Prepared by: Assoc. Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti Putra Malaysia Serdang
At the end of this session, each student should be able to: 1. Define statistics, 2. Describe basic statistical concepts, 3. Understand the four scales of measurements, and 4. Match statistics to the appropriate scale of measurement
As a tool in making decisions: Research objectives Research hypotheses Statistics as a process to: Collect DATA Analyze Present Make informed decisions Interpret
Problem Statement Research Design/ Methodology Population & Sample Instrumentation Data Collection Data Analysis & Presentation Interpretation & Reporting
A collection of tools and techniques that are used to convert data into meaningful information. Data Statistical Tools Descriptive Inferential Information Role of Statistics
Depends on: 1. Purpose Descriptive Inferential 2. Assumption on normality Parametric Nonparametric 3. Number of Variables Univariate Bivariate Multivariate
Population Comprises ALL elements individual or objects Parameters Sample A sub-set of population Statistics
Measures Parameter Statistic Number of cases N n Mean μ Ȳ Variance σ 2 s 2 Standard deviation σ s Correlation coefficient ρ r
1. Probability samples Simple random samples Stratified random samples Systematic samples Cluster samples 2. Non-probability samples Convenient samples Purposive samples
Characteristics studied that assume different values for different elements Demography: Gender Job tenure Occupational status Job characteristics: Work condition Job demand Job control Quality of work life Perceived quality of ICT facilities Independent Variables Dependent Variable Research Conceptual Framework
OR M M Demography: Gender Job tenure Occupational status X Mediator Y X Moderator Y Job characteristics: Work condition Job demand Job control Perceived quality of ICT facilities Career commitment Quality of work life Independent Variables Intervening Variable Dependent Variable Research Conceptual Framework
1. Quantitative or Continuous Variables A variable that can be measured numerically (Numeric) Can be classified into: Discrete variable Continuous variable 2. Qualitative or Categorical Variable A variable that cannot assume a numerical value but can be classified into 2 categories (Alpha numeric)
Basic elements used in statistical analysis Variable Data Primary Secondary
1. Experiments 2. Telephone survey 3. Mail questionnaires 4. Online questionnaires 5. Direct observation 6. Personal interviews
Nominal The lowest scale Numbers assigned to identify attributes No order/sequence Ordinal Numbers assigned in ranking order Arrange from lowest to highest or vice versa Interval Arbitrary zero (no absolute zero) Zero does not represent absence of the characteristic Ratio The highest scale True zero (represents absence of the characteristic)
RATIO INTERVAL Absolute ZERO Arbitrary ZERO ORDINAL Sequence of ATTRIBUTES NOMINAL List of ATTRIBUTES
(For Categorical Nominal and Ordinal) Variable Ethnicity Attributes Malay Chinese Indian Values 1 2 3
(Two-step questions) Data Alpha Numeric What is the data type? Numeric Sequence? Zero? Nominal Ordinal Interval Ratio
Exercise: What are the scales of measurement for these variables? 1. Program of study 2. Speed (km/hr) 3. Motivation scores 4. Income categories 5. Number of SMS received 6. Marital status 7. Quality of work life scores 8. Socio-economic status 9. Perception scores 10. Membership status Nominal Ratio Interval Ordinal Ratio Nominal Interval Ordinal Interval Nominal
Major Research Concerns Describe Phenomenon Frequency/Percent MCT MD Comparison between Groups T-Test ANOVA Mann-Whitney Kruskal Wallis Relationship between Variables Chi-square Spearman rank correlation Pearson PM correlation Regression Analysis
Data # Groups/ Indetype Question variables Scale pendence Statistics Nonmetric Multiple categories Chi-Square Two groups Independent Dependent Ind. t-test Paired t-test DATA Metric Differences Relationships Multiple groups or variables Two variables Independent Dependent Metric Rank Dichotomous One-way ANOVA Factorial ANOVA Repeated- Measure ANOVA Pearson s r Spearman s r Point biserial Multiple variables Multiple regression
Scales of Measurement Statistics Dependent Independent T-Test Interval/Ratio Nominal/Ordinal (k=2) ANOVA Interval/Ratio Nominal/Ordinal (k>2) Chi-square Nominal/Ordinal Nominal/Ordinal (At least one of the scales is Nominal) Spearman Rho Rank Ordered Rank ordered Interval/Ratio Interval/Ratio (x Normal) Pearson Correlation Interval/Ratio Interval/Ratio Regression Interval/Ratio Interval/Ratio
Statistic 1. Purpose 2. Requirements 3. Assumptions 4. Run the analysis in SPSS 5. Present results of the analysis 6. Interpretation