Methods for Data Analysis
Content analysis chapter 29
primary focus recorded information and people's relationship with it/focuses on features of recorded information – adopted as a useful information and library science science research technique
Use of content analysis
describes a message pool – identify relationships between message characteristics
infer or predict outcomes or effects of messages/characteristics of message content link to measures of human response/other measures
manifest and latent content
manifest observable and countable
latent content difficult/impossible to count – level of anxiety for example
content analysis concerned only with manifest content – validity may be questionable
Units of analysis
sampling units/recording units
sample from the overall population
recording units – elements of content that are coded, physical, conceptual, temporal
straightforward or not
Sampling senses – all members included
content analysis – obtain a representative sample which can be generalized to the population
Coding scheme development –instruction book and data collection instrument for use in conducting content analysis
first step identify the critical variables you wish to examine/grounded in prior research and theory step two choose or define indicators for those variables/what you will count and code for, often manifest content indicated presence of latent variables – require attention to validity reliability accuracy precision
indicators and content analysis take two forms definition of content characteristics (features it can be counted) and sets of categories or levels to record codes presenting the content of the message
Categories must be exhaustive – cannot be satisfied by other not present cannot determine
categories much be must be mutually exclusive – no recording should belong in more than one category of a particular variable
Multiple coders and inter-coder agreement
strives for objectivity and replicability
given same content, coding scheme and training, and he coders work should result in the same data. High measures of intercoder agreement indicate reliability of the results of the coding process
Analysis of coded data – numerical data to analyze
Computer supported content analysis
may not be appropriate for every content analysis/some better suited to computer supported analysis
Chapter 30 qualitative analysis of content
Content analysis primarily used in ILS as quantitative research method until recently/current studies use qualitative content analysis defined as
research method for the subjective interpretation of the content of text data through the systematic classification process of coding identifying themes or patterns
An approach of empirical methodical controlled analysis of text within the context of communication, following content analytic rules and step-by-step models, without rash quantification
any qualitative data reduction and sense making effort that takes a volume a qualitative material and attempts to identify core consistencies and meanings
Inductive versus deductive
qualitative content analysis – condense raw data into categories or themes based on valid inference and interpretation new paragraph three approaches to qualitative content analysis
conventional qualitative content analysis – coding categories derived directly in and deductively from the raw data
directed content analysis – coding starts with a theory or relevant research findings, researchers immerse themselves in the data and allow things to emerge
summative content analysis – starts with counting of words or manifest content/extensive analysis to include latent meanings and themes
Process of qualitative content analysis move back and forth between concept development and data collection
Involves a set of said systematic and transparent procedures for processing data
step one prepare the data
step to define the unit of analysis
step three develop categories in the coding scheme
step four tester coding scheme on a sample of text
step five code all the text
step six assess your coding consistency
step seven draw conclusions from the coded data
step eight reports your methods and findings
Computer support for qualitative content analysis
supported by computer programs such as NV IVO, AATLAS.TI
common purpose – assist researchers in organizing managing encoding qualitative data in an efficient manner
text editing, note and memo taking, coding, text retrieval and node/category manipulation
in corpse visual presentation module – see relationships between categories more vividly
recorded coding history to allow researchers to keep track of evolution of their interpretations
Trustworthiness
validity reliability and objective is to be our used to evaluate the quality of research in the conventional cost of the test research paradigm
credibility
adequate representation of constructions of the social world understudy
transferability – extent to which the researchers working hypothesis can be applied to another context
dependability – coherence of the internal process and the way the researcher calls for changing conditions of the phenomena
Qantas qualitative content analysis is a valuable alternative to more traditional quantitative content analysis – working in interpretive paradigm
identify important themes or categories within a body of content and provide a rich description
Chapter 31 discourse analysis
used to uncover other meanings that we negotiate in our everyday in professional interactions but are rarely made explicit within those interactions – analysis of discourse
Methods and tools – no analytic method at least is a term is understood elsewhere in social psychology – broad theoretical framework – attention on the constructive and functional dimensions of discourse
