict the total classes scores. More unknowns= more errors.They use a smaller group, or a sample to compare data to larger groups.
- Type 1: Alpha- rejecting the null hypothesis when the null is true. "there is some relationship" but there isnt.
- Type 2: Beta- You keep the null hypothesis but it is false. "Theres no relationship" but there is.
Drawn from populations. Smaller than populations. Take 10% of the 23 comm 300 students.
an entire set of objects, observations,or scores that have some characteristic in common.
The average of communication apprehension within a sample. They allow us to summarize or describe data. They enable scientists to make predictions and make sense of the world.
Measures of central tendency:
Mean, Median, Mode
Mode:The value that occurs most in a set of numbers
Measures of variability:
range, sum of squares, variance, standard deviation.
Highest number subtracted by lowest number
Sum of squares
SS= ex2- (ex)2/N
Useful for spotting trends. XAxis= horizontal<> YAxis= Vertical ^
SD= SS/N-1(square rooted)
x minus the mean divided by standard deviation
how scores scatter on a plot
If something is off center, it is skewed. Two types: Positive, Negative.
a curve in which the tail of the curve is longer on the right side of the distribution. The mean will be greater than the mode.
A curve in which the tail is longer on the left side. The mean will be less than the median or mode.
Deals with the shape of the curve. Tall and narrow, or short and squatty.
Short and squatty.... looks like a tea pot... wide range of variability in x axis, low range in y axis
Looks like a tall wine bottle.... low x axis, wide range of y axis.... tall and narrow.
in the middle... normal... roughly equal variances in both y and x axis.
Nominal, Ordinal, interval, ratio
Nominal measurement is a simple classification. Numbers are selected and meaningless. Lowest level of measurement.
Rank-order measurement. Assigns numbers to things in such a way as to reflect relationships among things. Allows us to see hierarchal levels between groups.
Interval measurement identifies the distance between any two things that are measured. We assign numbers to things in such ways that the distance between any two things is measured. They are probably the most common measures used by scientists.
A ratio scale has all of the characteristics of an interval scale, but in addition it has a true zero point as its origin. In ratio measurements, zero has an absolute value of its own.
a spark, a lightbulb of an idea for research... makes researcher start wondering if it can be researched.
Conceptualization and Operationalization
- Conceptualization- Clearly defining your variables.
- Operationalization: Process of measuring what you conceptualize. A good operationalization is like a recipe card. It tells you a very detailed procedure and what to do and when to do it.
parametric statistical test vs nonparametric statistical test
- parametric- researchers can calculate measures. Parameter = value of population.
- nonparametric- make no assumptions about parameters of the popultion from which the research sample can be drawn.
the process of systematic observation and assignment of numbers to phenomena according to rules.
means identity or similarity of form.
Disagree, Strongly disagree, agree, etc etc 5 terms.... ordinal
A variable that a researcher cannot directly observe, but is inferred from other variables that are observable and measured directly.
Stability and consistency over time. The accuracy that a measure has in producing stable, consistent measurement.
The reliability of individual research scales...... Good 1,2,3,4,5,6,7 Bad(circle whihc one fits best to individual personality)
Testing the same thing on more than one occasion to make sure you get the same data.
Alternate forms reliability
Same thing as test retest, but you use each test as its own separate form of reliability.
Split half reliability
Involves computing two scores for each participant on the basis of one administration of the test. One of the scores comes from one half of the test and the other score comes from the other half.
Hoyt Analysis of variance reliability
internal reliability estimates. Another method of split halves.
Cronbachs alpha reliability
1,2,3,4,5 are flipped on the results... 5= good, 1=bad
the degree to which the instrument measures what it is intended to measure.
approaches to validity:
face or content , criterion, predictive, concurrent, retrospective, construct or factual
threats to validity
inadequate preoperational explication of concepts, mono-operation bias, interaction of different treatments, interaction of testing and timing, restricted generalizability accross constructs.
item construction, length of the instrument, administration of the test.
A social scientific method for gathering quantifiable information about a specific group of people by asking the group members questions about their individual attitudes, values, beliefs, behaviors, knowledge, and perceptions.
A form containing a series of questions and mental measures that is given to a group of people in an attempt to gain statistical information about the group as part of a survey
is designed to find out how common a phenomenon is within a group of people. Ex: a researcher may be interested in communication apprehension accross the United States.... US is the group of people...
the purpose of this survey is to explain why people think or act as they do by identifying likely causal influences on their attitudes or behavior.
- the list of survey questions an interviewer reads an interviewee when conducting an oral survey...
- Paper and pencil methods= questionairre
- oral interviewing= interview schedule
5 steps to conducting a survey
- 1) pick your questions
- 2) create clear instructions
- 3) study design
- 4)Data processing and analysis
- 5) Pilot testing
Nominal, ordinal, interval, ratio, open ended)
- 2 ways: interviewing and self-administration
- Examples: face to face interviews, telephone interviews, mass administration, malied administration, internet administration.
Problems with survey research
Response rate, unit nonresponse(failure to obtain any survey measurements on a sample unit), item nonresponse(occurs when an individual participant fails to answer individual or groups of questions on a survey).... ALL CAN BE IMPROVED WITH CLEAR AND CONCISE DIRECTIONS,MAKING THE SURVEY EASY TO FILL OUT, KEEPING IT SHORT, A GOOD COVER LETTER...
When to use a survey: 4 questions
- 1) Do you know what you want to ask?
- 2) Do you need to collect data?
- 3) Does your audience know any information youre looking for?
- 4) Is your goal generalizability? (is it driven towards a population or a sample?)
a small scale test run for a planned piece of empirical research... They are used to make sure instructions are easy to follow and survey questions make sense to participants... When pilot testing: Use actual survey population members, anticipate survey context, test parts of the survey, determin a pilot sample size, and ask questions after someone completes the survey...
Cross sectional survey,
longitudinal survey design:1)trend design, 2)panel design,
accelerated longitudinal design
- Cross sectional survey- is used when a researcher wants to get information from a group of participants at a given point in time.
- longitudinal survey design- it allows you to make statements about variable order. 1st type: trend design- used to examine different samples of people at different points in time. 2nd type: Panel Design- When a researcher recruits a series of participants who agree to be surveyed periodically over a given amount of time.
- Accelerated longitudinal design- when a researcher wants to see how things change over a long period of time during a short period of time.
Threats to validity
- 1) history- events in the participants lives that occur during the experiment... ex: 9/11
- 2) maturation- any psychological or physiological changes taking place when the participants that occur with the passing of time regardless of the experimental manipulation
- 3)testing effects- changes in what is being measured brought about by reactions to the process of measurement.
- 4)instrumentation-unwanted changes in characteristics of the measuring instrument or in the measurement procedure.
- 5) attrition-losing participants in an experiment.
PRE EXPERIMENTAL DESIGNS
one shot case study, one group pretest postest design, static group comparison,
True Experimental Designs
the pretest postest control group design, postest only control group design, solomon four group design, factorial designs, quasiexperimental designs, non-equivalent control group designs.
selecting a random group of people throughout a table of numbers
what the experimentor changes in the the experiment
what remains unchanged by the experimentor
making sure the experiment is conducted as is... meaning there are no external factors messing with the results.... everything is controlled.
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