Matrix Queries and Matrix Data Representations in NVivo 11 Plus
1 like1,798 views
The document provides an overview of matrix queries and data representations in NVivo 11, detailing their structure, types, and applications in qualitative research. It explains the significance of matrices in analyzing relationships and patterns between data elements and proposes various use cases for extracting sentiment and thematic insights. Furthermore, it outlines procedures for setting up matrix queries and visualizing data outcomes.
![ADDITIONAL QUESTIONS?
What are some other types of matrix queries possible based on your own research?
How would you set up your matrix query, and why? (Is there a manual equivalency to
a computerized matrix query? A computerized equivalency outside of NVivo? How
would that work (in either case)?)
What can matrix queries tell you that you could not find out otherwise? (Or if the
matrix query is not possible, what are some other ways to surface and discover the
same information?)
How would you present matrix query findings in a presentation? A research paper?
[When would you keep a matrix query’s findings on background (just for your
analysis)? When would you put a matrix query’s findings on foreground (in
publications and presentations for the public consumption)?]
52](https://image.slidesharecdn.com/matrixqueries-160415212605/85/Matrix-Queries-and-Matrix-Data-Representations-in-NVivo-11-Plus-52-320.jpg)
Matrix Queries and Matrix Data Representations in NVivo 11 Plus
- 1. MATRIX QUERIES AND MATRIX DATA REPRESENTATIONS IN NVIVO 11 PLUS NVivo Advanced (Updated)
- 2. OVERVIEW 1. Matrices and their basic structures 2. Types of elements (variables) for matrix comparisons 3. Setting up matrix queries in NVivo 11 4. Specific matrix “use cases” in qualitative and mixed methods research 5. Wrap-up 2
- 3. IF MULTILINGUAL…SELECTION OF A BASE LANGUAGE (“TEXT CONTENT LANGUAGE”) Go with default (determined by QSR International based on purchaser location in the world) OR NVivo ribbon -> File tab -> Info -> Project Properties -> General (sub) tab -> Text content language (dropdown menu) 3
- 4. 1. MATRICES AND THEIR BASIC STRUCTURES 4
- 5. DEFINITION(S) AND PURPOSES General Purposes Matrices are used to explore and show data relationships and patterns In NVivo, any defined element may be used to populate the respective matrices NVivo uses (intensity) matrices to report out on sentiment analysis autocoding findings NVivo 11 Plus uses (intensity) matrices to report out on theme and subtheme autocoding findings Structure “a rectangular array of quantities or expressions in rows and columns that is treated as a single entity and manipulated according to particular rules” (in a mathematics sense; in a computer science / information technology sense) (per Google search engine) a complex of lines intersecting at right angles (via rows and columns) 5
- 6. MATRICES ARE ABOUT RELATIONSHIPS Types of Common Relationships found in Matrices: hierarchical such as general to specific, topic to subtopic, within the set of, part of a type, contained within, found within included with, co-occurring with, associated with (some relationship but not fully defined) appearing in close proximity with, similar to, related by likeness different from (orthogonally unrelated) 6
- 7. MATRICES ARE ABOUT RELATIONSHIPS (CONT.) For example, some relationships described by matrices include: Which source documents (articles) are related to particular themes? Which themes and subthemes are related to which documents? Which terms from text sets are related to the four sentiment categories (very negative, moderately negative, moderately positive, and very positive)? What are similarities and dissimilarities between the coding of two coders (in a dyadic comparison in a coding comparison)? What are macro themes in a coding structure or codebook (through a matrix coding query)? 7
- 8. TYPES OF MATRICES Matrices are referred to by their application (matrix type), which includes the types of data and the analytical uses of that data Confusion matrix / contingency table or cross-tabulation analysis / error matrix (predicted values vs. actual empirical values) These are sometimes used to highlight the differences between Type I and Type II errors in basic signals detection Effects matrix Sentiment analysis intensity matrix (a temperature matrix, ~ to a color-saturation heatmap but in matrix format or with intensity indicated by number) Relational or network matrices (for relationships), and others Matrices are generically referred to sometimes by the numbers of their elements in their rows and columns (as in a 5 x 7 matrix, or a 2 x 2 matrix) 8
