Basic data analysis
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Basic Data Analysis. Tabulation. Frequency table Percentages. A Typical Table. Type of Measurement. Type of descriptive analysis. Nominal. Cross Tabs Mode. Type of Measurement. Type of descriptive analysis. Ordinal. Rank order Median. Type of Measurement. Type of

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Basic Data Analysis

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Basic data analysis

Basic Data Analysis


Tabulation

Tabulation

  • Frequency table

  • Percentages


A typical table

A Typical Table


Basic data analysis

Type of

Measurement

Type of

descriptive analysis

Nominal

Cross Tabs

Mode


Basic data analysis

Type of

Measurement

Type of

descriptive analysis

Ordinal

Rank order

Median


Basic data analysis

Type of

Measurement

Type of

descriptive analysis

Interval

Arithmetic mean


Cross tabulation

CROSS-TABULATION

  • Analyze data by groups or categories

  • Compare differences

  • Percentage cross-tabulations


A typical cross tab table

A Typical Cross-Tab Table


Data transformation

Data Transformation

  • A.K.A data conversion

  • Changing the original form of the data to a new format

  • More appropriate data analysis

  • New variables

    • Summated

    • Standardized


Degrees of significance

Degrees of Significance

  • Mathematical differences

  • Statistically significant differences

  • Managerially significant differences


Testing the hypotheses

Testing the Hypotheses

  • The key question is whether we reject or fail to reject the hypothesis.

  • Depends on the results of the hypothesis test

    • If testing differences between groups, was the difference statistically significant

    • If testing impact of independent variable on dependent variable, was the impact statistically significant

  • How the hypothesis was worded


Differences between groups

Differences Between Groups

  • Primary tests used are ANOVA and MANOVA

  • ANOVA = Analysis of Variance

  • MANOVA = Multiple Analysis of Variance

  • Significance Standard:

    • Churchill (1978) Alpha or Sig. less than or equal to 0.05

  • If Sig. is less than or equal to 0.05, then a statistically significant difference exists between the groups.


Example

Example

  • Hypothesis: No difference exists between females and males on technophobia.

  • If a statistically significant difference exists, we reject the hypothesis.

  • If no s.s. difference exists, we fail to reject.


Example1

Example

  • Hypothesis: Males are more technophobic then females (i.e., a difference does exist)

  • If a statistically significant difference exists, and it is in the direction predicted, we fail to reject the hypothesis.

  • If no s.s. difference exists, or if females are statistically more likely to be technophobic, we reject the hypothesis.


Testing for significant causality

Testing for Significant Causality

  • Simple regression or Multiple regression

  • Same standard of significance (Churchill 1978)

  • Adj. R2 = percentage of the variance in the dependent variable explained by the regression model.

  • If Sig. is less than or equal to 0.05, then the independent variable IS having a statistically significant impact on the dependent variable.

  • Note: must take into account whether the impact is positive or negative.


Example2

Example

  • Hypothesis: Technophobia positively influences mental intangibility.

  • If a technophobia is shown to statistically impact mental intangibility (Sig. is less than or equal to 0.05), AND.

  • The impact is positive, we fail to reject the hypothesis.

  • Otherwise, we reject the hypothesis.


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