metopen 2 - analisa data dan hipotesis 16 mei 2011 [compatibility mode]

8/6/2019 Metopen 2 - Analisa Data Dan Hipotesis 16 Mei 2011 [Compatibility Mode]

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Metodologi Penelitian

ANALISIS DATA

DANUJI HIPOTESIS


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Proposal Penelitian (23 Mei 2011)

Abstrak

I. Pendahuluan: Latar Belakang; Rumusan Masalah;

Batasan Penelitian; Tujuan Penelitian

II. Landasan Teori: Teori Model Penelitian Hi otesis

III. Metodologi: Objek; Populasi dan Sampel Penelitian;

Uji Hipotesis (Formula dan Cara Uji Hipotesis); Flow

Chart Penelitian

Referensi


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(1) Identify a broad area of study. What is the general area of research?

(2) Select the research topic. What is the central research question?

(3) Decide the approach. What is the general philosophical position of

the research?

7 Steps of Research

(4) Formulate the plan. What is the project plan, or research design?

(5) Collect the data or information. What quantitative and/or

qualitative data should be collected?

(6) Analyze and interpret the data. What methods of analysis are being

applied to quantitative and qualitative data analysis?

(7) Present the findings. Are the findings supportable? In other words,

are they valid?


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Data Types

Order Interval Origin

Nominal none none none

r na yes unequa none

Interval yes equal or none

unequal

Ratio yes equal zero


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Validity

Face Validity

Content Validity : content validity (also known aslogical validity) refers to the extent to which a measure

.

Criterion-Related Validity: measure of how wellone variable or set of variables predicts an outcome

based on information from other variables

Construct Validity: refers to whether a scalemeasures or correlates with the theorized scientific

construct that it purports to measure.


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Reliability

Stability

Test-retest

Equivalence

Parallel forms

Internal Consistency

Split-half

Cronbachs alpha


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Approaches to Hypothesis Testing

Classical Statistics

sampling-theory approach

objective view of probability

decision makin rests on anal sis of available sam lin

data

Bayesian Statistics

extension of classical statistics

consider all other available information


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Types of Hypotheses

Null

that no statistically significant difference exists between

the parameter and the statistic being compared

Alternative logical opposite of the null hypothesis

that a statistically significant difference does exist

between the parameter and the statistic being

compared.


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Logic of Hypothesis Testing

Two tailed test

nondirectional test

considers two possibilities

directional test

places entire probability of an unlikely outcome to the

tail specified by the alternative hypothesis


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Testing for Statistical Significance

State the null hypothesis

Choose the statistical test

Select the desired level of significance

Compute the calculated difference value

Obtain the critical value

Interpret the test


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Classes of Significance Tests

Parametric tests

Z or ttest is used to determine the statistical

significance between a sample distribution mean and a

population parameter

Assumptions:

independent observations

normal distributions

populations have equal variances

at least interval data measurement scale


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Classes of Significance Tests

Nonparametric tests

Chi-square test is used for situations in which a test for

differences between samples is required

Assum tions independent observations for some tests

normal distribution not necessary

homogeneity of variance not necessary

appropriate for nominal and ordinal data, may beused for interval or ratio data


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Parametric Test


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Non-parametric Test


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Multivariate

Analysis

metopen 2 - analisa data dan hipotesis 16 mei 2011 [compatibility mode]

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