MSS 905 Methods of Missiological Research - PowerPoint PPT Presentation

1 / 22
About This Presentation
Title:

MSS 905 Methods of Missiological Research

Description:

Title: Slide 1 Author: J MOSTERT Last modified by: Johan Created Date: 1/23/2006 3:22:10 AM Document presentation format: On-screen Show (4:3) Company – PowerPoint PPT presentation

Number of Views:93
Avg rating:3.0/5.0
Slides: 23
Provided by: JMOS1
Learn more at: http://www.agts.edu
Category:

less

Transcript and Presenter's Notes

Title: MSS 905 Methods of Missiological Research


1
MSS 905 Methods of Missiological Research
  • Introduction to Statistics in Research

2
Introductory Concepts
  • Empiricism using direct observation to obtain
    knowledge
  • Empirical research acquiring knowledge by using
    scientific observational techniques
  • Experimental research using dependent and
    independent variables to identify causal
    relationships in experimental and control groups
  • Nonexperimental research are also called
    descriptive studies (no treatments historical
    analysis, case study, program evaluation, etc)

3
4 Levels of Quantitative Measurement (NOIR)
  1. Nominal (categorical) lowest, least precise,
    different categories (religious affiliation,
    gender, state)
  2. Ordinal difference plus categories can be
    ranked in order from high to low (height, weight,
    favorite subjects)

4
4 Levels of Quantitative Measurement (NOIR)
  1. Interval plus it can specify the amount of
    distance between categories (differences in
    inches between ten individuals, miles between
    five cities)
  2. Ratio plus true zero, can do proportions or
    ratios (not important difference from interval)

5
4 Levels of Quantitative Measurement (NOIR)
  • What are the following?
  • Strongly agree/agree/disagree/disagree
  • Racial categories?
  • IQ scores?
  • Temperature?
  • Freshman, sophomore, junior, senior?

6
Types of Statistics
  1. Descriptive to summarize data
  2. Corelational special subgroup of descriptive
    statistics that describe the relationship between
    two or more variables for one group of
    participants
  3. Inferential tools that can tell us how much
    confidence we can have when we generalize
    findings from a sample to a population

7
Descriptive Statistics
  • f stands for frequency, or the number of cases or
    times a number or attribute appears a score of
    17 (f 23) appeared the most
  • N is the number of participants (N 78)
  • Frequency distribution the shape of a set of
    scores
  • Normal distribution a bell shaped curve
  • Skewed distribution (positive and negative)

8
Descriptive Statistics
  • X (pronounced Xbar) the mean, the most
    frequently used average
  • Median is an alternative average it has 50 of
    the cases above it and 50 below the middle
    point
  • Mode is the most frequently occurring score in a
    distribution

9
Descriptive Statistics
  • Variability refers to the differences among
    scores of participants
  • Measures of variability are a group of statistics
    that are designed to describe the amount of
    variability in a set of scores
  • Range the simplest statistic to indicate
    variability (the difference between the highest
    and the lowest scores)

10
Descriptive Statistics
  • Standard deviation the most frequently used
    measure of variability (dispersion, spread of
    scores)
  • It provides an average of how far all the
    participants scored away from the mean
  • The more they differ from the mean of their group
    the higher the standard deviation
  • Standard deviation can be plotted on a normal
    curve (see handout)

11
Types of Statistics
  1. Descriptive to summarize data
  2. Corelational special subgroup of descriptive
    statistics that describe the relationship between
    two or more variables for one group of
    participants
  3. Inferential tools that can tell us how much
    confidence we can have when we generalize
    findings from a sample to a population

12
Corelational Statistics
  • Correlation refers to the extent to which two
    variables are related across a group of
    participants (eg SAT scores and first-year GPA in
    college spiritual maturity scores and church
    attendance)
  • Can be positive or negative or orthoganal
  • NOT causal (for that you need an experimental
    design)
  • Pearson r correlation coefficient (-1.00 to 1.00)

13
Types of Statistics
  1. Descriptive to summarize data
  2. Corelational special subgroup of descriptive
    statistics that describe the relationship between
    two or more variables for one group of
    participants
  3. Inferential tools that can tell us how much
    confidence we can have when we generalize
    findings from a sample to a population

14
Types of Statistics
  1. Descriptive to summarize data
  2. Corelational special subgroup of descriptive
    statistics that describe the relationship between
    two or more variables for one group of
    participants
  3. Inferential tools that can tell us how much
    confidence we can have when we generalize
    findings from a sample to a population

15
Inferential Statistics
  • Population all the members of a group of
    interest to a researcher
  • Sample a representative group of members from
    the total population
  • Null hypothesis that the true difference between
    the mean scores of two groups is zero (ie. there
    is no difference)
  • Has to be rejected first before an alternative
    hypothesis can be entertained

16
Inferential Statistics
  • Null hypothesis actually says that the observed
    difference between the means of two sets of
    scores was due to sampling error
  • Significance tests (like t Test) are applied to
    the data that yield a probability that the null
    hypothesis is true (p)
  • plt.05 (probability is only 5 in 100)
  • Plt.01 (probability is only 1 in 100)

17
Inferential Statistics
  • When you test the null hypothesis to determine
    the difference between means
  • Use a t Test if there are two means
  • Use a F Test if there are two or more means to be
    tested (this is referred to as the ANOVA or
    analysis of variance)

18
Scales
  • A data measure that captures the intensity,
    direction, level or potency of a variable
    construct along a continuum
  • Often used in survey research
  • Most scales are at the ordinal level of
    measurement

19
Scales
  • Likert Scale
  • Widely used in survey research
  • Ordinal-level measure of attitude
  • Examples on p. 208, Box 7.8
  • Minimum of 2 categories, but normally 4 to 8
  • Sometimes reverse scored to avoid response set
    (tendency to agree with every question)
  • Can be used to form an index of opinion

20
Scales
  • 2. Thurstone scaling
  • Group of judges rank many items into piles along
    a continuum
  • Seldom used due to limitations
  • Bogardus Social Distance Scale
  • Measures the social distance separating ethnic
    groups
  • Example p. 213, Box 7.11

21
Scales
  • Semantic Differential
  • An indirect measure using polar opposite
    adjectives or adverbs
  • Subjects indicate their feelings by marking the
    spaces between these opposites (7.12)
  • Guttman Scaling
  • A cumulative scale to determine whether
    relationships exist among indicators
  • A scale based on data that has been collected
    already, to determine if a hierarchical pattern
    exists among responses

22
Hypothesis and Causality
  1. Research has proved only in journalism,
    advertisements, courts of law not in scientific
    language (TV Dr. Jarvic, or eharmony.com)
  2. Rather evidence supports, or confirms the
    hypothesis
  3. Other ways to state a causal relationship (Box
    6.7, p. 163)
Write a Comment
User Comments (0)
About PowerShow.com