Quantitative Research Methods

• Module One: Concepts and Nature of Statistics
• The Logic of Research

Scientific Research

• The goal of science is understanding the laws of nature.
• This is achieved by examining a specific influence on a specific behavior in a specific situation.
• The process involves generalizing the influence back to the broader behaviors and laws with which the study began.

Examining Relationships

• A relationship exists when changes in the scores of one variable correspond with consistent changes in the scores of another variable.

Strength of a Relationship

• The strength of a relationship is determined by the degree of consistency within the relationship.
• A stronger relationship appears when a group of similar Y values is associated with one X score and a different group of similar Y scores is associated with the next X score.

Factors Affecting Strength

• A weaker relationship may occur due to additional extraneous influences and/or by individual differences.
• Individual differences refer to the fact that no two individuals are identical.

Graphing Relationships

• Relationships are described using a general format: "Scores on the Y variable change as a function of changes in the X variable."
• The X variable is the "given" variable in a study.

Four Sample Graphs

• A graph showing a perfectly consistent association depicts a clear and predictable pattern between variables.
• A relationship can be not perfectly consistent.
• Relationships can present as weak.
• Consistent patterns may not be present.

Applying Descriptive and Inferential Statistics

• Descriptive statistics is the use of procedures for organizing and summarizing data.
• Inferential statistics involves procedures for deciding whether sample data accurately represent a particular relationship in the population.
• Inferential statistics allow making inferences about the scores and relationships found in a population through use of the sample.

Statistics and Parameters

• A statistic describes a characteristic of a sample of scores.
• Similarly, a parameter describes a characteristic of a population of scores.

Understanding Experiments and Correlational Studies

• A study's design is the way that the study is laid out.
• Two major types of designs used include: Experimental, and Nonexperimental (correlational and qualitative studies).

Correlational Studies

• A correlational study measures participants' scores on two variables, then determining if a relationship is present.

Causality

• Causality between the independent and dependent variables is hard to definitively declare.
• It is possible that other hidden variables may be at play.

Basics of Quantitative Research

• Quantitative and qualitative research are foundational in the social and natural sciences.

• Research methods are opposite to one another.

• Quantitative research is the process during which numerical data is collected and analyzed.

• Quantitative research can be used for testing casual relationships within focus groups or testing a sample size of a larger population.

• Statistical analysis helps identify trends and establish statistical averages.

• Statistical analysis helps identify patterns and averages.

• The statistical analysis of numerical data enables researchers to make informed decisions and create statistical predictions.

• Qualitative design in research is a process of non-numerical data collection and analysis.

• Non-numerical data examples include: audio, video, and written text.

• Quantitative research is considered statistically valid since it is mathematically based.

• Making well informed predictions about the future is possible because of quantitative studies.

• Quantitative research can be utilized for many purposes.

• Experimental, correlational, and descriptive research can be include as methods of quantitative studies.

• Experimental and correlational research methods may be used with sample size results for larger, more generalized populations, furthermore this method is commonly used for formal test hypotheses and future predictions.

• Quantitative data collection usually necessitates operational definitions with specific qualities.

• Operational definitions should translate into observable, quantifiable measures and concepts.

• An abstract concept, such as a person's mood, can be defined with quantifiable and observable measures like subject self-ratings of specific feelings.

Experiment

• An experiment controls or manipulates an independent variable to measure its effect on a dependent variable.
• For example, testing intervention combats for procrastination in high school pupils can be carried out via experiment.
• High school students can be split into equitable groups, with one group having an intervention in the experiment.
• Afterward the experimenter compares self rating results from both groups.

Observation

• In quantitative research, observation is the act of identification of behavior within its natural setting.
• Observation in experimental quantitative methods includes monitoring students passively and actively in the class.
• Active and passive behaviors would be recorded in the classroom.

Secondary Data (Data Mining)

• Secondary data uses previously collected data for an alternative purpose.
• Analyzing secondary data can be helpful.
• For example, research data about attitudes can be used again in other research.
• Researchers could gain valuable insights into how attitudes regarding climate change have evolved over a specific period of time from the study.

Survey

• Surveys are used to gather more information by asking questions.

• Versatility and flexibility make surveys incredibly versatile to meet specific research objectives

• Surveys.can may be administered individually or on a larger scale

• Researchers commonly utilize surveys when working with focus groups and sample sizes make generalizations.

• In person, surveys can incorporate technologically advanced tools like online surveys for a wide variety of subjects to gain customer experience feedback to market research initiatives.

• Data gathered may necessitate processing prior to a statistical analysis.

• An important distinction for professionnals is between descriptive and inferential statistics.

  • Information regarding inferential statistics
  • Deceptive statistics

• Hypothesis testing is a prominent advantage that quantitative research offers research professionals.

• The utilization of established and formalized hypothesis testing necessitates consideration of everything from data collection to research variables.

• Quantitative research can have large samples for analysis.

• A distinct advantage of quantitative research is consistent and reliable procedures.

• There are different research methods available, not just quantitative research.

• Quantitative research can lack context if it occurs in unnatural settings.

• Cultural and historical biases which can affect the collection of data and inaccurate results are among the main disadvantages of quantitative research.

• Structural biases can include inappropriate sampling through a choice by research/data design collection method..

• Sampling bias is seen in nonprobability and probability sampling alike ways..

• Sampling bias can be combatted within quantitative research when using careful research design and sampling procedures.

• Sampling bias can be avoided with best strategies and using proper methods.

• A couple of sampling best practices include the definition of a target population and making sure online surveys are as accessible as possible.