- 9. TYPES OF MATRICES(CONT.) Specific topical matrices are referred to by their main contents (one content type along its column headers and the other along its rows) For example: type-by-document matrices, document-by-theme matrices, and others 9
- 10. GENERAL FEATURES Matrices do not have to be symmetrical in terms of the labels on Column A1 and Row 1A (in the next diagram) The numbers of entities (column and row headers) in the rows and columns do not have to match; they do not have to pair either (but may depending on the type of matrix) Data may be incomplete, and matrices still have informational value (they are robust even in the condition of missing data) Tables tend to be more structured, with unique records on each row (running horizontally) and variables at the top of each column (running vertically) Matrices may be automatically extracted (by computer); they may be manually created 10
- 11. BASIC STRUCTURE A 1 Data CellsMatrix Variables (Rows) Matrix Variables (Columns) 11
- 12. MATRIX INDICATORS (DEFINED ROW AND COLUMN LABELS/HEADERS) Variables may be… Of a similar kind or type (in the matrix): all nodes / case nodes (individuals to individuals, or organizations to organizations) / codes; all interviews (in various groupings); all (cross-referenced) responses to questions, etc. Of mixed kinds or types (across rows and columns, not within the cells): themes and research documents; physical locations and interview subjects; themes (terms) and interviews; themes (concepts) and categorical outcomes; individuals and organizations; time periods and themes; sources in NVivo (sources, memos, codes, transcripts, interviews, and others) In the data cells… Presence or non-presence of a relationship (1 or 0; a binary finding) Frequency of occurrence of relationship; “strength” of relationship (may be turned into a network graph); intensity Contents of matrix variable overlap in text (content) format; coded text 12
- 13. MATRIX CELLS Cells come at the intersection of respective column headers and rows (individual records) Matrices may be labeled “sparse” if there are more cells with 0s than with non- zeroes (whether in binary matrices or strength-of-relationship matrices) 13
- 14. …IN QUALITATIVE AND MIXED METHODS RESEARCH, MATRICES… Are word- or text-based; may include a quantitative aspect (usually frequency counts as an indicator of relationship strength) Text may be “raw” (primary source data; transcripts) or highly processed (edited research articles) Are based on variables (nodes, themes, interviewee / survey taker / focus group participant demographics and “characteristics” for grouping, and others) May be used at various scales: the micro (cell-level), meso (relational, dyadic, triadic / motif…), and macro (matrix-scale pattern) May be designed (1) based on a targeted question, (2) based on the need to surface leads for further exploration (such as a “text summarization” application), (3) based on pure exploratory discovery May contain single or multiple queries 14
- 15. 2. TYPES OF ELEMENTS (VARIABLES) FOR MATRIX COMPARISONS 15
- 16. VARIABLES FOR MATRIX COMPARISONS IN NVIVO 11 Any text (at the most atomistic level) Any groups of text or multimedia represented by text descriptors (folders of contents) Any codes (nodes, case nodes) Any groups of nodes Any “coded by” set (of codes / nodes) Coder-based comparisons Any attributes or variables Such as indicated by classification sheets Any categorical variables Any relationships Any models Any model items 16
- 17. THE RESEARCH PROCESS General Overview Literature review Research design Research instrument prototyping and design (or acquisition) and pilot testing Sampling Research Data collection Data cleaning Data analysis Write-up and presentation Possible Matrix Applications Text summarization (themes and documents) Relevant document identification (for close reading) in a literature review Pattern identification in interviews, surveys, notes, and codes Data visualization as relational network graphs (for analysis, for presentation) … and others 17
- 18. SOME TYPES OF “ASKABLE” QUESTIONS WITH MATRICES Are there instances of particular text in particular “searchable” documents (.PDF, .txt, .rtf, .doc, .docx, etc.)? Are there locational or spatial patterns in (textual) data? Are there temporal patterns in (textual) data? Are there topical or theme patterns in (textual) data? Are there similarities / differences between responses of individuals from different demographic or categorical or spatial or other groupings? (from interview, survey, focus group, or other similar types of data) Are there relationships between concepts? Individual entities? Group entities? 18
