Chapter 1: Introduction to Statistical Analysis

Statistics

It is a science dealing with the study of efficient methods of collecting and presenting information for a definite purpose and of effective and systematic methods of interpretation and the use of facts in making decision.

- Aids in decision making

- Summarizes or describes data

- Helps to forecast or predict future outcomes

- Aids in making inferences

- Helps in comparisons or establishing relationships

Importance of Statistics

1. Gathering of Data

2. Presentation of Data

3. Analysis of Data

4. Interpretation of Data

Steps in Conducting a Statistical Inquiry

1. Descriptive Statistics

2. Inferential Statistics

Types of Statistics

Descriptive Statistics

Are methods concerning collecting, describing, and analyzing a set of data without drawing conclusions (or inferences) about a larger group.

Descriptive Statistics

percentage, measures of central tendency and location, measures of variability, skewness, kurtosis

Inferential Statistics

Are methods concerned with analysis of a subset of data leading to predictions or inferences about the entire set of data.

Inferential Statistics

Examples:

hypothesis testing, t-test, simple linear correlation, ANOVA, chi-square, regression, time series analysis

1. Qualitative Data

2. Quantitative Data

Types of Data

Qualitative Data

It refers to the attributes or characteristics of the sample.

Quantitative Data

It is the numerical data gathered (counts or measures).

Discrete Data

It assumes finite or countable infinite values.

Continuous Data

It cannot take on finite values but values are related to points.

Nominal Data

Ordinal Data

Interval Data

Ratio Data

Levels of Measurement

Nominal Data

It labels variables without providing numerical value; consists of names, labels, or categories only.

Nominal Data

Example: letters, symbols, words, genders.

Ordinal Data

It follows a natural order; can be arranged but no indication of distance between positions; arithmetic cannot be performed.

Ordinal Data

Example: level of satisfaction, class standing.

Interval Data

They are variables with a natural order, equal intervals between values, no true zero.

Interval Data

Example: temperature.

Ratio Data

They are variables with a natural order, equal intervals, and a true zero.

Ratio Data

Example: weight, height.

Sampling Techniques

Experimental Design

Methods of Data Collection

Simple Random Sampling

Stratified Sample

Cluster Sample

Systematic Sample

Convenience Sample

Sampling Techniques

Simple Random Sampling

Every sample of specified size has the same chance of selection.

Stratified Sample

Population split into groups (strata), random sample taken from each stratum.

Cluster Sample

Population split into clusters, one or more clusters randomly selected, all individuals in chosen clusters are sampled.

Systematic Sample

List the entire population, randomly pick a starting point, take every nth value until sample size is reached.

Convenience Sample

Picking samples conveniently at hand (e.g., via social media).

Completely Randomized Experiment

Matched Pairs Design

Randomized Block Design

Factorial Design

Experimental Design

Completely Randomized Experiment

Individuals randomly placed into groups; one is the control group (no treatment).

Matched Pairs Design

Treatments given to two groups matched with each other in some way.

Randomized Block Design

Subjects grouped into blocks (similar in nature); blocks differ from each other.

Factorial Design

Two or more independent categorical variables (factors) with multiple treatment levels; tests effects of factors simultaneously on the dependent variable.

Observation Method

Data collected via observation by the observer or personally visiting the field.

Structured Observation

Unstructured Observation

Participant Observation

Non-participant Observation

Controlled Observation

Uncontrolled Observation

Types of Observation Method

Structured Observation

When observation is done by characterizing style of recording the observed information, standardized conditions of observation, definition of the units to be observed, selection of pertinent data of observation.

Unstructured Observation

It is done without any thought before observation.

Participant Observation

The observer is a member of the group being studied.

Non-participant Observation

The observer studies without informing participants.

Controlled Observation

A pre-arranged plan under controlled conditions (e.g., laboratory).

Uncontrolled Observation

Done in natural conditions to get spontaneous picture of life and persons.

Direct or Interview Method

It involves presentation or oral-verbal stimuli and reply in terms of oral-verbal responses. the interviewer asks questions, which are aimed at getting information required for the study to respondents.

Direct or Interview Method

Advantages: Can give complete info.

Disadvantages: Interviewer influence, possible dishonesty.

Indirect or Questionnaire Method

One of the easiest methods; questionnaires must be clear, precise, and attractive.

Indirect or Questionnaire Method

Advantages:

- Less expensive (can be distributed personally or by mail)

- Less time-consuming (can cover wider area quickly)

- Confidential responses possible

- Free from interviewer influence

Case Study Method

It is an intensive investigation of a specific unit.

- Researcher can study one or more units for his study purpose.

- Unit studied intensively in a minute detail.

Characteristics of a Case Study Method

- Government publications (central, state, local)

- Technical and trade journals

- Books, magazines, newspapers

- Reports from industry, banks, stock exchanges

- Research reports from scholars, universities, economists

- Public records

Methods of Collecting Secondary Data

- Reliability of Data

- Suitability of Data

- Adequacy of Dara

- Area Differences

Factors to be considered before using secondary data

Reliability of Data

who, when, which methods, at what time, etc

Suitability of Data

objective, scope, nature of original inquiry

Adequacy of Data

It is the level of accuracy

Area Differences

The data is not adequate for the study.