Instead of using scales or counting occurrences includes the works of theorists within ILS – suggest a number states there will be taken during discourse analysis (not set in stone/sometimes overlapping/completely absent)
First step construct your research question
step two select a sample of the course to study
next step collect records and documents
ready to code data – identifying themes within categories of emerge and take shape as you examine the tax
success predicted on close reading and rereading of the text
looking for nuance contradictions areas of vagueness or ambivalence next
final step validate your findings
Discourse analysis – strong proof/potential to explore passive inquiry to provide greater understanding/required rigor clarity and self inquiry
Chapter 32 analytic induction
induction is one of the main forms of scientific logic, best understood in contrast with other main form of scientific logic: deduction
deductive reasoning – logical process of arriving at specific facts it must be true given a broader sense that seem to be true
induction – logical process of arriving at a general conclusion by a examining set of specific backs
analytic induction – specific form of inductive reasoning to analyze qualitative data
Process of analytic induction
first formulate a rough description of the phenomena you want to explain
next develop a hypothetical explanation of the phenomena
derive your initial definition and hypothesis continue choosing cases and studying them to see if emergency of information is consistent with hypothesis
examination of cases
Challenges in using analytic induction
when to stop examining more cases/no were all negative cases may be lurking
analytic induction has been used relatively infrequently especially in ILS
Analytic induction is an analysis methods not been used in ILS studies yet – holds promise for application to research questions/often working with qualitative data
Chapter 33 descriptive statistics
describing a phenomena with statistics
variables and their levels of measurement
measures of central tendency
mean
median
mode
Choose which method of central tendency to use
Measures of dispersion
range and interquartile range
variance and standard deviation
confidence intervals
Summarize with simple statistics – include at least one measure of central tendency and one manner measure of dispersion to interpret findings to draw valid conclusions from the data
Friday basic charts and graphs for frequency distributions
Descriptive statistics are the most essential view of your study findings critical components of any report of any research
for each variable of interest you should report with a measure of dig central tendency (mean median or mode)
measure of dispersion (standard deviation, range or interquartile range)
starting point for understanding your findings
Chapter 34 frequencies cross tabulation and the chai – square statistic
frequency distributions
counts can be organized and displayed in a table referred to as the frequency distribution
two-way frequency distributions: cross tabulation tables – displays frequency of categories of a single variable
chai square statistic – test with the frequency distribution is likely to occur by chance
Visualizations of your data set
pie charts
bar charts
histograms
line graphs
box plots
lying with graphs
Worthwhile to report descriptive statistics on his findings included frequency distributions on key category of variables
reported in tables – frequency and relative frequency in each cell
row and column totals should be displayed
chai square statistics used to test statistical significance possible relationship between variables in a two-way table
graphical display of the frequency
clearly understand the study results and communicate to the audience
Chapter 35 analyzing sequence he of events
behaviors happen over time
coding the data
approaches to sequential event analysis
Mark of models: examining sequences step by step
optimal matching approaches: comparing whole sequences
visualizing sequences of events
Advantages and event to disadvantages to each – may go beyond your level of expertise
before making a decision about which approach to use in your study, examine that analysis of sequences of search moves
Sequence of events, not enough to know which occur most frequently – need to know the sequence of those actions and which sequences occur most frequently
step-by-step approach using simple first order Markov models and transition matrix, look for frequently occurring longer sequences, pattern analysis, similarity between sequences through optimal matching algorithms
Chapter 36 correlation
statistical analysis method that helps to examine the relationship between two variables
proportion of the variability in one variable explained in the variability of the other variable
if perfectly correlated explains variability
two variables not correlated – none of one is explained by the other
linearly really related and data are ordinal interval or ratio data
Selecting the appropriate correlation statistic
some cautions for interpreting the correlation coefficient
Many research questions are oriented to discovering whether there is a relationship between two variables, how strong the relationship is
whether one variable in some cases causes the other
correlation cannot directly address – provide evidence of a relationship between the two
Chapter 37 comparing means: T tests and analysis of variance
compare different groups in terms of the mean ( average)
two statistical techniques – the t-test and an alley analysis of variance ANOBA
comparing two groups the t-test – probability a difference between two means
interpreting the results of the t-test
statistically significant results
