QDA Miner 是一套易於使用於編碼、註釋、擷取和分析大小集合的文件甚至是圖像資訊的混合模型的質性數據分析套件軟體。QDA Miner 常用於團體焦點訪談逐字稿、合法資料、雜誌文章甚至是整本書的分析。或者可以運用在繪圖、照片、繪畫及任何形式的視覺資料。它能與 SimStat 統計數據分析工具軟體，而且跟 WordStat 質性分析和文本採礦組元軟體緊密的集合，使您能夠靈活的分析文本以及包括數值和分類數據的相關結構性資訊。
誰會使用 QDA MINER？
QDA MINER 質性數據分析軟體，可適用於需要從大型或小型的文黨和圖像的集合中，進行編碼文字或圖片、註釋、搜尋、探索和提取信息的任何人，包括：
● 商業智能分析師、市場研究人員、民調人員和 CRM 專業人員
QDA Miner 新版特色
WHAT’S NEW IN VERSION 5.0
1. IMPORTATION FROM NEW SOURCES
2. IMPORTATION AND MONITORING OF TWITTER, FACEBOOK, AND RSS FEEDS
3. DOCUMENT OVERVIEW
4. LINK ANALYSIS FEATURE
6. INTERACTIVE PLOT OF DATA POINTS
KEY AND UNIQUE FEATURES
QDA Miner offers higher levels of computer-assistance for qualitative coding, analysis, and report writing than any other qualitative data analysis software on the market today:
● Intuitive ON-SCREEN CODING AND ANNOTATION OF TEXTS AND IMAGES with features offering greater flexibility and ease-of-use, such as code splitting, merging, easy resizing of coded segments, interactive code searching and replacement or virtual grouping.
● Flexible MEMOING AND HYPERLINKING features to annotate documents and images and connect various pieces of qualitative evidence by creating links to other coded segments, cases, documents, files, or web sites.
● Advanced GEOTAGGING AND TIME-TAGGING tools to associate geographic and time coordinates to text segments or graphic areas, retrieve coded data based on time or location and plot events in space and time, create dynamic maps and interactive timelines.
● Unique COMPUTER ASSISTANCE FOR CODING with more than seven text search tools including keyword search, section retrieval, a powerful query-by-example search tool that learns from the user, and a unique cluster extraction and coding tool.
● Flexible CODING RETRIEVAL TOOLS for extracting coded segments associated with specific codes or code patterns and identifying coding co-occurrences, coding sequences and assessing relationships between coding and numerical or categorical properties.
● Integrated STATISTICAL AND VISUALIZATION tools, such as clustering, multidimensional scaling, heatmaps, correspondence analysis and sequence analysis, allow one to quickly identify patterns and trends, explore data, describe, compare and test hypotheses.
● Unprecedented TEAMWORK SUPPORT with flexible multi-user settings, a powerful merge feature for bringing together coding, annotations, reports, and log entries of multiple coders as well as an INTER-RATERS AGREEMENT assessment module for assessing coding reliability.
● A unique REPORT MANAGER tool allows to store queries and analysis results, tables, graphs, research notes and quotes in a single location. Its outliner design is ideal for organizing findings and interpretations, assisting qualitative researchers in the report-writing process.
● Command log A powerful COMMAND LOG keeps track of every project access, coding operation, transformation, query, and analysis performed. It may be used to document the qualitative analysis process and supervising teamwork. It represents a detailed audit trail that helps ensure the transparency of the qualitative research process and enhances its credibility.
