Research

• It is the science of collecting, organizing,

  presenting, analyzing, and interpreting numerical data

  to assist in making more effective decisions.

  Statistics
• What refers to numerical information in its more common usage?

  Statistics
• It is a characteristic of a sample (mean, standard deviation, and variance)

  Statistic
• It is a datum that can be represented numerically.

  Statistic
• A collection of more than one figure is called?

  Statistics
• Why study statistics?

  • To scientifically measure conditions of any given problem and assess existing relationship(s).
  • To show the laws underlying facts and events that cannot be determined by individual observations.
  • To reveal cause and effect relations that otherwise may remain unknown; and
  • To uncover ambiguous trends and behavior in related conditions.
• Uses and applications of statistics:

  Statistical techniques are used and applied extensively

  in all fields of research like in marketing, accounting,

  business, quality control, politics, sports, health

  administration, education, etc.

   Statistical techniques/tools are used extensively in

  quantitative research.
• What are the two areas of Statistics?

  descriptive statistics and inferential statistics
• Descriptive Statistics is the method of organizing,

  summarizing, and providing a description of the

  sample data in an informative way. It includes

  presenting data in percentage, ranks, standard

  units, frequency distribution, measures of location,

  measures of dispersion, among others. The main

  purpose of descriptive statistics is for collecting,

  organizing, summarizing and presenting data.
• Inferential Statistics is used to infer the truth or

  falsity of a hypothesis. It includes making a

  decision, estimate, prediction, or generalization

  about a population based on a sample. The

  ultimate goal is to gain information about the

  sample drawn from a population rather than on the

  population itself. Inferential statistics allow one to

  make accurate inferences about the population

  itself on the basis of the sample data. The main use

  of inferential statistics is for making inferences,

  hypothesis testing, determining relationships, and

  making predicitons.
• What is a collection of possible individuals, objects, elements, or measurements of interest. It is a group of individuals/subjects that comprise the same characteristics

  Population
• What is a portion, or part, of the population of interest. It is a subgroup of the target population which the researcher plans to study for the purpose of making generalization about the entire population.

  Sample
• What letter represents a population?

  N
• What letter represents a sample?

  n
• What are the two branches of inferential statistics?

  non-parametric and parametric statistics
• Non-parametric statistics- The branch of statistics

  wherein the gathered data to be analyzed are not required

  to fit a normal distribution. The name “non-parametric”

  stems from the fact that these statistics are not based on

  assumptions about the parameters of the normal

  distribution. Nonparametric statistics use data that are

  often ordinal; these do not rely on number but rather a

  ranking or order. In other words, the variables being

  analyzed are either nominal or ordinal and when interval

  measurement may not be assumed. For example, a survey

  conveying consumer preferences ranging from
• Nonparametric statistics have gained

  appreciation due to their ease of use. As the need for

  parameters is relieved, the data become more applicable

  to a larger variety of tests. This type of statistics can be

  used without the mean, sample size, standard deviation,

  or the estimation of any other related parameters when

  none of that information is available.
• Parametric statistics - The branch of statistics concerned

  with data measurable on interval or ratio scales and the

  sample size is appropriate, so that arithmetic operations

  are applicable to them, enabling parameters such as the

  mean of the distribution to be defined.
• What is the risk the researcher would run if you use the wrong statistical technique/tool?

  incorrect statistical procedure or may use a less powerful procedure
• Non-parametric statistical procedures are less powerful

  because they use less information in their calculation. For

  example, a parametric correlation uses information about the

  mean and deviation from the mean while a non-parametric

  correlation uses only the ordinal position of pairs of scores.
• What statistics would you use if your measurement scale is nominal or ordinal?

  Non-parametric statistics
• What statistics would you use if your measurement scale is interval or ratio?

  Parametric statistics
• What are the two types of variables?

  Qualitative and Quantitative variable
• The characteristic or variable being studied is nonnumeric?

  Qualitative variable
• The variable can be reported numerically?

  Quantitative variable
• What are the two classifications of quantitative variable?

  Discrete and Continuous variables
• It can only assume certain values and there are usually gaps between values. Typically, these variables result from counting.

  Discrete variable
• It can assume any value within a specific range

  Continuous variables
• What is the singular form for data?

  Datum
• What are the two types of data?

  Ungrouped and grouped data
• This type of data is raw, unorganized information

  Ungrouped data
• This type of data is presented in a frequency distribution table, organised, or processed data

  Grouped data
• Other than "population" what uses the letter "N" as a symbol?

  frequency
• What are the 4 levels of data measurement?

  nominal, ordinal, interval, ratio
• Numerical data are classified according to

  their levels of measurement which dictate the

  appropriate calculations that can be applied in

  processing, summarizing and presenting the

  data at hand.
• Certain test statistic can only be applied

  correctly based on the measure of the data. For

  instance, the chi-square test statistic cannot be

  used to analyze ratio data because it can only

  be used for either nominal or ordinal level

  data. Thus, make sure you will have the full

  grasp of the four levels of data measurement

  after you have read below and have carefully

  listened to the discussion of your course

  professor.
• It is considered the “lowest” level

  of the most primitive data measurement. The

  classification has no natural order.

  Nominal level
• There is no

  measurement involved, only counts. There is

  no particular order to the categories. Data

  categories are mutually exclusive and

  exhaustive, so an object belongs to one and

  only one category. Data categories have no

  logical order.

  Nominal level
• What are the properties of nominal level?

  Mutually exclusive and exhaustive
• data may be arranged in some

  order, but differences between data values

  cannot be determined or are meaningless. One

  category is “higher” or “better” than the next

  one. Data categories are mutually exclusive

  and exhaustive and are ranked according to the

  particular trait they possess.

  Ordinal level