nonsignificant results
Comparing multiple groups
more than two groups to compare
interpreting the results
Many research questions in information and library science of all making comparisons – comparing the effectiveness/performance
calculated amine for each group or entity you want to compare
variability across different samples and within each sample
likelihood indifference hold true across entire population
examine the size of the difference
Chapter 38 putting it all together conclusion
starting over from the beginning
some examples of how study can be put together financial winding your way through this book
providing evidence to promote progress in chapter information and library science
Friday, April 14, 2017
Unit 7 - Data Analysis Techniques / Connaway & Powell, Chapter 9
Analysis of Data
"A knowledge of basic statistics is imperative to research producer and consumer in library science
statistical analysis is concerned with development and application of methods and techniques for organizing and analyzing data quantitatively so that the reliability of conclusions based on the data may be evaluated objectively in terms of probability" – page 261
Types of statistics – theoretical/applied
Role of statistics – four basic purposes
1. – statistics show central point around which a mass of data revolves
2. – show how broad or diverse the spread can be in a mass of data
3. – reveal how closely or distantly certain features within the mass of data are related/relationship?
4. – degree to which the facts have been incurred by mere chance/probability question mark influenced by some factor other than pure chance
Cautions – cannot interpret
necessary for studies involving quantitative data/crucial at the sampling and analysis stage
study should not be dictated by the statistical techniques to follow
Steps involved in statistical analysis
establishment of categories take place before the data are gathered
four basic rules or guidelines
1. set of categories or values – derived from a single classificatory principle
2. each set of category should be exhaustive – possible to place every observation in one of categories of the set
3. each set category should be mutually exclusive – not possible to place a specific observation correctly and more than one category
4. based on sound knowledge of the site of subject matter and anticipation of likely responses
Coding the data
categories have been established and data assigned to them – necessary to convert the new data responses to numerical codes – tabulated or tallied
original responses numerical? – Do not need to be assigned new codes
Important consideration: Reliability / poorly worded question near item may not produce enough relevant information for the purposes of the study
asked more than one question
problems with the categories may lead to lack of reliability
important that coders are adequately trained and important to verify or check the accuracy of their work
researchers transcribed observations or scores for each individual or case
from the data collection to coding or transfer sheets
analyze the data manually
if large mass of data/statistical techniques to be employed were relatively complex and time-consuming to conduct – use a computer to analyze the data
After data has been prepared and saved to electronic file – analyzed using a software program
Analyzing the data date – descriptive statistics
descriptive statistics – predominant type of data analysis employed by researchers in library and information science
six basic functions
1. indicate how many persons objects scores or whatever achieved each value – frequency distributions/reported in tables
2. difficult to grasp the overall meaning of a frequency distribution tables? Pictorial representations – pretrip variety of characteristics of cases or individuals
indent paragraphs charts pie charts histograms frequency polygons
graphs are useful with a large number of cases
3. – typical – recent measures of central tendency
mean median mode
4. how widely the cases in a group very
dispersion or variability
mean aviation – arithmetic mean of the absolute difference of each score from that mean
standard deviation – square root of the arithmetic mean of the square deviations from the mean
variance – the mean squared aviation
standard deviation is one of the most frequently used measures of dispersion – most difficult to comprehend
5. measure the relationship – correlation or associational statistics
cross tabulation or by via direct frequency – products of tables in which two variables have been cross classified – table consists of rows and columns of one variable labels for the rows of another variable
finally compare percentage differences
6. basic function – descriptive statistics can perform is to describe the difference between two or more groups of individuals
special case – showing the relationship between variables
measures of central tendency
Analyzing the data – inferential statistics
"A knowledge of basic statistics is imperative to research producer and consumer in library science
statistical analysis is concerned with development and application of methods and techniques for organizing and analyzing data quantitatively so that the reliability of conclusions based on the data may be evaluated objectively in terms of probability" – page 261
Types of statistics – theoretical/applied
- theoretical equal mathematical aspects of statistics
- applied involves the practical application of statistics
Role of statistics – four basic purposes
1. – statistics show central point around which a mass of data revolves
2. – show how broad or diverse the spread can be in a mass of data
3. – reveal how closely or distantly certain features within the mass of data are related/relationship?
