dummy table

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Making dummy tables : 

By Lucell Larawan Making dummy tables

introduction : 

Part of the research proposal requirement is the dummy table/s which is part of the appendices. It shows the table that will appear in the data analysis portion without the figures yet. It assumes that data are not gathered yet. The purpose is to give a guide for the researcher and give an idea on what tables to present. introduction

Univariate analysis : 

As a rule, each specific objective (for quantitative analysis) would need at least one table. The kind of table will depend on the measurement scale of the variable. Univariate analysis

Slide 4: 

Specific objective: To determine the teachers’ level of innovativeness in each academic unit of CPU. (variable “innovativeness” is interval which needs either mean, median or mode type of data analysis) Dummy table: (F test value appears below if needed to compare between groups. Scales of interpretation also appears as an index.) Table 1. Teacher’s level of innovativeness per academic unit in CPU.

Slide 5: 

Objective: to determine the CPU teachers’ level of innovativeness as a whole. Dummy table: (scales of interpretation appears as index) Table 1. CPU teachers’ level of innovativeness as a whole.

Slide 6: 

Scales of interpretation should usually appear below tables which give mean scores that correspond to a certain interpretation. To create scales of interpretation, let us say the responses to questions about innovativeness is 1 to 5 (Likert scale). Solve for the intervals suppose you will create three possible interpretations: high, moderate or low. To do this determine the highest Likert scale (which is 5) and subtract it with the lowest (which is 1). The result is 4. Divide 4 by the number of possible interpretations of scores (which is 3: high, moderate, low) and this will be 1.33. Make 1 as the lowest in the first scale then add 1.33 to make the start of the next row (resulting to 2.33). Then add 1.33 to put the start of the next row. Adjust figures to complete. The result which will appear as index of the table is as follows:

Slide 7: 

Scales: Interpretation: 3.66– 5.00 High 2.33 – 3.65 Moderate 1.00 – 2.66 Low From the scales we created, a mean of 2.80 in the real data, for instance, would mean “moderate” innovativeness.

Slide 8: 

Specific objective: To rank the reasons why Company X does not attract better talents. (variable is reasons why Company X does not attract talents; measurement scale is ordinal) Dummy table: Table 1. Reasons why Company X does not attract better talents

Slide 9: 

Specific objective: to determine the different forms of collaboration between the CCII firms and the academe. (variable: forms of collaboration) Dummy table: Table 1. Distribution of respondents according to forms of collaboration with academic institutions practiced by CCII (multiple response).

Bivariate analysis : 

In this type of analysis, the contingency table is used for nominal and nominal or between ordinal and nominal variables analyzed simultaneously. As a rule, the rows present the dependent variable while the columns present the independent variable. Categories under each variable will appear based on the questionnaire categories. Bivariate analysis

Slide 11: 

Specific objective: To determine the association between sex and general well-being of CPU staff members. (independent variable: sex; dependent variable: general well-being) Cramer’s V =

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