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Chapter 4 PRESENTATION AND ANALYSIS OF DATA

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Presentation, Analysis and Interpretation of data

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Data are collected often in raw form. These are then not useable unless summarized. The techniques of presentation in tabular and graphical forms are introduced. Some illustrations provided are real-world examples. Graphical presentations cover bar chart, pie chart, histogram, frequency polygon, pareto chart, frequency curve and line diagram. Data are often collected in raw form. These are then not useable unless summarized. There are certain guidelines for data summarization such as summarization-should be as useful as possible,-should represent data fairly, and-should be easy to interpret. After collection of data (primary or secondary), it is necessary to summarize them suitably and present in such forms as can facilitate subsequent analysis and interpretation. There are two major tools/techniques for presentation of data as follows:-Presentation in tabular form-Presentation in graphical form. 2.1 Tabular Presentation Data may be presented in the form of statistical tables. In one table only simple frequencies can be shown. Also, in the same table cumulative frequencies, relative frequencies, and cumulative relative frequencies can be shown. Relative frequencies and cumulative frequencies are defined as follows: Relative frequency: It means the ratio of the frequency in the category of concern to the total frequency in the reference set.

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Mar 29, 2019

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Chapter 4. Principles of Quantitative Research. Answering Questions. Quantitative Research attempts to answer questions by ascribing importance (significance) to numbers or sizes or reactions and results. Scientific Theory.

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• specific case
• quantitative case study
• few examples
• applied behavior analysis

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Chapter 4 Principles of Quantitative Research

Answering Questions • Quantitative Research attempts to answer questions by ascribing importance (significance) to numbers or sizes or reactions and results

Scientific Theory • Self correcting: Prevailing wisdom requires constant re-evaluation when new evidence appears. Each discovery reveals a tiny piece of a giant puzzle. • Science never proves anything, it just continues to add puzzle pieces to the big picture.

The Researcher • The researcher’s relationship with study participants can influence outcomes. • The researcher is always concerned with how various factors (including the nature of the relationship) affect study results.

Infractions • Sloppy thinking • Poor planning • Careless documentation • Tainting responses with unacknowledged bias

Pluralist Approach • Embrace qualitative and quantitative as best fits each particular situation • Acknowledge the value of more than one method of knowing what we need to know

Pros of Quantitative Research? • Clear interpretations • Make sense of and organize perceptions • Careful scrutiny (logical, sequential, controlled) • Reduce researcher bias • Results may be understood by individuals in other disciplines

Cons of Quantitative Research? • Can not assist in understanding issues in which basic variables have not been identified or clarified • Only 1 or 2 questions can be studied at a time, rather than the whole of an event or experience • Complex issues (emotional response, personal values, etc.) can not always be reduced to numbers

Scientific Attitudes • Empirical Verification through observation or experimentation • Ruling out simple explanations prior to adopting complex ones • Cause-Effect • Probability of response • Replication of response

Six Types • Experimental • Survey • Meta-Analysis • Quantitative Case Study • Applied Behavior Analysis • Longitudinal

Experimental Research • Compare two or more groups that are similar except for one factor or variable • Statistical analysis of data • Conditions are highly controlled; variables are manipulated by the researcher “The effects of” “The influence of…”

Survey Research • Use set of predetermined questions • Collect answers from representative sample • Answers are categorized and analyzed so tendencies can be discerned

Meta-Analysis • Numerous experimental studies with reported statistical analysis are compared • Distinguishes trends • Effect size (the influence of the independent variable on the dependent variable) can be compared

Case Study • Also called single case design • Describes numerically a specific case (can be group or individual) • May test or generate hypotheses • Results often presented with tables and graphs

Applied Behavior Analysis (ABA) • One person • Examine the individual’s responses in different situations (conditions) across time • Results are usually depicted with tables and graphs • Conclusions based on data in these forms of presentation

Longitudinal • Individual or group research conducted across time • Few examples in MT literature • Subject attrition is major problem • Preserving confidentiality is also difficult • Specific standardized tools may change over time

Hypothesis • Hypothesis = an idea that will be tested through systematic investigation • A researcher’s prediction of what outcomes will occur • More clearly stated in research of 10 years ago than now • Fits experimental research, also called “Hypothesis Testing”

Independent Variable • The variable that is controlled or manipulated by the researcher • The variable that is thought to have some effect upon the dependent variable • The one difference between the treatment (experimental) and control groups

Dependent Variable • That which is measured • The outcome • That which is influenced or affected by the dependent variable

Reliability • The ability of a measurement tool to yield consistent results over time or under similar conditions

Content Validity • The extent to which the items on a testing tool (that being used to measure the dependent variable) reflect all of the facets being studied • All aspects are sampled (e.g. aural skills final exam)

Criterion-Related Validity • Also called Predictive Validity • The extent to which a testing tool yields data that allow the researcher to make accurate predictions about the dependent variable

Construct Validity • The extent to which the testing tool measures what it is supposed to measure • Relationship between the items on the tool and the dependent variable • Also relates to actual (physical) construction of a written tool (e.g. Dean’s Survey) and how this impacts the accuracy of the results

Internal Validity • Relates to the internal aspects of a study and their effect on the outcome: researcher planning and preparation judgment control for potential confounding variables

External Validity • Relates to the extent to which findings can generalize beyond the actual study participants • “How valid are these results for a different group of people, a different setting, or other conditions of testing, etc.?”

Objective Evaluation • Rigorous • Expository • Time Consuming

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