Within-Case Displays: Exploring, Describing and Predicting

Definition

Description: Making complicated things understandable by reducing them to their component parts

Explanation: Making complicated things understandable by showing how their component parts fit together according to some rules- as Theory- Including a range of activities: providing requested information or descriptions

Prediction: Making about the probable evaluation of case events or outcomes over the following months or years

Ladder of Analytical Abstraction

Summarizing and packaging the data- Creating a text to work on: Reconstruction of interview tapes as written note and Trying out coding categories to find a set that fits

Repacking and aggregating the data- Identifying themes and trends in the data overall: Searching for relationship in the data

Developing and Testing propositions to construct and explanatory framework- Testing hypotheses and reducing the bulk of the data for analysis and Integrating the data into one explanatory framework

Display Format

There are several formats to display the data:

– Matrices: “Crossing” of two lists, set up as rows and columns

– Networks: “Nodes” or points connected by lines

Display Type

There are several types of Partially ordered display:

– Time-ordered: Understanding the flow and sequence of events and processes

– Role-ordered display: Understanding the role the people who live in groups and organizations

Type of Good Explanation

Explanatory Effects Matrix is first step in the direction of answering such questions:

– Why were these outcomes achieved?

– What caused them- either generally or specifically?

The matrix helps to understand things temporally

It looks at outcomes or results of a process, and turn to the “what leads to what”

It is hard to see the links between assistance types, roles and effects

Causal Networks: Pull together independent and dependent variables and their relationships into a coherent picture

It is useful to describe the meaning of the connections among factors

Causal Map

For the causal network, the researcher should proceed the causal map:

Consider a specific case for which your data

Translate the pattern codes into variables

Rate the variable

Draw a line between pairs of variables

Draw a directional arrow

Prediction

For predicting, the researcher should follow the procedures:

Generating the prediction: The analyst predicted that the project would be phased out gradually in the following year

Justifying the prediction: How do you puzzle out the “why” of a prediction?

Analyzing the feedback: How good is the prediction? How good are the supporting and spoiling factors?

Cross-Case Displays: Exploring, Describing and Predicting

Strategies for Cross-Case Analysis

There are three strategies for Cross-Case Analysis:

Case-oriented strategies

– A theoretical framework is used to study on case in depth

– Check whether the pattern found matches with the previous cases

Variable-oriented strategies

– Focus on variables and their relationship

Mixed strategies

– Combine or integrate case-oriented and variable-oriented

Working Principle

Understand the case: Understand each particular case before proceeding to cross-case explanations

Preserve case configurations: Must be protected the network of conditions during analysis

Combine variable-oriented and case-oriented strategies

Methods for Cross-Case Analysis

There are several methods for Cross-Case Analysis for both of case-ordered and time-ordered:

Meta-matrices:

– Assembling descriptive data from each of several cases in a standard formant

– Inclusion of all relevant data

A case-ordered meta-matrix: The case are ordered according to the main variable

A time-ordered meta-matrix: columns organized sequentially by time period

Scatter plots

– Display data from all cases on two or more dimensions

Scatter plots over case: Data from cases are scaled carefully, and the cases positioned in the space formed by respective “axes”

Scatter plots over time: Scatter plots can be useful when they display similar in cases over time periods

Case-ordered effects matrix:

– Sorts the cases by degrees major cause being studied, and shows the diverse for each case

– The focus is on out-coming dependent variable

Case-ordered Predictor-Outcome matrix:

– Array cases on a main out-coming criterion variable, and provide data for each case

Variable-by-Variable matrix:

– Includes two main variables in its rows and columns; specific indicators of each variable are ordered by intensity

Causal Model:

– A network of variables with causal connections among them, drawn from multiple-case analyses

– The Principle is one of theory building

– Place causes and effects in a linear chain: Causal Chain

Reference: Qualitative Data Analysis