4. – degree to which the facts have been incurred by mere chance/probability question mark influenced by some factor other than pure chance
Cautions – cannot interpret
necessary for studies involving quantitative data/crucial at the sampling and analysis stage
study should not be dictated by the statistical techniques to follow
Steps involved in statistical analysis
establishment of categories take place before the data are gathered
four basic rules or guidelines
1. set of categories or values – derived from a single classificatory principle
2. each set of category should be exhaustive – possible to place every observation in one of categories of the set
3. each set category should be mutually exclusive – not possible to place a specific observation correctly and more than one category
4. based on sound knowledge of the site of subject matter and anticipation of likely responses
Coding the data
categories have been established and data assigned to them – necessary to convert the new data responses to numerical codes – tabulated or tallied
original responses numerical? – Do not need to be assigned new codes
Important consideration: Reliability / poorly worded question near item may not produce enough relevant information for the purposes of the study
asked more than one question
problems with the categories may lead to lack of reliability
important that coders are adequately trained and important to verify or check the accuracy of their work
researchers transcribed observations or scores for each individual or case
from the data collection to coding or transfer sheets
analyze the data manually
if large mass of data/statistical techniques to be employed were relatively complex and time-consuming to conduct – use a computer to analyze the data
- Optical scanning for producing data flow files
- direct data entry may not be the most common technique for creating data files
- computer program displays each question on the screen
- prompts the researcher to input the response directly on the screen – and into the computer file
- database management program to control data entry
- statistical programs now enable downloading a computer datafile rent tearing data directly into the program using the data editor
- program prompts the person entry data for each response
- checks the response to ensure its valid
- phase response of the property to file
After data has been prepared and saved to electronic file – analyzed using a software program
Analyzing the data date – descriptive statistics
descriptive statistics – predominant type of data analysis employed by researchers in library and information science
six basic functions
1. indicate how many persons objects scores or whatever achieved each value – frequency distributions/reported in tables
2. difficult to grasp the overall meaning of a frequency distribution tables? Pictorial representations – pretrip variety of characteristics of cases or individuals
indent paragraphs charts pie charts histograms frequency polygons
graphs are useful with a large number of cases
3. – typical – recent measures of central tendency
mean median mode
4. how widely the cases in a group very
dispersion or variability
mean aviation – arithmetic mean of the absolute difference of each score from that mean
standard deviation – square root of the arithmetic mean of the square deviations from the mean
variance – the mean squared aviation
standard deviation is one of the most frequently used measures of dispersion – most difficult to comprehend
5. measure the relationship – correlation or associational statistics
cross tabulation or by via direct frequency – products of tables in which two variables have been cross classified – table consists of rows and columns of one variable labels for the rows of another variable
finally compare percentage differences
6. basic function – descriptive statistics can perform is to describe the difference between two or more groups of individuals
special case – showing the relationship between variables
measures of central tendency
Analyzing the data – inferential statistics
Sunday, April 9, 2017
Unit 6 Data Collection Methods / Wildemuth 18-28
Wildemuth Chapters 18-28 Methods for Data Collection
Chapter 18 – transaction logs
Chapter 19 – think aloud protocols
Chapter 20 - direct observation
Chapter 21 - participant observation
Chapter 22 - research diaries
Chapter 23 - unstructured interviews
Chapter 24 - semi structured interviews
Chapter 25 - focus groups
Chapter 26 - survey research
Chapter 27 - measuring cognitive and affective variables
Chapter 28 - developing new measures