|LITE V1.0||FULL V4.1|
|IMPORTATION OF DOCUMENTS|
|MS Word, RTF, Plain txt, HTML||V||V|
|Excel, MS Access, tab delimited||V||V|
|Image files (bmp, jpg, png, ewmf)||V||V|
|PDF files (text only)||V||V|
|PDF filles with formatting and image||X||V|
|SPSS, Atlas.ti, HyperResearch, Transana & Transcriber files||V||V|
|Reference Information System (RIS) files||V||V|
|Document splitting and variable extraction (Document Conversion Wizard)||X||V|
|Importation from ODBC databases||X||V|
|Export tables to text, MS Word, Excel, CSV, TAB delimited, SPSS, XML, or HTML||V||V|
|Export charts to image files (PNG, BMP, JPG, WMF)||V||V|
|Export project to XLS, XML, dBase, etc.||X||V|
|Export code statistics to Excel, SPSS, CSV, TAB delimited or XML||X||V|
|Export coded segments to a new project||X||V|
|Export documents (all at once) to RTF or plain text files||V||V|
Backup and restore
|Add and delete cases||V||V|
|Append documents / Images||V||V|
|Case description and grouping||V||V|
|Append cases from data file||X||V|
|Transform variables’ type||V||V|
|Variable statistics and charting||X||V|
|Append from a data file (other project, XLS or SPSS files)||X||V|
|Transform coding into variables||X||V|
|Add, delete and edit codes||V||V|
|Split & merge codes||X||V|
|Virtual grouping of codes||X||V|
|Combine codes (creating compound codes)||X||V|
|Import codebook from another project||X||V|
|Assign codes to text segments or images||V||V|
|Attach memos (or comments)||V||V|
|Search and replace codings||V||V|
|MIXING QUALITATIVE AND QUANTITATIVE DATA
The interoperability of QDA Miner, WordStat and SimStat allows researchers to integrate numerical and textual data into a single project and to seamlessly move back and forth between quantitative and qualitative data analysis. More importantly, the integration of structured and unstructured data into a single data set allows one to explore connections between text responses and numerical or categorical data. For example, one can easily analyze relationships between closed and open-ended survey questions, identify social media topic trends over time, find out how supporters’ comments differ from those of detractors, or easily explore differences in word usage between gender, age groups, or geographic locations.
INTEGRATING VARIOUS TEXT ANALYTICS TECHNIQUES
There are many ways to extract information from textual data. Techniques such as in-depth qualitative data analysis, powerful exploratory text mining, or carefully designed content analysis are just a few examples of the range of approaches available today. Each text analysis method has its own strengths and weaknesses, and no single method is appropriate for all text analysis tasks. Yet, most text analysis tools on the market today rely on a single approach to text analysis. At Provalis Research, we are committed to offering a software platform that does not confine researchers and analysts to a single approach, but allows them to choose the one that best fits the research question or the available data.
We also strongly believe that a single text analysis task often profits from combining several methods; our software platforms facilitates this type of integration. One may thus combine in many ways the automatic content analysis and text mining features of WordStat with the manual coding an annotation available in QDA Miner.
WHAT IT IS USED FOR?
WordStat can be used by anyone who needs to quickly extract and analyze information from large amounts of documents. Our content analysis and text mining software is used for:
● Content analysis of open-ended responses, interview or focus group transcripts
● Business intelligence and competitive web sites analysis
● Information extraction and knowledge discovery from incident reports, customer complaints
● Content analysis of news coverage or scientific literature
● Automatic tagging and classification of documents
● Fraud detection, authorship attribution, patent analysis
● Taxonomy development and validation
KEY AND UNIQUE FEATURES
● Powerful CONTENT ANALYSIS AND TEXT MINING SOFTWARE for handling large amounts of unstructured information. WordStat can process up to 20 million words per minute and identify all references to user-defined concepts using categorization dictionaries.
● Text Mining and Visualization Tools Integrated EXPLORATORY TEXT MINING AND VISUALIZATION TOOLS such as clustering, multidimensional scaling, proximity plots, and more, to quickly extract themes and automatically identify patterns.
● RELATES UNSTRUCTURED TEXT WITH STRUCTURED DATA such as dates, numbers or categorical data for identifying temporal trends or differences between subgroups or for assessing relationship with ratings or other kinds of categorical or numerical data.
● Use existing or create your own HIERARCHICAL CONTENT ANALYSIS DICTIONARIES OR TAXONOMIES composed of words, word patterns, phrases as well as proximity rules (such as NEAR, AFTER, BEFORE) for achieving precise measurement of concepts.
● COMPUTER ASSISTANCE FOR DICTIONARY BUILDING with tools for extracting common phrases and technical terms and for quickly identifying in your text collection, misspellings, synonyms, antonyms and related words.
