Unit 7 Data Analysis Techniques / Wildemuth, Chapters 29-37

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

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

• 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

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

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

Unit 5 Research Method, Design and Sampling Conclusion of readings

Remaining readings from Unit 5