Chapter 18 – transaction logs
Chapter 19 – think aloud protocols
Chapter 20 - direct observation
Chapter 21 - participant observation
Chapter 22 - research diaries
Chapter 23 - unstructured interviews
Chapter 24 - semi structured interviews
Chapter 25 - focus groups
Chapter 26 - survey research
Chapter 27 - measuring cognitive and affective variables
Chapter 28 - developing new measures
Unit 6 Data Collection Techniques / Connaway & Powell, Chapter 5 Data Collection Techniques
Connaway & Powell, Chapter 5 Data Collection Techniques
Wildemuth Chapters 18-28 Methods for Data Collection
There are different types of questionnaires
factual
opinion
information
self perception
many more
Interviews –
structured
unstructured
semi structured
Considerations for good design of questionnaires and interviews
Scaled responses:
Qualities of good interviewing and good interviewers
Difference between individual interviews and focus group interviews
When appropriate to use questionnaires rather than interviews
appropriate to use interviews rather than questionnaires
research study
Different types of observational research
Usability testing at an introductory level
Transaction logs and their analysis
Think aloud protocols
specific examples
Research diaries/specific examples
Advantages and disadvantages of each technique
Measure cognitive and affective variables
describe specific examples of written and edited introductory level
Wildemuth Chapters 18-28 Methods for Data Collection
There are different types of questionnaires
factual
opinion
information
self perception
many more
Interviews –
structured
unstructured
semi structured
Considerations for good design of questionnaires and interviews
Scaled responses:
Qualities of good interviewing and good interviewers
Difference between individual interviews and focus group interviews
When appropriate to use questionnaires rather than interviews
appropriate to use interviews rather than questionnaires
research study
Different types of observational research
Usability testing at an introductory level
Transaction logs and their analysis
Think aloud protocols
specific examples
Research diaries/specific examples
Advantages and disadvantages of each technique
Measure cognitive and affective variables
describe specific examples of written and edited introductory level
Tuesday, March 28, 2017
Unit 5 Research Designs and Sampling, Chapter 13 Sampling for Extensive Studies
Wildemuth chapter 13
sampling for extensive studies – conclusion to draw from your research will hold apply to a particular set of people or organizations
a sample was collected to study and plan to generalize your findings to all the people organizations representative sample
practice of sampling is intended to achieve greater efficiency/if done properly results in a more accurate findings because you can control your data collection procedures and other aspects of the procedure preferably
– Design your sampling procedures carefully/the selection of particular people organizations to participate will these does set of rules that reply to a carefully defined population
Went to selected a probability sample from the population of interest it needs to be reviewed subsequently
probability sampling: concepts and definitions: the population is the collection of elements but which we wish to make an inference – an element is an object in which a measurement is taken
the sampling frame is a list of all agile eligible elements in the population
Probability sample has two specific characteristics first every element of the fibrillation of interest has a known nonzero probability of being selected into the sample
the second important characteristic elements be selected randomly at some point in the process
does not mean haphazard selection Newmont line selection based on the theory of probability that will help you avoid bias in your sample
Procedures for selecting a probability sample
first define your population of interest then define more carefully those who have you something within the past year three times within the year different definition I fear population of interest will depend on your goal
Compromise the definition of the study population to make your sampling plan possible to carry out
define your target population and actual study population
Unit of analysis – could be individuals or could be groups or organizations that unit of analysis you are making clear which elements are eligible for inclusion
Identify or construct the sampling frame – list of elements in the study of population
matches the definition of your study population – missing some portion?