● One click access toKEYWORD-IN-CONTEXT AND KEYWORD RETRIEVAL TOOLS for easy identification and coding of relevant text segments, validation of content analysis dictionaries, word-sense disambiguation or for drilling down to the source documents.
● Seamless integration with a state of the art QUALITATIVE CODING TOOL (QDA Miner), allows more precise exploration of data or more in-depth analysis of specific documents or extracted text segments when needed.
● MACHINE LEARNING FOR AUTOMATIC DOCUMENT CLASSIFICATION using Naive Bayes and K-Nearest Neighbours algorithms with automatic features selection and validation tools. Classification models may then be saved on disk and reapplied on new data.
● Easy IMPORTATION of databases, spreadsheets and documents (including PDF and HTML) as well as EXPORTATION of text analysis results to common industry file formats (Excel, SPSS, ASCII, HTML, XML, MS Word) and graphs (PNG, BMP and JPEG).
共有 SIMSTAT / MVSP 請點選觀看
Simstat supports not only numerical and categorical data, dates and short alpha-numeric variables but also memos and documents variables allowing one to store in the same project file responses to open-ended questions, interview transcripts, full reports, etc. Since all Provalis Research tools share the same file format, one can easily perform statistical analysis on numerical and categorical data using Simstat, perform qualitative coding on stored documents using QDA Miner or apply the powerful content analysis and text mining features of WordStat on those same documents. Moreover, the coexistence of numerical, categorical and textual data in the same data file gives a unique ability to explore relationships between numerical and textual variables or to compare qualitative codings or content categories between subgroups of individuals.
STATISTICAL ANALYSIS FEATURES
● DESCRIPTIVE statistics (mean, variance, standard deviation, etc.).
● FREQUENCY analysis including frequencies table, descriptive statistics, percentiles table, barchart, pie chart, Pareto chart, histogram, normal probability plot, box-&-whiskers plot and cumulative distribution plot.
● CROSSTABULATION: normal crosstabulation and INTER-RATERS AGREEMENT table,nominal statistics (chi-square, Pearson’s Phi, Goodman-Kruskal’s Gamma, Contingency coefficient), ordinal statistics (Kendall’s tau-b and tau-c, Pearson’s R, symmetric and asymmetric Somers’ D, Dxy and Dyx), inter-raters agreement statistics (percentage of agreement, Cohen’s Kappa, Scott’s Pi, Krippendorf’s r and R-bar, free marginal correction for nominal and ordinal measure), 3-D bar chart.
● BREAKDOWN analysis with multiple Box-and-Whiskers plot.
● MULTIPLE RESPONSES frequency and crosstabulation analysis.
● PAIRED AND INDEPENDENT T-TESTS with effect size measures (r and d), error bar graph, barchart, dual histogram.
● ONEWAY ANALYSIS OF VARIANCE with post hoc tests (LSD, Tukey’s HSD, Scheffé’s test), effect size measures, error bar graph, barchart, deviation chart.
● GLM ANOVA/ANCOVA (up to 5 factors and covariates) including detailed ANOVA table, 3 different adjustment methods for unequal cell sizes (regression, nonexperimental, hierarchical), multiple regression statistics, test of change of R-Square, regression equation (B, standard error of B, beta, confidence, interval of B, zero-order, semi-partial and partial correlations, tolerance level, F, significance), residuals caseplot with Durbin-Watson statistic, standardized residuals scatterplot, normal probability plot of residuals, ability to save predicted and residual values.
● CORRELATION MATRIX including covariance and cross product deviation, user-specified confidence interval, scatterplot matrix.
● PARTIAL CORRELATION MATRIX with interactive correlation matrix for inclusion of exclusion of control variables, computation of confidence intervals, etc.
● REGRESSION analysis including linear and 7 nonlinear regressions (quadratic, cubic, 4th and 5th degree polynomial, logarithmic, exponential, inverse), regression equation, analysis of variance, Durbin-Watson statistics, scatterplot, residuals caseplot, standardized residuals scatterplot, normal probability plot of residuals, ability to save predicted and residual values.