- 19. 3. SETTING UP MATRIX QUERIES IN NVIVO 19
- 20. DATA INGESTION IN PROPER FORMS Sizes of Contents All text ingested atomistically (the lowest “unit of analysis” or “record” or multi- media-based “object” such as an article or a memo) If a series of articles are all created as one text set, generally, the text set will be queried as one document (instead of a series of articles in a text corpus) Accessible Machine-Readable Text All scanned text as “searchable” or optical character recognition (OCR) text All video transcribed into machine-readable text All audio transcribed into machine-readable text All imagery alt-texted into machine-readable text 20
- 21. DATA PREPARATION General in NVivo All relevant research materials included and coded; clear data labeling, consistent naming protocols Various types of groupings (by folder, by node, by nickname, by classification variable, and others), without creating data redundancy (which skews text queries and text frequency counts and other types of analyses); may delete redundant text for data queries…or create new (sub) NVivo projects with select data for particular data queries Goal: all data fully exploited in clear ways Specific in NVivo Combined master file of group coded projects, with multiple user coded contents (for runs of interrater reliability) Relationships defined and linked Case node source classifications applied Models created 21
- 22. PATHS USED TO CONDUCT DATA QUERIES RESULTING IN DATA MATRICES NVivo ribbon -> Query tab -> Matrix Coding NVivo ribbon -> Query tab -> Coding Comparison NVivo ribbon -> Query tab -> Group Query (models, relationships, attributes, coding at) (…to live and interactive demos) 22
- 23. QUERIES FOLDER STORAGE Matrix queries are stored in the Queries folder… (unless saved elsewhere by the researcher) 23
- 24. RELATED DATA VISUALIZATION: MATRIX “CHART” 24
- 25. A RELATIONAL MATRIX TO A NETWORK GRAPH (READ ACROSS) A B C D E F G H I A -- B -- C -- D -- E -- F -- G -- H -- I -- 25
- 26. A RELATIONAL MATRIX TO A NETWORK GRAPH (CONT.) Node-link diagram (vertex-relationship diagram) 26
- 27. PATHS USED TO CONDUCT AUTO CODING RESULTING IN INTENSITY DATA MATRICES (IN NVIVO 11 PLUS) Theme and subtheme extraction / topic modeling Highlight source. (You can use CTRL + A to select all in a folder.) In ribbon, select Analyze tab. Click Auto Code Button. Select “Identify themes…” Proceed with the Auto Code Wizard… Sentiment extraction Highlight source. (You can use CTRL + A to select all in a folder.) In ribbon, select Analyze tab. Click Auto Code Button. Select “Identify sentiment…” Proceed with the Auto Code Wizard… 27
- 28. 28
- 29. 29
- 30. 30
- 31. 31
- 32. 4. SPECIFIC MATRIX “USE CASES”… In Qualitative and Mixed Methods Research 32
- 33. MACHINE-READING RESEARCH ARTICLES (OR OTHER TEXTS) FOR THEMES AND SUB-THEMES (TO SAVE ON HUMAN “CLOSE READING,” ESP. OF RELATIVELY “BIG DATA” CORPUSES USING AUTOCODING) Theme or Concept or Phenomena or Individual (keyword or phrase) Theme or Concept or Phenomena or Individual Theme or Concept or Phenomena or Individual Theme or Concept or Phenomena or Individual Research article (or source) #1 “ “ “ “ “ 33
- 34. COMPARING AND CONTRASTING RESEARCH SUBJECT RESPONSES BY CATEGORICAL GROUPINGS Sex Age Group Birthplace Ethnicity Income Level Marital Status Variable 34
- 35. COMPARING AND CONTRASTING RESEARCH SUBJECT RESPONSES BY (CATEGORICAL) OUTCOMES On-time Graduation Late Graduation Withdrawal Variable Variable Variable Variable 35
- 36. EXPLORING POTENTIAL LOCATIONAL OR SPATIAL PATTERNS Location #1 Location #2 Location #3 Location #4 Interview Subject #1 / or Nodes / or … etc. #2 #3 … 36
- 37. EXPLORING POTENTIAL TIME PATTERNS (LIKE CHANGES OVER TIME, LIKE PRE-POST EVENT TIME CHANGES) Time Period 1 Time Period 2 Time Period 3 Time Period 4 Variable Variable Variable … 37
- 38. IDENTIFICATION OF OVERLAPS IN CODIFIED THEMES (MATRIX CODING QUERIES) Node Node Node… Node Node Node… 38
- 39. OUTLIER CASE COMPARISONS Outlier Case A Outlier Case Z 39
- 40. COMPARING SENTIMENTS, EMOTIONS, ATTITUDES, AND BELIEFS Sentiments Emotions Attitudes Beliefs… Interviewee #1 … 40
- 41. CROSS-QUERY RESPONSE ANALYSIS (COMPARISONS AND CONTRASTS) Question 1 Responses Question 2 Responses 41