• There are many research techniques available such as quantitative and qualitative.

• The main research advantage to consider is the lack of context which comes about in too narrow scopes of settings within quantitative studies

• Emotions/moods of human beings cannot accurately be measure in quantitative studies which can be more successful using an alternative format.

• There are research methods available such as qualitative research.

• Understanding when to use and when not to use quantitative research/ alternative study types is essential for researchers.

• If a researchers needs help in the selection in study type case studies are successful study guide to go over.

Quantitative Research Conclusion

• Quantitative research: used primarily for numerical research only

• Qualitative research: used for non-numerical data.

• Quantitative research can be used correctly is a powerful tool for making predictions and confirming a research theory.

• The way that math and stats is useful for research is in:

  • Quantification: numbers is used for quantity analysis
  • Theoretical modelling: used for stats
  • Weighing the evidence

• Course topics:

  • Measure the different psychological behaviours.
  • Establish a math model
  • Test the models and analysis the data

• What are Statistics?

• The word statistics refers to statistical procedures and the answers that are obtained from those procedures.

• What Do Researchers Do with Statistics?

  • Statistical procedures organize data
  • Statistical procedures Summarized data
  • Statistical procedures Communicate data
  • Statistical procedures Lead to data based conclusions.

• What is Empirical Research?

• Empirical means knowledge obtained through observation of events.

• Empirical research involves measurement.

• The scores obtained in research are the data.

• How Do I Learn Statistics?

• Need to learn the different statistical procedures in order to analyze

• -The key is knowing when each type is needed.

• -You need to learn why use the procedures, and what insights they can get you.

• Statistics should be used: Statistical notation is where the standardized code is for mathematical operations in formulas obtained.

• **Variables and Types of Variables

• **Variables Defined

• -variables can be measured and counted

• -variables are categorized by other variables in a study or depending on role and relationship to variables within the data sets.

• -A construct is that variables that are more abstract than another variables.

• -A variable that one can see is more stable of an environment.

• Obtaining Data

• -Data is created through a variable of measurements that can created two or more values/score.

• ---Some common variables are: age, race, gender, intelligence, personality type and etc.

• Types of Variables

• -Quantitative Variable is measure as an amount that is present.

• -Qualitative Variable is classified by a characteristic

• Type of Variables: --Independent Variable: ------- This is a variable is that is manipulated to observe it’s effect on another variable. --Dependent Variable: --------This can be affected by other variables in value/amount. -- Control Variable: --------This variable is held in place.

• -----Extraneous Variable: --------These are often left out of view (they are not often spoken on)

• Population and Sample

  • Sample and Population: --- The entire group to which a law applies is the population. ---A sample is a small subject of the population to follow/stand for that particular population. ---The subjects of population, in relation to their population should be known as participant’s, subjects , or respondents.

• Drawing Inferences

  • Sample and Population: --- To Estimate or infer from population, use the sample score’s to be expected into find in the population.

• Representativeness* - --- The samples characteristics can be accurately shown into data, but it’s character is needed to be representative of the total view of the population. - unrepresentative Samples

• --------Random sampling is to establish a population, but is not really "fool proof".

• --------Unrepresentative sample can come with problems on misleading information or conclusions.

• --Sampling is the statistical process of selecting a smaller study group for analysis of observations that about the specific population in order to draw statistical inferences.

  • Statistical study: Is generally about behaviours of certain specific population.

• ----the population’s unit of analysis is the study.

• ----the target population.

• ----Sample : is made only by small select observation (example the selection of an employee or firm)

• ---Types of probability techniques

• ---Simple random:

• --------------All selected are given equal chance of being picked.

• ---Systematic:

• --------------select the criteria for the population and elements

• --------------Homogenous : divide the subgroups in non overlapping

• ---Divide the Population:

• -------Random units withing the other random units.

• ---Pair Match:

• --------When researchers plan on comparing two subgroups

• ---Multi choice:

• -------Pick one to pick one that has multI choice from each technique. Non-Probability Sampling

• ------This is a sampling type , where the population does not have anything or any change of chance /selection rate to be accurately determinate.

• ---------------Units can only be selected based on certain conditions that are not made to become random/equal are quota,

• ----------Sample is not the estimation because of these errors..

• • Therefore information cannot be generalized out into the population.

• -Types of techniques of nonprobability's.

• -1) convenience

• -2) quota

• -3) expert.

• -4) snowball.

Levels of Measurement

• There are four types of scales

• Nominal is to not indicate instead it just there to tell data.

• ordinal to rank in measurement as their are equal amounts available.

• an internal is to measure an actual amount that is to separate the amount. This doesn't have "true "0.

• And ratio to show actual value that have equal unit to separate

  • So "0" is the the data doesn't have any amount.

• ------Characteristic from variable

• -Two important aspects is. -- the Measurement type --weather it constant/disrate.

• -constant to mean is fractional amounts and decimals make much since --But in discrete it just measure the whole

• dichotomous Variable--A variable is a dichotomous variable that just has two options is the value.

• Math symbols X--is to stand for a data set. Add, subtract. divide. multiply

• -parentheses should follow rules to keep data straight. (1) round, multiply. all said data the equation will show.

• -Transformations are math procedures to data for certain data points.

• -Proportions is --A total that adds between 0-- 1 - a transformation to show proportions in data and can have an effect on the data it comes with.

• Precents ----A data shows amount times "100"

• -Graph

  • X axis : horizontal line at base
  • Y axis : is at base.

Explore quantitative research methods. Questions cover data collection, experimental design, and statistical analysis. Test your knowledge of numerical data and research design.