● MULTIPLE REGRESSION analysis including 5 different regression methods (hierarchical entry, forward selection, backward elimination, stepwise selection, enter all variables), P to enter, P to remove, and tolerance criteria, ANOVA table, test of change ANOVA table, regression equation (B, standard error of B, beta, confidence, interval of B, zero-order, semi-partial and partial correlations, tolerance level, F, significance), residuals caseplot, Durbin-Watson statistic, standardized residuals scatterplot, normal probability plot of residuals, ability to save predicted and residual values.
● TIME SERIES analysis including data transformation (ex.: remove mean, lag, etc.), auto-correlation diagnostic (ACF and PACF plot), smoothing techniques (moving average,running median), control bars with user-specified confidence interval.
SINGLE-CASE EXPERIMENTAL DESIGN analysis with descriptive statistics, interrupted time-series graph, various graphical judgmental aids such as smoothing (moving average and running median), trend lines and control bars.
● RELIABILITY analysis with item, inter-item and item-total statistics, split-half reliability statistics, internal consistency measures (Cronbach’s alpha, etc.).
● CLASSICAL ITEM ANALYSIS for multiple-choice item questionnaires.
● FACTOR ANALYSIS including principal components analysis and image covariance factor analysis, Q-type factor analysis, varimax rotation, scree plot, etc..
● SENSITIVITY ANALYSIS with false-positives and false negatives statistics, sensitivity and specificity statistics, ROC curve (Receiver operating characteristics), error rate graph.
● NONPARAMETRIC analysis including binomial test, one sample chi-square test, runs test, McNemar test, Mann-Whitney U test, Wilcoxon t-test, sign test, Kruskal-Wallis ANOVA, Friedman two way ANOVA, Kolmogorov-Smirnov test for 2 samples and goodness of fit test, Moses test of extreme reactions, median test (2 or more samples).
● NONPARAMETRIC ASSOCIATION MATRIX including Spearman’s R, Somer’s D, Dxy and Dyx, Goodman Kruskal’s Gamma, Kendall’s Tau-a, Tau-b, Kendall Stuart’s Tau-c, etc.
● BOOTSTRAP RESAMPLING analysis including resampling of 7 univariate and 21 bivariate estimators, descriptive statistics, percentile table, nonparametric confidence intervals, nonparametric power analysis, variable sample size, random sampling simulation, histogram.
● FULL ANALYSIS BOOTSTRAP resampling on almost every analysis (frequency, crosstab, multiple regression, reliability, nonparametric tests, etc.).
● Integer weighting of cases using another variable.
● Runs LOGISTIC, a freeware logistic regression program written by Gerard L. Dallal.
● SIMCALC probability calculator computes probabilities for 9 types of test/distribution as well as confidence intervals for proportions, mean, and correlation.
DATA MANAGEMENT FEATURES
The data window is a spreadsheet like data editor where values can be entered, browsed, or edited.
● Data file can store up to 2035 variables (or fields).
● Supports plain text as well as Rich Text Format documents.
● Imports comma or tab separated text files, DBase, FoxPro, Excel, MS Access, Paradox, Lotus, Quattro Pro, SPSS/PC+, and SPSS for Windows files.
● Exports comma or tab separated text files, DBase, FoxPro, Excel, MS Access, Paradox, Lotus, Quattro Pro, SPSS/PC+, and XML files.
● Allows merging and aggregation of data files.
● Supports variable and value labels and up to 3 missing values.
● Cases can be filtered using complex xBase expressions.
● Data grid may be sorted on one or several variables.
● Customizable grid provides alternate view of the data file
● Supports data transformation (including conditional transformation), recoding, ranking. Provides more than 50 transformation functions including trigonometric, statistical, random number functions.
OUTPUT MANAGEMENT FEATURES
The Notebook window displays the statistical output for all analysis performed during a session. The notebook metaphor provides an efficient way to browse and manage outputs.
● The text output of each analysis is displayed on a separate page.
● Each page can be annotated or edited.
● Empty pages can be inserted to put down ideas or remarks, sketch an analysis plan, or write down interpretation of results.
● Tabs can be added to create sections in the notebook allowing storage of different kinds of analysis in different sections of the notebook.