May be complete the contact information or inaccurate or duplications this list is flawed – you will be implicitly redefining your population
Finally select particular animal elements from the sampling frame – identifying specific people – probability sample you use
simple random sampling single sampling frame all the elements in it will have equal chance of being selected, generate a set of random numbers, work through the random numbers and to get the sample size you need
Systematic sampling
extremely tedious and error-prone instead use systematic sampling – random numbers chosen to select the first element from their every and element is selected and varies based on the size of the sample
should not be used if the frames order has some type of pattern it could introduce bias into the sample
Stratified sampling
population is divided into strata 1st/elements a random list sampled from each stratum – may be possible to use a smaller's sample size take your research more efficient
Define the strata – selective variable to divide your population
second decide in the number of strata
finally decide in the sample size
Cluster sampling
sampling units are groups begin the sampling procedure by randomly selecting a sample of clusters
in single stage cluster sampling you would then include in your sample all the elements within the selected clusters
minimize the cost of collecting data from a geographically widespread sample
Nonprobability sampling: concepts and definitions
Not possible to select a random sample from the population which interested? Not possible to identify create a valid sampling frame? Some type of nonprobability sample will need to be defined
Quota sampling
quota for each level of each characteristic/recruiting people until you have met your quota – resemble stratified sampling/no random selection of elements
Purposeful purposive sampling
Judgment sample relies on expert judgment of the person selecting the sample
based on their individual characteristics, a sample that is representative of the population in terms of both central tendency in range of characteristics – include both a typical staff member and more extreme cases/potential for bias
Snowball sampling
particularly sensitive or when eligible belt members may be particularly difficult to identify – ask each participant for suggestions for additional people
forward to five more people
Convenient sampling
try to recruit people because they are available
characteristics of the people who are eligible for study participation
Sample size
as efficient as possible – recruit and collect data from a small sample as possible
don't use a standard method parens (local tradition, at least enough, data availability, intuition, experience, negotiations) – confidence interval around the estimate – area can tolerate any parameter estimates – no fix criteria for setting acceptable confidence interval balance the importance of the accuracy
Special case usability testing special case five or six users can identify 80% of the usability problems homes there are – larger sample size significant portion of the usability problems
The effects of nonresponse in a sample
some of the participants you selected will not accept your invitation response rates only 63% – oversample to increase the number/will not necessary help avoided by a sample
Use appropriate data/use wording/well-designed questionnaires are study procedures = gift certificates cash other items
sampling for extensive studies – conclusion to draw from your research will hold apply to a particular set of people or organizations
a sample was collected to study and plan to generalize your findings to all the people organizations representative sample
practice of sampling is intended to achieve greater efficiency/if done properly results in a more accurate findings because you can control your data collection procedures and other aspects of the procedure preferably
– Design your sampling procedures carefully/the selection of particular people organizations to participate will these does set of rules that reply to a carefully defined population
Went to selected a probability sample from the population of interest it needs to be reviewed subsequently
probability sampling: concepts and definitions: the population is the collection of elements but which we wish to make an inference – an element is an object in which a measurement is taken
the sampling frame is a list of all agile eligible elements in the population
Probability sample has two specific characteristics first every element of the fibrillation of interest has a known nonzero probability of being selected into the sample
the second important characteristic elements be selected randomly at some point in the process
does not mean haphazard selection Newmont line selection based on the theory of probability that will help you avoid bias in your sample
Procedures for selecting a probability sample
first define your population of interest then define more carefully those who have you something within the past year three times within the year different definition I fear population of interest will depend on your goal
Compromise the definition of the study population to make your sampling plan possible to carry out
define your target population and actual study population
Unit of analysis – could be individuals or could be groups or organizations that unit of analysis you are making clear which elements are eligible for inclusion
Identify or construct the sampling frame – list of elements in the study of population
matches the definition of your study population – missing some portion?