- 42. COMPARING CHANGES ACROSS TIME PERIODS (TYPES OF TIME: DISCRETE, PERIODIC, CONTINUOUS; SHORT-TERM VS. LONGITUDINAL) Time Period #1 Time Period #2 … Variables of a Type 42
- 43. INTER-RATER RELIABILITY (SIMILARITY/DIFFERENCE ANALYSIS) Coder A Coding Coder B Coding 43
- 44. AUTOMATED SENTIMENT ANALYSIS Positive Negative Very Positive Moderately Positive Moderately Negative Very Negative Tweetstreams Interviews Facebook Postings Survey Responses Organized by Topic Newspaper Articles on a Specific Topic Ad Hoc #Hashtag Discussions Others… 44
- 45. 45 ALSO CROSS-TABULATION ANALYSES (USED WITH NON-PARAMETRIC CATEGORICAL DATA) A 1 Data Cells with Counts Need to Calculate: (Observed Variables – Expected Variables)2 / Expected Variables Need to calculate chi-squared Need to calculate p (statistical significance level) Need to calculate degrees of freedom (df) = (banners -1)(stubs -1) Matrix Variables (“Stubs” / Row Headers) Matrix Variables (“Banners” / Column Headers) 45
- 46. ALSO CROSS-TABULATION ANALYSES (CONT.) aka contingency tables Contrasting what is expected (if there is nothing acting on the variables) vs. what is observed Results in associational observations (not causal ones), insufficient power to assert causation Chi-square analysis based on: raw number counts and percentages: (actual observations - expected observations)2 / expected observations Goodness-of-fit test (from pure randomness / null hypothesis to some form of non-randomness or patterning) Test of independence of variables (Of two categorical variables from one population: Is there any association between the two variables? Can the level or incidence of one be used as a possible predictor of the other variable?) 46
- 47. ALSO CROSS TABULATION ANALYSES (CONT.) Degrees of freedom (df) = (number of banners – 1)*(number of stubs – 1) df consists of the mean of the chi square distribution df is used to calculate statistical significance of a chi-square statistic and the (in)validity of the null hypothesis Minimum of 2x2 tables but may be much larger p-value (probability of obtaining a particular observed result ) A value used to assess statistical significance (p < .05, p < .01, or other) Will need to transfer table to another tool (Excel, Qualtrics) for the complete cross- tabulation analysis (by setting up a pivot table, calculating expected frequencies, calculating observed frequencies, frequency distributions, percentages of columns and of rows, etc. 47
- 48. WRAP-UP 48
- 49. WHERE MATRICES COME FROM Not all matrices come from matrix queries or matrix coding queries Some forms of autocoding (sentiment analysis, theme and sub-theme extraction) result in intensity matrices that are used to report out the findings From the matrices, various data visualizations may be created, including bar charts, hierarchy charts (treemap and sunburst in NVivo 11 Plus) 49
- 50. EXPORT OF MATRICES Matrices export out as .txt, .xl, and .xlsx formats Extracted data (highly portable) may be analyzed in other software tools and in other ways ~ to a data table, for some of the autocoded matrices: Column headers are variables Rows are records 50
- 51. 51
- 52. ADDITIONAL QUESTIONS? What are some other types of matrix queries possible based on your own research? How would you set up your matrix query, and why? (Is there a manual equivalency to a computerized matrix query? A computerized equivalency outside of NVivo? How would that work (in either case)?) What can matrix queries tell you that you could not find out otherwise? (Or if the matrix query is not possible, what are some other ways to surface and discover the same information?) How would you present matrix query findings in a presentation? A research paper? [When would you keep a matrix query’s findings on background (just for your analysis)? When would you put a matrix query’s findings on foreground (in publications and presentations for the public consumption)?] 52
- 53. ADDITIONAL QUESTIONS?(CONT.) How would you use some matrix data visualizations with data created in autocoded / auto-created ways? How would you represent the findings? Besides bar charts, what are some other ways to represent matrix data? What about relational matrices expressed as network graphs? (a very common visualization) 53
- 54. CONCLUSION AND CONTACT INFORMATION Dr. Shalin Hai-Jew Instructional Designer, iTAC 212 Hale / Farrell Library Kansas State University shalin@k-state.edu 785-532-5262 The presenter has no formal tie to QSR International. This slideshow was created as part of a training at Kansas State University. 54