● An index of all analysis is automatically generated. This index can be used to quickly locate and go to a specific page, move pages within the notebook, or delete some pages.
● Rich Text notebook allows one to change font attributes (bold, underline, strikeout, italic) and font colors.
● Highlight tool allows to color passages of text.
● Notebooks can be exported in Rich Text Format or in plain text.
CHART CREATION AND MANAGEMENT FEATURES
All high-resolution charts created during a session are displayed in the Chart window. This window can be used to view the charts and perform various operations on individual charts or on the entire collection of charts. For example, you can modify the various chart attributes, save those charts to disk, export them to another application using the clipboard or disk files, or print them. It is also possible to delete a specific chart or to modify the order of those charts in this window.
● Control of axis, titles, legends, colors, lines, etc.
● Charts can be imported on disk or copied to the clipboard in Window bitmap or metafile format
● Charts can also be copied as tab separated values and imported by another charting application.
SCRIPT, EDUCATIONAL, & MULTIMEDIA FEATURES
The script window is used to enter and edit commands. Those commands can be either read from a script file on disk, typed in by the user or automatically generated by the program. When used with the RECORD feature, the script can also be used as a log window to keep track of the analysis performed during a session. Those commands may then be executed again, providing an efficient way to automate statistical analysis. Additional commands also allows one to create demonstration programs, computer assisted teaching lessons, and even computer assisted data entry.
● Statistical analysis, data filtering and transformation commands
● Record script feature to automatically generate commands corresponding to operations performed with menus and dialog boxes.
● Flow control features such as IF-THEN-ELSE statements, GOTO or GOSUB commands, RUN command to run external programs, etc.
● Can read, write, and perform mathematical operations on user defined variables or any database field
● Create menus, text boxes, input boxes, dialog boxes, multiple items questions, etc. (responses from a user can be stored in memory variables or in a data file)
● Multimedia features: play sound (.WAV), music (.MDI), and movie (.AVI) files, display graphics (.BMP) and text.
一旦您的數據進行了分析，您就可以直接繪製結果。選擇您想看到的協調軸 (the ordination axes) 和散點圖 (scattergrams) 的繪製。自動生成叢集分析 (cluster analyses)結果的樹狀圖 (Dendrograms)。而這些圖表可以在各種輸出設備所列印。
● Data matrix manipulation: data may be transposed, transformed (transformations available include logarithms to base 10, e, and 2, square root, and Aitchison’s logratio for percentage data), converted to percentages, proportions, standard scores, octave class scale, or range through format for stratigraphic studies, and rows and columns may be selected for deletion
● Data import and export; Lotus 1-2-3 and Symphony and Cornell Ecology Programs
● Principal Coordinates Analysis, performed with the following options: use any type of input similarity matrix, user defined minimum eigenvalues and accuracy level
● Principal Components Analysis, with the following options: correlation or covariance matrix, centered or uncentered analysis, user defined minimum eigenvalues, including Kaiser’s and Jolliffe’s rules for average eigenvalues, user defined accuracy level.
● Correspondence Analysis, with these options: Hill’s detrending by segments, choice of eigenanalysis or reciprocal averaging algorithm, weighting of rare or common taxa and scaling to percentages, user defined minimum eigenvalues and accuracy level.
● Nineteen different similarity and distance measures, including Euclidean, squared Euclidean, standardized Euclidean, cosine theta (or normalized Euclidean), Manhattan metric, Canberra metric, chord, chi-square, average, and mean character difference distances; Pearson product moment correlation and Spearman rank order correlation coefficients; percent similarity and Gower’s general similarity coefficient; Sorensen’s, Jaccard’s, simple matching, Yule’s and Nei’s binary coefficients.
● Cluster analysis, with the following options: seven strategies (UPGMA, WPGMA, median, centroid, nearest and farthest neighbor, and minimum variance), constrained clustering in which the input order is maintained (e.g. stratigraphic studies), randomized input order, integral dendrogram production. Separate utility program allows data matrices to be sorted in the order of the dendrograms; allows patterns to be seen in the data.
● Diversity indices, with the following options: Simpson’s, Shannon’s, or Brillouin’s indices, choice of log base, evenness and number of species can also be calculated.