May be complete the contact information or inaccurate or duplications this list is flawed – you will be implicitly redefining your population
Finally select particular animal elements from the sampling frame – identifying specific people – probability sample you use
simple random sampling single sampling frame all the elements in it will have equal chance of being selected, generate a set of random numbers, work through the random numbers and to get the sample size you need
Systematic sampling
extremely tedious and error-prone instead use systematic sampling – random numbers chosen to select the first element from their every and element is selected and varies based on the size of the sample
should not be used if the frames order has some type of pattern it could introduce bias into the sample
Stratified sampling
population is divided into strata 1st/elements a random list sampled from each stratum – may be possible to use a smaller's sample size take your research more efficient
Define the strata – selective variable to divide your population
second decide in the number of strata
finally decide in the sample size
Cluster sampling
sampling units are groups begin the sampling procedure by randomly selecting a sample of clusters
in single stage cluster sampling you would then include in your sample all the elements within the selected clusters
minimize the cost of collecting data from a geographically widespread sample
Nonprobability sampling: concepts and definitions
Not possible to select a random sample from the population which interested? Not possible to identify create a valid sampling frame? Some type of nonprobability sample will need to be defined
Quota sampling
quota for each level of each characteristic/recruiting people until you have met your quota – resemble stratified sampling/no random selection of elements
Purposeful purposive sampling
Judgment sample relies on expert judgment of the person selecting the sample
based on their individual characteristics, a sample that is representative of the population in terms of both central tendency in range of characteristics – include both a typical staff member and more extreme cases/potential for bias
Snowball sampling
particularly sensitive or when eligible belt members may be particularly difficult to identify – ask each participant for suggestions for additional people
forward to five more people
Convenient sampling
try to recruit people because they are available
characteristics of the people who are eligible for study participation
Sample size
as efficient as possible – recruit and collect data from a small sample as possible
don't use a standard method parens (local tradition, at least enough, data availability, intuition, experience, negotiations) – confidence interval around the estimate – area can tolerate any parameter estimates – no fix criteria for setting acceptable confidence interval balance the importance of the accuracy
Special case usability testing special case five or six users can identify 80% of the usability problems homes there are – larger sample size significant portion of the usability problems
The effects of nonresponse in a sample
some of the participants you selected will not accept your invitation response rates only 63% – oversample to increase the number/will not necessary help avoided by a sample
Use appropriate data/use wording/well-designed questionnaires are study procedures = gift certificates cash other items
Unit 5 Research Designs and Sampling, Chapter 12 experimental studies
Wildemuth chapter 12
variables are manipulated and their effects upon other variables observed – basic definition provides a strong framework for understanding primary characteristics of a to experiment
1st some variables called independent variables are manipulated – you must exert control over their variation to be sure you can understand the effects of their understudy
independent variables are the input to the experiment
2nd of their those effects and other variables (call dependent variables) the dependent variable understood as the output from the experiment
characterized by control – all possibilities for variation are either controlled or they are varied systematically
three experimental designs
pretest protest control group design
posttest only control group design
factorial design
Randomization
the validity of your experiment – threats to internal validity
threats to external validity
Additional issues
experimental setting: lab or field
within versus between subjects designs
ethical issues in experiments
Principles of experimentation are simple: randomly's assigned the subjects to two or more groups and measure the outcomes resulting from the activation experienced age group
move beyond the basic characteristics of peer experiments you find their number of decisions to make about design
crucial that you understand your research question thoroughly and stated clearly
identify which independent variable should be manipulated and which dependency variables should be measured
variables are manipulated and their effects upon other variables observed – basic definition provides a strong framework for understanding primary characteristics of a to experiment
1st some variables called independent variables are manipulated – you must exert control over their variation to be sure you can understand the effects of their understudy
independent variables are the input to the experiment
2nd of their those effects and other variables (call dependent variables) the dependent variable understood as the output from the experiment
characterized by control – all possibilities for variation are either controlled or they are varied systematically
three experimental designs
pretest protest control group design
posttest only control group design
factorial design
Randomization
the validity of your experiment – threats to internal validity
threats to external validity
Additional issues
experimental setting: lab or field
within versus between subjects designs
ethical issues in experiments
Principles of experimentation are simple: randomly's assigned the subjects to two or more groups and measure the outcomes resulting from the activation experienced age group
move beyond the basic characteristics of peer experiments you find their number of decisions to make about design
crucial that you understand your research question thoroughly and stated clearly
identify which independent variable should be manipulated and which dependency variables should be measured
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